Proceedings of the Annual Meeting of the Cognitive Science Society

Cognitive capacities such as learning, reasoning, anddecision-making are often studied in tasks where a single par-ticipant acts in isolation. Yet humans don’t learn, reason, andmake decisions in a vacuum. Human cognition is distinc-tively social: Much of what we do influences—and is influ-enced by—other people.The goal of this workshop is to bring together diverse per-spectives on the interplay between human cognition and thedynamic, social environments we inhabit. The workshop isorganized around three key themes. Theme 1 lays out thecognitive tools that equip individuals to thrive in social en-vironments, including specialized mechanisms for teachingand learning from others. Theme 2 examines how the socialenvironment is itself shaped by the dynamic interactions be-tween multiple individuals, producing emergent behaviors atthe level of the collective. Finally, Theme 3 explores howhuman cognition responds to the demands of particular so-cial environments, including how cultural variability in socialcognition might emerge across development.Collectively, the research showcased in this workshopenriches this year’s conversation on “How to Develop aMind: Learning in Humans, Animals, and Machines”by highlighting the social and cultural context of learn-ing and development. In addition, our speakers representa broad cross-section of the conference, spanning multi-ple disciplines (computer science, anthropology, psychol-ogy), perspectives (computational, ecological, developmen-tal), and career stages (from research assistants to full pro-fessors). Below, we describe each theme and presentercontributions in detail. To take part in the workshop,visit cognitioncollectivesandculture.github.io forthe current schedule.

The proliferation of web-connected devices has presentedsignificant opportunities and challenges to cognitive sci-ence — opportunities in that cognitive scientists can collectdata relevant to human cognition orders of magnitude fasterthan before, addressing questions that were otherwise impos-sible to address; and challenges in that cognitive scientistsrequire new infrastructure to collect these data and new meth-ods to analyze them once collected.This workshop brings together cognitive scientists who areat the forefront of these opportunities and challenges in scal-ing cognitive science, along with cognitive scientists whowould like to be, to engage in a day of interactive exchangeand development of ideas related to scaling cognitive science.The workshop is a full day. Each presentation addresses adifferent opportunity or challenge. One set of presentationshighlights opportunities. A second set highlights challengesof statistical analysis and data collection. In a break-out ses-sion, attendees address these opportunities and challengeshead on.

Research in decision-making has recently begun to empha-size predictive accuracy as the dominant principle for design-ing and evaluating choice models. This emphasis has led tothe development of increasingly more precise models of hu-mans’ risk preferences, as measured in certain experimentalparadigms built upon certainty equivalence testing. In thispaper, we argue that the level of precision attained by recentchoice models is unexpected, because human preferences areirreducibly noisy. We support this argument by conducting ex-periments to measure intra-observer consistency in choice be-havior in two common risk preference paradigms: decisionsfrom description and experience. We find that while currentchoice models of decisions from experience align fairly wellwith the upper limits of choice consistency seen in our experi-mental data, choice models for decisions from description aresignificantly more consistent with humans’ choices than thehumans themselves are consistent with their own choices. Wediscuss some theoretical and practical implications of our re-sults.

Evidence from a variety of learning tasks suggests that cuesthat are more predictive of an outcome attract greater attentionand are learned about more effectively in subsequent tasks. Wetested whether this learned predictiveness effect is due to theobjective strength of the cue-outcome association (cue-outcome correlation), or the degree to which the cue isinformative for making the correct choice on each trial (choicerelevance), by manipulating the possible outcome choicesavailable on each trial. Experiment 1 compared two sets of cuesthat were equally (and imperfectly) correlated with outcomesand showed learning biases in favor of the set of cues that hadinitially been more relevant for choices made on each trial.Experiment 2 used a more conventional learned predictivenessdesign in which the cue-outcome correlation was stronger forone set of cues (perfect predictors) than the other set (imperfectpredictors). However, here we manipulated whether or not theimperfect predictors could be used to make a correct choice,and thus whether the imperfect predictors possessed choicerelevance that was equal to or less than the perfect predictors.In this case, we found no evidence that the relevancemanipulation made any difference; learning biases towards theperfect predictor were evident regardless. The results suggestthat both cue-outcome correlation and choice relevance canlead to changes in associability and learning biases; both wereindividually sufficient but neither were necessary.

We investigate the relationship between exploratory learningand confidence scale judgments in understanding andimproving children’s early recognition of uncertainty. Four-and five-year-olds were presented with stimuli that varied intheir amount of occlusion. We assessed children’s ability todistinguish between these levels of uncertainty using twotypes of measures. Experiment 1 used a traditional 3-pointconfidence scale to examine explicit uncertainty judgments.Experiment 2 examined exploration preference as an implicitmeasure of uncertainty using the same stimuli. We comparedchildren’s performance on these two tasks before and aftertheir experience of disconfirming evidence, to assess theimpact of surprising events on the recognition of uncertainty.Results indicate that children intuitively recognize gaps intheir knowledge and express this in their exploratory behaviorbefore they are able to spontaneously produce accurateconfidence judgments. We also find that this implicitrecognition of uncertainty may be leveraged to support andimprove explicit judgments, even without extensive training.

Decision making under risk is often studied as a preferential choice governed by stable individual personality characteristics, but risky choice can also be viewed as a dynamic problem of resource accumulation to survive. When decision makers aim to reach a particular goal in limited time, such as “earn at least $100 in five choices,” risky choice becomes a non-trivial planning problem. This problem has an optimal solution that can differ from immediate expected-value maximization. We studied the optimality of risky choices under such minimum goal requirements experimentally and find that the observed choices under goals approximate the optimal solution. However, because the optimal model is very complex, we examine if simpler models can predict people’s choices better. We test an extended version of prospect theory, assuming a dynamic reference point that depends on the distance to the goal. This “dynamic prospect theory” was better than the alternative model in describing people’s decisions (i.e., for 63% of the participants, it was the best model). Our findings show that humans can excel in a highly complex, dynamic, risky choice problem and that a dynamic version of prospect theory provides one possible explanation for how people decide under risk when long-term goals matter.

PRIMs theory describes a computational foundation for un-derstanding task-general human learning and transfer usingrule-based cognitive architectures. Integration with ACT-Rhas yielded Actransfer, a model that replicates human learn-ing and transfer across many tasks. However, this model re-quires task-specific latency scaling parameters from ACT-Rto model different tasks, implying that there is missing com-putation in the theory. Neuroscience literature has separatelydefined the “task set” as the neural encoding that configuresstimulus-response rule behavior in working memory. The pro-cess of switching between different task sets is often used toexplain human latency costs. This paper introduces an alter-nate instantiation of PRIMs theory that enacts task set process-ing to account for the missing computation via a novel memorystructure called a procedure context. Human tasks of varyingcomplexity are modeled across two experiments. Procedurecontexts model human latencies and interference effects in alltasks by integrating latency, decision making, task representa-tion, and learning as aspects of a single unified process. Thisapproach offers promise for future modeling within cognitivescience by uniting theories from neuroscience and cognitivearchitectures.

This study examined the separate and combined effects ofexercise and cognitive training on children’s executivefunction (EF) and associated neural substrates. Sixty-twochildren were recruited and randomly assigned to an Exergame(exercise + cognitive activity), Exercise (physical activity),Sedentary (cognitive activity), or Control (no-play) Condition.The training consisted of 20 min sessions 2x/week and wascompleted by 49 children 4- to 5-years-old. Resting-stateprefrontal cortex (PFC) connectivity utilizing functional near-infrared spectroscopy, behavioral assessments of EF, andteacher ratings of EF were assessed pre- and posttest.Exergame training significantly improved performance ontransfer EF assessments compared to the other conditions andincreased PFC connectivity. The changes in PFC connectivitywere positively associated with EF improvement. Thesefindings suggest that the combination of cognitive and exercisetraining modulates the effects on EF and elucidates the neuralmechanisms underlying the changes in EF induced fromexergame play.

Recent neuroimaging evidence suggests that scene processing depends on dissociable systems for visually-guided navi-gation (including the occipital place area, OPA) and scene categorization (including the parahippocampal place area). Ifthese systems are truly dissociable, then it should be possible to find cases in which one system is impaired, while theother is spared. Further, if dissociable, then these systems may develop independently. Here we tested visually-guidednavigation and scene categorization abilities in 36 adults with Williams syndrome (WS) a developmental disorder in-volving cortical thinning in and around the OPA as well as 82 typically developing 4-8 year old children. We foundthat i) WS adults are impaired in visually-guided navigation, but not scene categorization, relative to mental-age matchedchildren; and ii) visually-guided navigation matures later in typical development than scene categorization. These findingsprovide neuropsychological and developmental evidence for dissociable scene processing systems for recognizing placesand navigating through them.

What role does the motor system play in language understanding? Here we show that effector-specific motor interferencecan change how people interpret language about actions. An action like voting can be understood in terms of its concretedetails (writing marks on a ballot) or its abstract significance (influencing an election). If neural circuits for performingmotor actions enable people to mentally represent an actions concrete details, then occupying these circuits with a sec-ondary motor task should make the actions details harder to represent. Consistent with this hypothesis, in two experiments(N=180), tapping a complex rhythm with either the hands or the feet increased the proportion of abstract interpretations ofphrases describing actions with the same effector. Thus, meaningless motor activity causes qualitative changes in languagecomprehension: Performing different actions can lead to different understandings of the same words and phrases.

Tetris is a fast-paced puzzle solving game that requires play-ers to rapidly maneuver falling blocks to clear rows and scorepoints. Skilled Tetris players learn to execute moves in thegame very quickly to keep up with the increasing time pres-sure. But world champion Tetris players employ more complexstrategies that save precious milliseconds that enable them toreach even higher levels of play. Such strategies show mas-tery of the game’s event structure, but also come with a startupcost— a “cognitive speed bump”— wherein they must mo-mentarily decide whether to rotate a block left or right, evenfor scenarios where the distinction is not meaningful for per-formance. We present data showing both the world champions’superior overall action times, but also a preliminary “speedbump” that is consistent both within and between world cham-pion players. Potential underlying memory structures are ex-plored, and implications are discussed for both the Soft Con-straints Hypothesis and the relationship between Hick’s Lawand expertise.

A large body of research has shown that engaging in explanation improves learning across a range of tasks. The act of explaining has been proposed to draw attention and cognitive resources toward evidence that will support a good explanation—information that is broad, abstract, and consistent with prior knowledge—which in turn aids discovery and generalization. However, it remains unclear whether explanation acts on the learning process via improved hypothesis generation, increasing the probability that the correct hypothesis is considered in the first place, or hypothesis evaluation, the appraisal of the correct hypothesis in light of evidence. In the present study, we address this question by separating the hypothesis generation and evaluation processes in a novel category learning task and quantifying the effect of explanation on each process independently. We find that explanation supports the generation of broad and abstract hypotheses but has less effect on the evaluation of hypotheses.

People spontaneously make connections between superficially distinct domains through relational similarity, but this spontaneous transfer has yet to be demonstrated across distinct classification tasks. A related issue is that the acquisition of a category may affect recognition memory for category- consistent items. Participants in the Category Learning condition completed an initial classification task. The Category Learning and Baseline conditions each received category- consistent items to study followed by a recognition test. Both groups completed a final classification task in a novel domain abiding by the same underlying category structures as the initial classification task. The Category Learning group showed 1) increased false alarms during the recognition test and 2) higher accuracy in the final classification task (when told the classification phases were unrelated). This suggests that classification learning led to a schematization of the category-defining concept (evidenced by increased false alarms), which supported spontaneous transfer of relational concepts across distinct classification tasks.

Our ability to plan and build a wide array of physicalstructures, from sand castles to skyscrapers, is a definingfeature of modern human intelligence. What cognitive toolsenable us to create such complex and varied structures?Here we investigate how practice “reverse-engineering” a setof physical structures impacts the procedures that peoplesubsequently use to build those structures, as well as how wellthey build them over time. Participants (N=105) viewed 2Dsilhouettes of 8 unique block towers in a virtual environmentsimulating rigid-body physics, and aimed to reconstruct eachone in less than 60 seconds. We found that people learnto build each tower more accurately and quickly acrossrepeated attempts, and that these gains reflect both group-levelconvergence upon a smaller set of viable policies, as wellas error-dependent updating of each individual’s strategies.Taken together, our study provides novel insight into howhumans learn from prior experience to discover better solutionsto physical reasoning problems over time.

Prior knowledge of relational structure allows people toquickly make sense of and respond to new experiences. Whenawareness of such structure is not necessary to support learn-ing, however, it is unclear when and why individuals “spon-taneously discover” an underlying relational schema. Thepresent study examines the determinants of such discovery indiscrimination-based transitive inference (TI), whereby peo-ple learn about a hierarchy of interrelated premises and aretested on their ability to draw inferences that bridge studiedassociations. Experiencing “chained” sequences of overlap-ping premises during training was predicted to facilitate thediscovery of relational structure. Among individuals withoutprior knowledge of the hierarchy, chaining improved relationallearning and was most likely to result in explicit awareness ofthe underlying relations between items. These findings addto growing evidence that the temporal dynamics of training,including successive presentation of overlapping associations,are key to understanding spontaneous relational discovery dur-ing learning.

The ability to chain together sequences of information and action is pivotal to everyday acquisition of skills. Despite extensive research of sequence learning, little focus has been given to individual performance in standard tasks measuring this capability. As a result, little is known regarding what knowledge participants gain during such tasks. In the current work, an individual- and item-based analysis is performed of eye movements that occur during a spatial sequence learning task and reflect anticipation of upcoming target locations. We show that the knowledge participants acquire during the task is tightly linked to a-priori response biases they bring into the experiment. Results suggest that a-priori biases may be a sizeable influence on performance in learning experiments, that tends to be overlooked. Implications for designing and reading studies of sequence learning are discussed.

Standard methods in deep learning fail to capture composi-tional or systematic structure in their training data, as shownby their inability to generalize outside of the training distribu-tion. However, human learners readily generalize in this way,e.g. by applying known grammatical rules to novel words. Theinductive biases that might underlie this powerful cognitive ca-pacity remain unclear. Inspired by work in cognitive sciencesuggesting a functional distinction between systems for syn-tactic and semantic processing, we implement a modificationto an existing deep learning architecture, imposing an analo-gous separation. The resulting architecture substantially out-performs standard recurrent networks on the SCAN dataset, acompositional generalization task, without any additional su-pervision. Our work suggests that separating syntactic fromsemantic learning may be a useful heuristic for capturing com-positional structure, and highlights the potential of using cog-nitive principles to inform inductive biases in deep learning.

During language comprehension, what guides how wedistinguish between objective facts and subjective opinions?Our three experiments investigate whether people’s ability todetect subjective content – which we indicated by means ofopinion-conveying adjectives (e.g. amazing, frustrating) – ismodulated by the adjective’s structural position. Our resultsindicate that altering the linguistic structure of a sentenceinfluences our perception of how subjective it is: Even whenthe basic information being conveyed is held constant,packaging this information in different ways elicits differentlevels of perceived subjectivity. When a subjective adjectiveoccurs in a structural position associated with newinformation, the text is rated as more subjective compared to atext that conveys the same basic information but has the sameadjective in a position associated with already-knowninformation. This suggests that the difference between factand opinion, or at least our ability to recognize opinion-basedinformation, can be distorted by linguistic packaging.

Scalar inferences are commonly assumed to involve both lit-eral semantic interpretation and social cognitive reasoning.However, the precise way to characterize listeners’ represen-tation of context - including the space of possible utterance al-ternatives as well as the space of possible conventional mean-ings associated with linguistic forms - is a matter of ongoingdebate. We report a partial replication of a scalar inferencepriming study by Rees and Bott (2018), introducing a novelbaseline condition against which to compare behavior acrossdifferent priming treatments. We also investigate the effectof raising participants’ awareness of communicatively strongeralternatives that explicitly encode an exhaustive meaning (e.g.some but not all with respect to some). Our results suggestthat exhaustive alternatives (which are ‘symmetric’ to canoni-cal alternatives) can modulate the availability and strength ofscalar inferences, and that semantic uncertainty is an indepen-dent channel through which scalar inferences are modulated.We discuss implications for theories of pragmatic competence.

Many hallmarks of human intelligence including language, reasoning, and planning require us to draw upon knowledge about the world in which concepts, denoted by words, are organized by meaningful, semantic links between them (e.g., juicy-apple-pear). The goal of the present research was to investigate how these organized semantic networks may emerge in development from simple but powerful mechanisms sensitive to statistical co- occurrence regularities of word use in language. Specifically, we tested whether a mechanistic account of how co-occurrence regularities shape semantic development accurately predicts how semantic organization changes with development. Using a sensitive, gaze-based measure of the semantic links organizing knowledge in children and adults, we observed that developmental changes in semantic organization were consistent with a key role for statistical co-occurrence regularities.

With development, we acquire rich body of knowledge aboutthe world in which concepts denoted by words (e.g., juicy,apple, and pear) are connected by meaningful, semantic links(e.g., apples and pears are similar, and can both be juicy). Onepotentially powerful driver of this development is sensitivity toregularities with which words co-occur in language.Specifically, language is rich regularities that can support: (1)Associative semantic links between words that directly co-occur together (e.g., juicy-apple), and (2) Taxonomic semanticlinks between words similar in meaning that share patterns ofdirect co-occurrence (e.g., apple and pear both co-occur withjuicy). Here, we investigated the development of abilities toform semantic links from these regularities. Results revealedthat both children and adults formed direct co-occurrence-based links, whereas only adults formed shared co-occurrencebased links. We discuss how these results may provide keyinsight into how semantic organization develops.

Temporal connectives are function words that relate events intime. Despite their ubiquity and utility, children acquire themeanings of temporal connective words late in development.Experimental work has uncovered patterns in the acquisitionof temporal connectives that clarify the learning challenge thatthese words pose to children. In particular, developmentalstudies have identified differing acquisition trajectories acrossconnective types, asymmetries in learning within pairs of re-lated connectives, and monotonic increases in comprehensionwith age. Expanding on prior theoretical accounts, we formal-ize temporal connective acquisition in a computational wordlearning framework. We demonstrate that each of the empir-ically determined acquisition patterns emerges in the learningbehavior of our computational model. Finally, we discuss ourfindings in relation to earlier theories and to general learnabil-ity concerns in language acquisition.

This paper presents simulations of eye movements during read-ing, lexical decision, and naming using Über-Reader, a newcomputational model that aims to provide a complete accountof the perceptual, cognitive, and motor processes involved inreading. The present simulations focused on Über-Reader’sword-identification module—an implementation of the Multi-ple-Trace Memory model (Ans et al., 1998) based on the theo-retical assumptions of the MINERVA 2 model of episodicmemory (Hintzman, 1984)—with a vocabulary comprising thefull corpus of the English Lexicon Project (Balota et al., 2007).The model’s lexicon was probed with words and one-letter-dif-ferent non-words from the Schilling et al. (1998) corpus, andoutputs of the model were scored to evaluate performanceagainst the empirical data. The outcomes of these simulationswill inform further development of Über-Reader by providingthe foundation for our ultimate goal of simulating reading, inits entirety.

What determines degree of competition among phonologicallysimilar words? One proposal is that proportion of overlappredicts competition independently of word length. We arguethat proportion of overlap may provide descriptive adequacy,but does not provide an explanation. We show that TRACEcorrectly predicts patterns previously attributed to proportionof overlap. In additional simulations, with independentmanipulations of word length and proportion of overlap,proportion of overlap fails to predict the full pattern of results.We discuss how competition dynamics in TRACE modulatecompetition as word length and proportion of overlap change.These results have implications for theories of human spokenword recognition, and will motivate experiments to test thesenew TRACE predictions.

The debate about how attention is allocated during readinghas been framed in as: Either attention is allocated in a strictlyserial manner, to support the identification of one word at atime, or it is allocated as a gradient, to support the concurrentprocessing of multiple words. The first part of this article re-views reading models to examine the feasibility of both posi-tions. Although word-identification and sentence-processingmodels assume that words are identified serially to incremen-tally build larger units of representation, discourse-processingmodel allow several propositions to be co-active in workingmemory. The remainder of this article then describes an in-stance-based model of word identification, Über-Reader, andsimulations comparing the identification of single words andword pairs. These simulations indicate that, although wordpairs can be identified, accurate identification is restricted toshort high-frequency words due to the computational de-mands of both memory retrieval and limited visual acuity.

Reference resolution is one of the core components of language understanding. In spite of its centrality, psychological evidence has shown that the reference resolution process is prone to errors and egocentric bias. In this work, we propose an extension to Analogical Reference Resolution, a computational model based on analogical retrieval, which accounts for such errors. We test the extended model on a study by Epley et al. (2004) and replicate human patterns of bias and correction.

A fundamental issue in cognitive science concerns the mentalprocesses that underlie the formation and retrieval of conceptsin the short-term and long-term memory (STM and LTMrespectively). This study advances Chunking Theory and itscomputational embodiment CHREST to propose a singlemodel that accounts for significant aspects of conceptformation in the domains of literature and music. The proposedmodel inherits CHREST’s architecture with its integratedSTM/LTM stores, while also adding a moving attentionwindow and an “LTM chunk activation” mechanism. Theseadditions address the overly destructive nature of primacyeffect in discrimination network based architectures andexpand Chunking Theory to account for learning, retrieval andcategorisation of complex sequential symbolic patterns – likereal-life text and written music scores. The model was trainedthrough exposure to labelled stimuli and learned to categoriseclassical poets/writers and composers. The model categorisedpreviously unseen literature pieces by Homer, Chaucer,Shakespeare, Walter Scott, Dickens and Joyce, as well asunseen sheet music scores by Bach, Mozart, Beethoven andChopin. These findings offer further support to mechanismsproposed by Chunking Theory and expand it into thepsychology of music.

Recognition memory studies have reliably demonstrated the word frequency effect (WFE), where low-frequency words are more accurately recognized than high-frequency words. The context noise account of WFE argues that pre-experimental exposure to stimuli generates interference that compromises high-frequency words more than low-frequency words. Because the representations of the contexts associated with more recent exposures are assumed to overlap more with the representation of the study context, stimuli that have been seen more recently are thought to generate the most interference. We asked participants to log their daily email for two months. Based on the participant’s email corpus, we constructed an individualized study-test recognition memory task to investigate the effect of recency. Results show that recency has a graded effect on recognition memory that extends for at least two months providing support for the context noise account.

Humans are highly variable in their ability to learn and executecomplex tasks; however, there are conflicting theories on skillacquisition. This study compared two different explanationsfor how association learning interacts with other cognitiveprocesses: a) reinforcement learning and working memory areseparate, competing processes operating simultaneously onassociation learning; and, b) associations are proceduralizedinto production rules and reinforcement learning acts on thoserules. Participants completed a simple association learning taskfollowed by a delayed test under two conditions designed tocontrast these theories. The results are consistent with aproceduralization account in which reinforcement learning andworking memory are not competitive interfering systems, butthere remain important questions about how these two accountsmay be best integrated.

A primary challenge for alibi generation research is establishing the ground truth of the real world events of interest.We used a smartphone app to record data on participants for a month prior to a memory test. The app captured theiraccelerometry continuously and their GPS location and sound environment every ten minutes. After a week retentioninterval, we asked participants to identify where they were at a given time from among four alternatives. Participants wereincorrect 36% of the time. Furthermore, our forced choice procedure allowed us to conduct a conditional logit analysisto assess the relative importance of different aspects of the events to the decision process. We found strong evidence thatparticipants confuse days across weeks. In addition, people often confused weeks in general and also hours across days.Similarity of location induced more errors than similarity of sound environments or movement types.

Previous work has revealed that the ability to strategically en-code high-value information may improve gradually over de-velopment as cognitive control mechanisms mature. However,studies of value-directed memory have relied on explicit cuesof information value, which are rarely present in real-worldcontexts. Here, using a novel fMRI paradigm, we examinedwhether individuals across a wide age range (N = 90; ages 8– 25 years) could learn the value of information from expe-rience and use learned value signals to strategically modulatememory. We found that memory prioritization for high-valueinformation improved across development, and was supportedby increased engagement of the caudate and prefrontal cortexduring both encoding and retrieval of high-value information.Our results suggest that across development, the dynamic ad-justment of memory based on the statistics of the environmentis supported by a wide network of brain regions involved inboth the recognition and use of information value.

Two studies explore gender gaps that favor girls in low-stakeslearning contexts yet are not evident in high-stakesachievement measures. Study 1 (n = 386) combined controldata across multiple experiments testing student’s learningfrom a challenging proportional reasoning lesson to exploreconsistent gender gaps in favor of girls. This learning gap couldnot be explained by the baseline mathematics, affective,motivational, or Executive Function individual differences wemeasured. In Study 2 (n =178), we experimentally manipulatedpressure, raising the stakes by telling some students that theirperformance would determine whether or not their entire classreceived an incentive. Gender gaps in favor of girls remainedin the absence of pressure, but when external pressure wasimposed before or after learning, the female advantagedisappeared. These data suggest managing feelings of pressurein learning or testing contexts may be an important step inultimately increasing female representation in math-intensivefields.

Love is a central theme in modern music, but do women andmen differ in their expressions of love? Results from empiri-cal studies on gender differences in love attitudes have evolvedfrom showing consistent differences to more similarities overtime and witnessed gender convergence in relationship expec-tations, housework responsibilities, and sexual attitudes. Inde-pendently, pop culture studies have shown how music can beused as a contextual artifact whose lyrics can reflect a culture’schanging psychological processes and ideologies. We combinethese two research areas to explore whether the gender con-vergence reported in psychological studies is mirrored in lovesongs. Using a corpus of lyrics and song metadata from 1960to 2009, we present a computational analysis of the lexical dis-tribution of lyrics across genre, gender and time. We show thatlove songs between vocalists who are men vs. women havebecome significantly more similar in their lyrical expressionsof love.

The current study tests subjects’ vocal alignment toward femaleand male text-to-speech (TTS) voices presented via threesystems: Amazon Echo, Nao, and Furhat. These systems vary intheir physical form, ranging from a cylindrical speaker (Echo), toa small robot (Nao), to a human-like robot bust (Furhat). We testwhether this cline of personification (cylinder < mini robot

We don’t learn about each person we meet from scratch: Ourknowledge of social groups (e.g., cognitive scientists) shapesour expectations about new individuals (e.g., the reader). Herewe explore how 4- and 5-year-old children and adults use min-imal statistical evidence about social groups to support induc-tive inferences about individuals. Overall, we find that bothchildren and adults readily infer the preferences and groupmembership of new individuals when they have appropriateevidence to support these inferences. However, our resultsalso suggest that children and adults interpret this informa-tion in different ways. Adults’ responses align closely witha Bayesian model that assumes that each group’s preferencesare independent of one another. By contrast, we find prelimi-nary evidence that children’s inferences about the preferencesof new group members are sensitive to the composition (Exper-iment 1) and size (Experiment 2) of the opposing group. Ourwork provides insights into how people form structured, gen-eralizable representations of social groups from sparse data.

Proportional reasoning is a ubiquitous part of the humanexperience. We engage in proportional reasoning to meetboth informal and specialized goals across a range ofdomains, such as medicine (e.g., disease rates, drug dosages),finance and commerce (e.g., interest rates, discounts),cooking and baking (e.g., scaling ingredient amounts), andmany others. Given this variation in usage, it may not besurprising that proportional reasoning does not have asingular definition or interpretation, but instead is a complextopic with many interconnected concepts. The central goal ofthis symposium is to shed light on this complexity bydiscussing diverse perspectives of proportional reasoning.

Speakers often overspecify by using colour adjectives redun-dantly in referential communication. We investigated whetherthis tendency to overspecify is influenced by a partner’s lin-guistic behaviour, and whether the effect is enhanced by lex-ical repetition and semantic relatedness. We used a director-matcher task in which speakers interacted with either a consis-tently overspecific or a consistently optimal partner. Our re-sults show that partner behaviour influences overspecification.An analysis over time indicates that speakers tended to over-specify at the outset, but reduced this behaviour over interac-tion with an optimal partner much more than with an overspe-cific partner. This may suggest that overspecification (at leastwith colour modifiers) is the “default” behaviour, with speak-ers adapting to optimality in a partner’s linguistic behaviour.

A key property of linguistic conventions is that they hold overan entire community of speakers, allowing us to communicateefficiently even with people we have never met before. Atthe same time, much of our language use is partner-specific:we know that words may be understood differently by differ-ent people based on our shared history. This poses a chal-lenge for accounts of convention formation. Exactly how doagents make the inferential leap to community-wide expecta-tions while maintaining partner-specific knowledge? We pro-pose a hierarchical Bayesian model to explain how speakersand listeners solve this inductive problem. To evaluate ourmodel’s predictions, we conducted an experiment where par-ticipants played an extended natural-language communicationgame with different partners in a small community. We ex-amine several measures of generalization and find key signa-tures of both partner-specificity and community convergencethat distinguish our model from alternatives. These resultssuggest that partner-specificity is not only compatible with theformation of community-wide conventions, but may facilitateit when coupled with a powerful inductive mechanism.

In successful communication, speakers tailor their language tothe context and listeners make inferences about the speaker’sknowledge. Several current accounts propose that both speak-ers and listeners accomplish this by rational analysis of thestatistics in the environment, including their partner. Here weexamine perspective-taking behaviour in a dyadic conversationtask, where the same individuals act in the role of both speakerand listener. We model perspective-taking in both productionand comprehension, taking into account the dyadic situation.Our findings suggest that conversational partners weight theirown perspective more than the partner’s when speaking, andthe partner’s perspective more than their own when listening.We also find that in both production and comprehension, con-versational partners change the weighting of perspectives overtime, moving towards relying more on the partner’s perspec-tive at the expense of their own perspective. Surprisingly, wefind little evidence that listeners or speakers adapt to the id-iosyncratic statistics of their partner’s linguistic behaviour.

Contrastive inferences, whereby a listener pragmatically infersa speaker’s referential intention of a partial referring expres-sion like the yellow by reasoning about other objects in thecontext, are notoriously unstable. We report a production-centric model of interpretation couched within the RationalSpeech Act framework. Adjective production probabilities alistener expects for objects in a context drive the size of con-trastive inferences: the greater the asymmetry in expectationfor a speaker to use a pre-nominal adjective for the target ratherthan for competitors, the greater the listener’s resulting targetpreference. Modifier production probabilities were collected(Exp. 1) and used to make predictions about comprehensionin an incremental decision task (Exp. 2). The model’s inter-pretation predictions are supported by the data. This accounthas the potential to explain the fluctuating appearance of con-trastive inferences and shifts the explanatory focus away fromcontrastive inference towards online interpretation of referringexpressions more broadly.

Learners faced with competing statements that each have support from multiple sources must decide whom to trust. Lacking firsthand knowledge, they frequently trust the majority. Yet, majorities can be misleading if most members are relying on hearsay from just a few members with firsthand knowledge. Thus, past work has emphasized the importance of informational independence when deciding whom to trust, showing that children and adults do consider informational independence important in certain contexts. However, because informational independence precludes group deliberation, we ask whether children make the reverse inference and devalue informational independence when facing a problem that could benefit from deliberation. In two studies, children and adults ignore informational independence when attempting to answer abstract reasoning questions. However, for a question type for which deliberative reasoning would be of doubtful benefit, children and adults seek advice from multiple independent sources rather than a deliberative group.

People tend to place value on information even when it doesnot affect the outcome of a decision. Two competingaccounts offer explanations for such non-instrumentalinformation seeking. One account foregrounds the role ofanticipation and the other focusses on uncertainty aversion.Both accounts make similar predictions for short cue-outcome delays and when outcomes are positively valenced,but they differ in their explanation of information preferenceat long delays with negative outcomes. We present a seriesof experiments involving both primary and secondaryreinforcers that pit these accounts against each other. Theresults indicate a consistent preference for non-instrumentalinformation even at long cue-outcome delays and noevidence for information avoidance with negative outcomes.This pattern appears to provide more support for theuncertainty-aversion account than one based on anticipation.

In the present paper, we introduce a novel computationalapproach for uncovering mental representations underlyinghealthiness judgments for food items. Using semantic vec-tor representations derived from large-scale natural languagedata, we quantify the complex representations that people holdabout foods, and use these representations to predict how bothlay decision makers and experts (trained dietitians) judge thehealthiness of food items. We also successfully predict theimpact of behavioral interventions (e.g. the provision of nutri-ent content information or “traffic-light labels”) on healthinessjudgments for food items. Our models are highly general, andare capable of making predictions for nearly any food item.Finally, these models outperform competing models based onfactual nutritional content, suggesting that health judgmentsdepend more on complex (semantic) knowledge representa-tions than on quantified nutritional information. The results inthis paper illustrate how methods from cognitive science andcomputational linguistics can be combined with existing theo-ries in psychology, to better predict, understand, and influencehealth behavior.

Learning what is relevant for reward is a ubiquitous and crucialtask in daily life, where stochastic reward outcomes can de-pend on an unknown number of task dimensions. We designeda paradigm tailored to study such complex scenarios. In the ex-periment, participants configured three-dimensional stimuli byselecting features for each dimension and received probabilis-tic feedback. Participants selected more rewarding featuresover time, demonstrating learning. To investigate the learningprocess, we tested two learning strategies, feature-based rein-forcement learning and serial hypothesis testing, and found ev-idence for both. The extent to which each strategy was engageddepended on the instructed task complexity: when instructedthat there were fewer relevant dimensions (and therefore fewerreward-generating rules were possible) people tended to seri-ally test hypotheses, whereas they relied more on learning fea-ture values when more dimensions were relevant. To explainthe behavioral dependency on task complexity and instruc-tions, we tested variants of the value-based serial hypothesistesting model. We found evidence that participants constructedtheir hypothesis space based on the instructed task condition,but they failed to use all the information provided (e.g. rewardprobabilities). Our current best model can qualitatively capturethe difference in choice behavior and performance across taskconditions.

Adolescents are going through a period of rapid growth in cognitive resources, both in crystallized, or knowledge-based, cognition and fluid cognition, or the ability to think and reason flexibly. Past literature reveals an ongoing debate as to whether, or in what way, different activities during childhood relate to these abilities. The current study leveraged the Adolescent Brain Cognitive Development baseline dataset to explore the interplay between nine- and ten-year-olds’ extracurricular activities, screen time, and the different components of cognition. Results indicate that adolescents’ activities explain more variance in crystallized than fluid cognition. Further, participation in artistic activities is associated with increased fluid and crystallized cognition, though sports is positively associated with fluid but negatively associated with crystallized cognition. Different types of screen time, though notably not video game playing, may be negatively associated with cognition. Screen time explains more variance in fluid cognition than extracurricular activities do, whereas the opposite is true of crystallized cognition. This correlational study suggests potential avenues for further work to disentangle the causal links underlying the relationships between experiences and cognition. Do such activities change adolescents’ cognitive skill, or do children self-select to participate in certain types of activities that complement their existing skills?

Curiosity motivates exploration and is beneficial for learning,but curiosity is not always experienced when facing theunknown. In the present research, we address this selectivity:what causes curiosity to be experienced under somecircumstances but not others? Using a Bayesian reinforcementlearning model, we disentangle four possible influences oncuriosity that have typically been confounded in previousresearch: surprise, local uncertainty/expected informationgain, global uncertainty, and global expected informationgain. In two experiments, we find that backward-lookinginfluences (concerning beliefs based on prior experience) andforward-looking influences (concerning expectations aboutfuture learning) independently predict reported curiosity, andthat forward-looking influences explain the most variance.These findings begin to disentangle the complexenvironmental features that drive curiosity.

Questions about the origins of life and the universe seem to call out for explanation, with science and religion offering candidate answers. These answers clearly differ in content, but do they also differ in psychological function? In Study 1 (N=501) participants on Amazon Mechanical Turk rated scientific and religious answers to existential questions on dimensions related to epistemic functions (e.g., “This explanation is based on evidence”) as well as moral/social/emotional functions (e.g., “If everyone believed this, the world would be a more moral place”; “This explanation is comforting”). For non-religious participants, only scientific explanations were assigned high values along epistemic dimensions; For religious participants, only religious explanations were assigned high values along non-epistemic dimensions. In Study 2 (N=130), priming a non-epistemic need boosted religious participants’ evaluation of the quality of religious (vs. scientific) explanations. These findings shed light on the functions of scientific and religious cognition and raise new questions about explanatory co-existence and the origins of religious belief.

Piaget proposed that development proceeded in stages; morerecently researchers have proposed modular theories in whichdifferent abilities develop on their own timetable. Despite theabundance of theory, there is little empirical work on the struc-ture of developmental changes in early childhood. We inves-tigate this question using a large dataset of parent-reporteddevelopmental milestones. We compare a variety of factor-analytic item response theory models and find that variationin development from birth to 55 months of age is best de-scribed by a model with three distinct dimensions. We alsofind evidence that dimensionality increases across age, withthe youngest children described by a two-factor model. Theseresults provide a model-based method for linking holistic de-scriptions of early development to basic theoretical questionsabout the nature of change in childhood.

Sustained attention (SA) is a critical skill in which a child is able tomaintain visual attention to an object or stimulus. The current studyemploys head-mounted eye trackers to study the cognitive processesunderlying SA by analyzing micro-level behaviors during parent-child social interactions in both typically and atypically developingchildren. Specifically, we examined the role of parent look, parenttouch, and child touch on SA duration. Results show that parent lookequally influences SA in both groups, while parent touch is morecritical for SA for TD children and the child’s own touching is morecritical for SA in children with autism spectrum disorder (ASD).Implications of different pathways to maintain SA between typicallydeveloping children and children with ASD are discussed.

This is a joint work of two labs that offers a perspective ondevelopment and learning, which complements theconference’s focus on “changes in representation andprocessing abilities in development”. Strong background inecological psychology allowed us to recognize the richnessand multilayered structuring of infants’ environment, whichactively engages them and to which infants tune their action-perception. We conceptualize this environment as reliable“social physics”, constituted of predictable, enacted socialevents, in which infants learn to participate. Using bothtraditional (qualitative and quantitative) and dynamicalsystems methods, we show the structuring of such events onmultiple timescales and levels and how participating in themsculpts the child’s agency in the social world. We show howthis background allows a fresh look on language acquisitionand how it informs computational modelling of languageemergence and models of human-robot interaction.

Language acquisition research has shown that children aredelayed in their production and comprehension of truth-functional negation (e.g., “A raven is not a writing desk.”) ascompared to other kinds of negation (e.g., rejection and nonex-istence). The source of this delay is unclear, it may reflectdifficulty in mapping the concept of negation to the way itmanifests in their language, or it may be due to a lack of aconceptual or cognitive ability. This work aims to investigatethe circumstances under which a learner might infer the pres-ence of negation in a message, inspired by the approach ofPapafragou, Cassidy, and Gleitman (2007). Namely, we in-vestigate the degree to which videos in which agents fail incompleting an action encourages adult participants to infer theuse of negation in an utterance describing it. In addition toEvent Type (i.e., Failures vs. Successes), we provided par-ticipants with additional linguistic information (i.e., syntacticinformation via Jabberwocky sentences), lexical information(i.e., an alphabetical list of the content words), and Full Lin-guistic Context (the English sentence with a single item miss-ing). With adults, we ask whether learners with the ability toattend to goals and perceive deviations from their completioncould make use of this information, and if so, to what extentdo varying degrees of converging linguistic evidence furtherassist in inferring the use of a negator.

Children’s prelinguistic gestures play a central role in theircommunicative development. Early gesture use has beenshown to be predictive of both concurrent and later languageability, making the identification of gestures in video data atscale a potentially valuable tool for both theoretical and clini-cal purposes. We describe a new dataset consisting of videos of72 infants interacting with their caregivers at 11&12 months,annotated for the appearance of 12 different gesture types. Wepropose a model based on deep convolutional neural networksto classify these. The model achieves 48.32% classification ac-curacy overall, but with significant variation between gesturetypes. Critically, we found strong (0.7 or above) rank ordercorrelations between by-child gesture counts from human andmachine coding for 7 of the 12 gestures (including the criticalgestures of declarative pointing, hold outs and gives). Giventhe challenging nature of the data - recordings of many differ-ent dyads in different environments engaged in diverse activi-ties - we consider these results a very encouraging first attemptat the task, and evidence that automatic or machine-assistedgesture identification could make a valuable contribution to thestudy of cognitive development.

During sentence comprehension, listeners form expectationsabout likely structures before they have reached the end of asentence. Children are more likely than adults to ignore late-arriving evidence when it contradicts their initial parse. Whilethis difference is often ascribed to developmental changes inexecutive function, this paper investigates whether statisticalproperties of child-directed speech could be responsible forchildren’s failure to revise temporarily ambiguous sentences.We examined well-studied garden-path sentences andcalculated surprisal values derived from adult and child-directed corpora at each word. For adult corpora, surprisal washighest where the sentence structure was disambiguated. Forchild corpora, however, values at the disambiguating regionwere low relative to other words in the sentence. This suggeststhat for children, the disambiguating words may be statisticallyweak cues to ruling out their original parse, and that inprinciple, the statistics of child-directed speech couldcontribute to children’s difficulty with garden-path sentences.

Two main classes of theory have been proposed to account forinsight problem-solving performance; those that invoke theovercoming of constraints arising from prior knowledge as thesource of insight, and those that propose strategic search formoves that make progress towards a hypothesized goal state.An experiment using matchstick algebra problems assessed thecontributions of each source. Results indicate that, while priorknowledge creates the conditions under which matchstickalgebra problems are more or less difficult to solve, search formoves that make the most apparent progress towards ahypothesized goal provides the key to eventual solution.

How do neural network models of quasiregular domains learnto represent knowledge that varies in its consistency withthe domain, and generalize this knowledge appropriately?Recent work focusing on spelling-to-sound correspondencesin English proposes that a graded “warping” mechanismdetermines the extent to which the pronunciation of a newlylearned word should generalize to its orthographic neighbors.We explored the micro-structure of this proposal by training anetwork to pronounce new made-up words that were consistentwith the dominant pronunciation (regulars), were comprisedof a completely unfamiliar pronunciation (exceptions), orwere consistent with a subordinate pronunciation in English(ambiguous). Crucially, by training the same spelling-to-soundmapping with either one or multiple items, we tested whethervariation in adjacent, within-item context made a givenpronunciation more able to generalize. This is exactly whatwe found. Context variability, therefore, appears to act as amodulator of the warping in quasiregular domains.

The number of the subject in English must match the num-ber of the corresponding verb (dog runs but dogs run). Yetin real-time language production and comprehension, speak-ers often mistakenly compute agreement between the verb anda grammatically irrelevant non-subject noun phrase instead.This phenomenon, referred to as agreement attraction, is mod-ulated by a wide range of factors; any complete computationalmodel of grammatical planning and comprehension would beexpected to derive this rich empirical picture. Recent develop-ments in Natural Language Processing have shown that neuralnetworks trained only on word-prediction over large corporaare capable of capturing subject-verb agreement dependen-cies to a significant extent, but with occasional errors. In thispaper, we evaluate the potential of such neural word predic-tion models as a foundation for a cognitive model of real-timegrammatical processing. We use LSTMs, a common sequenceprediction model used to model language, to simulate six ex-periments taken from the agreement attraction literature. TheLSTMs captured the critical human behavior in three out of thesix experiments, indicating that (1) some agreement attractionphenomena can be captured by a generic sequence process-ing model, but (2) capturing the other phenomena may requiremodels with more language-specific mechanisms.

Humans carry stereotypic tacit assumptions (STAs) (Prince,1978), or propositional beliefs about generic concepts. Suchassociations are crucial for understanding natural language.We construct a diagnostic set of word prediction prompts toevaluate whether recent neural contextualized language mod-els trained on large text corpora capture STAs. Our promptsare based on human responses in a psychological study of con-ceptual associations. We find models to be profoundly effec-tive at retrieving concepts given associated properties. Our re-sults demonstrate empirical evidence that stereotypic concep-tual representations are captured in neural models derived fromsemi-supervised linguistic exposure.

Research beginning with Piaget reveals a change in infants’understanding of multistep, means-end action sequences:Whereas 12-month-old infants reason that (e.g.) one opens abox to access its contents, younger infants are more likely toreason that one’s goal is simply to open the box. Here weexplore the implications of this developmental change ininfants’ action understanding for infants’ social evaluations.Using a puppet show paradigm, we examined infants’evaluations of two agents who helped another agent to achieveeither the end or the means of a means-end sequence, bothbefore and after 12 months of age. In a subsequent preferencetest, 15-month-old infants reached for an End-Helper over aMeans-Helper, whereas 8-month-old infants did the reverse.These findings link infants’ evaluation of helpers to theirrepresentations of action plans, consistent with recentcomputational models of naïve psychology.

While it is straightforward to compare the costs of differentvariants of the same action (e.g., walking to a coffeeshop at theend of the block will always be less costly than walking to acoffeeshop three blocks away), the relative costs of differentactions are not directly comparable (e.g., would it be easier tojump over or walk around a fence?). Across two experimentswe demonstrate that 10-month-old infants spontaneouslyencode the manner of different goal-directed actions (jumpingover an obstacle vs. detouring around it, Experiment 1) and usethe principle of cost-efficiency to infer their relative costs(jumping is less costly to bypass low walls, but detouring isless costly to bypass high walls, Experiment 2). By relatingaction choices to the physical parameters of the environment,infants identify the least costly actions given thecircumstances, which allows them to make behavioralpredictions in new environments and may also enable them toinfer others’ motor competence.

People’s assessment of their ability varies in whether it is mea-sured once following a task or sequentially via confidencejudgments recorded throughout. Multiple models have beendeveloped to predict one-off judgments of performance, whichhave often distinguished between peoples’ biases about theirgeneral ability in a domain and their sensitivity to correctness.We propose a rational model of sequential self-assessmentwhich allows us to make predictions about each individualseparately—unlike in the one-off case which looks exclusivelyat the population level—and to identify, in addition to bias andsensitivity, the extent to which individuals’ beliefs are respon-sive to their most recent evidence over the course of a task. Wefit our model to data where participants solve algebraic equa-tions and show that bias, sensitivity, and responsiveness varymeaningfully across participants.

Our world is filled with statistical information: from dice rolls to lotteries, we often act based on our impressions of probability. Yet the human mind is not wired to reason about truly probabilistic events, often imposing structure on data or events where no such structure exists (as in ‘illusory correlations’). Here, we consider a case study in intuitive statistics: disjunctive events. For example, participants are asked to imagine a button that, when pressed, has a 1 in 100 chance of yielding a prize. They are told to imagine pressing that button 100 times. Across several paradigms, we show that people overestimate the probability of this disjunctive event — in stark contrast to classic demonstrations where people underestimate such probabilities (e.g., when iteratively selecting marbles from jars with replacement). These results reflect a tendency to view events as causally connected in illusory ways; implications for other domains of reasoning are discussed.

Theories of event cognition have hypothesized that the boundaries of events are characterized by change, including a change in the agent’s goal, but the role of higher-order goal information on the placement of event boundaries has not been addressed experimentally. We tested whether goals can affect how viewers determine event boundaries. Participants read a context sentence stating an agent’s goal (e.g., “Jesse wants to eat the orange with her breakfast” vs. “Jesse wants to use the orange as a garnish”). Participants then saw an image of an event outcome (e.g., a partly peeled orange) and were asked to identify whether the event had occurred (“Did she peel the orange?”). Participants were more likely to respond Yes to a partly complete outcome if the outcome satisfied the agent’s goal. Our results offer the first direct evidence in support of the conclusion that higher-order intentionality information affects the way events are conceptualized.

Exposure to images constitutes a ubiquitous day-to-dayexperience for most individuals. From mass-media exposure,to engagement with social-networking sites, to educationalcontexts, we are bombarded with images. Here, we explore theeffect that emotional images have on belief endorsement. Toinvestigate this effect, we test whether statements accompaniedby emotionally arousing images become more or lessbelievable than the same statements when they areaccompanied by neutral images or by no images. We find thatemotional images increase statement believability (Experiment1, replicated in preregistered Experiment 2). We discuss theimplications of this finding in the context of interventionsaimed at reducing misinformation.

Emotional experiences have a significant impact on learningabout complex topics. Yet, challenges exist becauseemotions are typically operationalized as end products,excluding if, how, and when emotions change duringlearning and their relation to metacognition and performancewith advanced learning technologies such as intelligenttutoring systems (ITSs). In this paper, we addressed thesechallenges by capturing and analyzing 117 college students’concurrent and self-reported emotions at 3 time points duringlearning with MetaTutor, an ITS. Analyses revealed negativerelationships between increases in boredom, metacognitivemonitoring accuracy, and performance. We also foundthat if confusion persisted over time during learning, itwas detrimental to performance. These findings provideimplications for designing affect-sensitive ITSs which fosteremotion-regulation and metacognitive monitoring based onchanges in emotions during learning to optimize performance.

For human and automatic text annotation of emotions, it is as- sumed that affect can be traced in language on (combinations of) individual words, text fragments, or other linguistic pat- terns, which can be identified and labelled correctly. For exam- ple, many sentiment analysis systems consider isolated words affectively meaningful units, whose proportions in a given text reveal its overall affective meaning. However, whether these words and their combinations as identified either by humans or algorithms also match the actual feelings of the authors remains unclear. Potential discrepancies between affect expression and perception in text have received surprisingly little scholarly at- tention, although a number of studies has already identified dis- parities between self- and other-annotation in affect detection for speech and audio-visual data. Therefore, we ask whether a similar difference shows in annotations of emotions in text.

Although many studies have shown that being exposed toempirical data that contradict one’s beliefs can lead to learning,it is not clear whether calling attention to inconsistenciesamong beliefs without the provision of new data, leads tolearning. The present study asked whether calling attention toinconsistent beliefs via thought experiments leads to beliefrevision. Five-hundred-seventy-five participants were assignedto three different conditions in a pre-training, training, post-training design. The results showed that participants generatedinconsistent beliefs between pre-training and training, but theydid not spontaneously revise them at post-training (BaselineCondition). They did revise them, however, when they wereasked to reason about the implications of the training thoughtexperiments (Warning Condition) and when they saw anexplicit inference drawn from the training thought experiments(Explicit Inference Condition). These results show that, withprompting, scientifically naïve adults can learn from thoughtexperiments.

People’s beliefs and attitudes about social and scientificissues, such as capital punishment and climate change, appearto form complex but generally coherent networks.Understanding the nature of these networks is a prerequisitefor designing interventions for changing beliefs on the basisof rational arguments and evidence. It is therefore importantto develop methods to represent and analyze the form andnature of belief networks, which may not be explicitlyverbalizable. Adopting an emerging approach that utilizescrowdsourcing to develop educational interventions, wemined discussions from the Reddit forum Change My View todetermine which beliefs and types of information underliepeople’s attitudes about capital punishment. By combiningcomputational analyses based on a topic model with morequalitative assessments of the extracted topics, we found thatmoral arguments are more prevalent than statistical ordata-based arguments. The present study serves as a test casefor the open sourced software crowdpy, a Python toolkit forrunning naturalistic studies on the web, which will enableother researchers to use crowdsourcing in their research. Thisapproach sets the stage for research exploring potentialinterventions to change people’s beliefs.

Interpersonal coordination is essential for successfulcooperative action. Beyond synchronized joint action toachieve a goal such as moving furniture, humans tend tospontaneously coordinate movement in everyday action (i.e.,coordinated limb movement during walking). Furthermore,these actions are said to arise from the interaction dominantdynamics between agents and foment cooperative behavior. Assuch, existing research demonstrates that closer affiliation isassociated with entrainment of physiological signals includingheart beat and rhythmic limb movement. Considering the rolesocial stigmatization plays in disrupting social interaction, thepresent research investigated the impact of concealable stigmadisclosure (depression diagnosis or bisexual identity)—as aperturbation to a nonlinear dynamical system—oninterpersonal coordination and affiliation. Study 1 resultsdemonstrate that depression disclosure may lead to more socialdistancing in a collision avoidance walking task compared tobisexual and neutral disclosures. In study 2, interactionimproved affiliation regardless of disclosure type.

Groups of interacting individuals often coordinate in service ofabstract goals, such as the alignment of mental representationsin conversation, or the generation of new ideas in group brain-storming sessions. What are the mechanisms and dynamicsof abstract coordination? This study examines coordination ina sophisticated paragon domain: collaboratively improvisingjazz musicians. Remarkably, freely improvising jazz ensem-bles collectively produce coherent tonal structure (i.e. melodyand harmony) in real time performance without previously es-tablished harmonic forms. We investigate how tonal structureemerges out of interacting musicians, and how this structureis constrained by underlying patterns of coordination. Dyadsof professional jazz pianists were recorded improvising in twoconditions of interaction: a ‘coupled’ condition in which theycould mutually adapt to one another, and an ‘overdubbed’ con-dition which precluded mutual adaptation. Using a computa-tional model of musical tonality, we show that this manipu-lation effected the directed flow of tonal information amongstpianists, who could mutually adapt to one another’s notes incoupled trials, but not in overdubbed trials. Consequently,musicians were better able to harmonize with one another incoupled trials, and this ability increased throughout the courseof improvised performance. We present these results and dis-cuss their implications for music technology and joint actionresearch more generally.

From childhood, people routinely explain each other’s behav-ior in terms of inferred mental states, like beliefs and desires.In many cases, however, people can also infer the mental statesof agents whose behavior we cannot see, such as when we in-fer that someone was anxious upon encountering a chewed-uppencil, or that someone left in a hurry if they left the door open.Here we present a computational model of mental-state attri-bution that works by reconstructing the actions an agent took,based on the indirect evidence that revealed their presence. Ourmodel quantitatively fits participant judgments, outperforminga simple alternative cue-based account. Our results shed lighton how people infer mental states from minimal indirect evi-dence, and provides further support to the idea that human The-ory of Mind is instantiated as a probabilistic generative modelof how unobservable mental states produce observable action.

Is cognitive science interdisciplinary or multidisciplinary? Wecontribute to this debate by examining the authorship struc-ture and topic similarity of contributions to the Cognitive Sci-ence Society from 2000 to 2019. We compare findings fromCogSci to abstracts from the Vision Science Society over thesame time frame. Our analysis focuses on graph theoretic fea-tures of the co-authorship network—edge density, transitivity,and maximum subgraph size—as well as clustering within thetopic space of CogSci contributions. We also combine struc-tural and semantic information with an analysis of homophily.We validate this approach by predicting new collaborations inthis year’s CogSci proceedings. Our results suggest that cog-nitive science has become increasingly interdisciplinary in thelast 19 years. More broadly, we argue that a formal quantita-tive approach which combines structural co-authorship infor-mation and semantic topic analysis provides inroads to ques-tions about the level of interdisciplinary collaboration in thecognitive science community.

Prediction problems vary in the extent to which accuracy isrewarded and inaccuracy is penalized—i.e., in their loss func-tions. Here, we focus on a particular feature of loss functionsthat controls how much large errors are penalized relative tohow much precise correctness is rewarded: convexity. Weshow that prediction problems with convex loss functions (i.e.,those in which large errors are particularly harmful) favor sim-pler models that tend to be biased, but exhibit low variability.Conversely, problems with concave loss functions (in whichprecise correctness is particularly rewarded) favor more com-plex models that are less biased, but exhibit higher variabil-ity. We discuss how this relationship between the bias-variancetrade-off and the shape of the loss function may help explainfeatures of human psychology, such as dual-process psychol-ogy and fast versus slow learning strategies, and inform statis-tical inference.

There is a considerable amount of literature on the role ofimmersion and presence in virtual reality learningenvironments. Far less is known about the interaction ofimmersion and presence with the important individualcharacteristics that influence learning behavior, particularly,dispositional awe. Dispositional awe is manifested by anemotional response to information that defies existing mentalschemas in a given domain and by a need to accommodate thisexperience. In a virtual reality study with eight elementaryschool classes, we investigated the interaction of immersivetendencies with dispositional awe and compassion on learninggains in the domain of nature conservation. We tested thisinteraction using a novel virtual reality concept in whichchildren are sent to virtually simulated space to experience theoverview effect, a cognitive shift in awareness reported byastronauts. The findings of the study showed that participantsexperienced strong feelings of awe and scored highly onoverview effect constructs. Importantly, their learning gainswere influenced by the overview effect which was, in turn,supported by presence, dispositional awe, and compassion.This study shows the potential of using immersive virtualreality experiences in educational programs, combiningwonder and learning.

Without conscious thought, listeners link events in the worldto sounds they hear. We study one surprising example: Adultscan judge the temperature of water simply from hearing itbeing poured. How do these nuanced perceptual skillsdevelop? Is extensive auditory experience required, or arethese skills present in early childhood? In Exp.1, adults wereexceptionally good at judging whether water was hot vs. coldfrom pouring sounds (M=93% accuracy; N=104). In Exp.2, wetested this ability in N=113 children aged 3-12 years, and foundevidence of developmental change: Age significantly predictedaccuracy (p<0.001, logistic regression), such that 3-5 year oldchildren performed at chance while 85% of children age 6+answered correctly. Overall our data suggest that perception ofnuanced differences between auditory events is not part ofearly-developing cross-modal cognition, and instead developsover the first six years of life.

Sensitivity to transitional probabilities (TP) in continuous speech has been extensively documented, yet little is knownabout how infants represent sequences that are the output of statistical learning. Across 3 experiments we test 8-month-old English-learning infants indexical, segmental, and suprasegmental representations of newly-encountered statistically-defined words. Following familiarization with a naturally-produced Italian corpus that contained two trochaic (strong-weak) high TP (HTP) words produced by a female speaker, infants were tested on their ability to discriminate modifiedHTP words (Experiment 1=male voice; Experiment 2=onset consonant change); Experiment 3=iambic stress pattern),from foils. Infants demonstrated a significant familiarity preference for modified HTP words in Experiments 1 and 3,but failed to recognize consonant modified HTP words in Experiment 2. Findings demonstrate infants can generalizerepresentations of statistically-defined words across a range of acoustic forms less relevant to word meaning in English,but not across phonemic characteristics that are core to word meaning.

How do children learn the typical features of objects in theworld? For many objects, this information must come from thelanguage they hear. However, language does not veridicallyreflect the world: People are more likely to talk about atypicalfeatures (e.g., “purple carrot”) than typical features (e.g., “or-ange carrot”). Does the speech children hear from their parentsalso overrepresent atypical features? We examined the typical-ity of adjectives produced by parents in a large, longitudinalcorpus of parent-child interaction. Across nearly 2000 uniqueadjective–noun pairs, we found parents’ adjectives predomi-nantly mark atypical features of objects, although parents ofvery young children are relatively more likely to comment ontypical features as well. We then used vector space models toshow that learning the typical features of common categoriesfrom linguistic input alone is challenging even with sophisti-cated statistical inference techniques.

Perceptual judgments result from a dynamic process, but little is known about the dynamics of numerosity estimation. Arecent study proposed a computational model (D-MLLM) that combined a model of trial-to-trial changes with a modelfor the internal scaling of discrete number. Here, we tested a surprising prediction of the model – a situation in whichchildren’s estimates of numerosity would be better than those of adults. Consistent with the model simulations, taskcontexts led to a clear developmental reversal: children made more adult-like, linear estimates when to-be-estimatednumbers were descending over trials (backward), whereas adults became more like children with log estimates whennumbers were ascending (forward). In addition, adults’ estimates were subject to inter-trial differences regardless ofstimulus order. In contrast, children were not able to use the trial-to-trial dynamics unless task contexts were salient(forward or backward order), indicating the limited memory capacity for dynamic updates. Together, the model adequatelypredicts both developmental and trial-to-trial changes in number-line tasks.

Humans make frequent and powerful use of external symbolsto express number exactly, leading some to question whetherexact number concepts are only available through the acqui-sition of symbolic number systems. Although prior work hasaddressed this longstanding debate on the relationship betweenlanguage and thought in innumerate populations and semi-numerate children, it has frequently produced conflicting re-sults, leaving the origin of exact number concepts unclear.Here, we return to this question by replicating methods pre-viously used to assess exact number knowledge in innumer-ate groups, such as the Pirah ̃a, with a large sample of semi-numerate US toddlers. We replicate previous findings fromboth innumerate cultures and developmental studies showingthat numeracy is linked to the concept of exact number. How-ever, we also find evidence that this knowledge is surprisinglyfragile even amongst numerate children, suggesting that nu-meracy alone does not guarantee a full understanding of exact-ness.

The Give-a-Number task has become a gold standard ofchildren’s number word comprehension and has beenincreasingly used to organize debate in developmentalpsychology. In this task, the experimenter asks children togive specific numbers of objects (e.g., 1 to 6), and based ontheir pattern of responses, children are classified into stagesthat can be readily related to other developmental milestones.The increasing popularity of Give-a-Number raises thequestion of how reliable it is, since the size of a correlationbetween two different tasks cannot reliably exceed the test-retest reliability of either measure taken individually. InExperiment 1, 2- to 4-year-old children were tested twice in asingle session with Wynn’s (1992) version of the Give-a-Number task, which features a titrated design. In Experiment2, we tested a second group of children with an alternativeversion that uses a larger number of trials in a non-titrateddesign. We found that in both cases the task was highlyreliable in differentiating children who could accurately countfrom those who could not, but that reliability differed forspecific numbers, and was more reliable for very smallnumbers (i.e., “one” and “two”) than for slightly larger ones(i.e., “three” and “four”). We discuss practical implications ofthese results for researchers studying numeracy and discussfurther directions to assess the validity of the task.

Although most U.S. children can count sets by 3.5 years of age, many fail to understand that adding 1 to a set correspondsto counting up 1 word in the count list (i.e., the successor function). Initially, children have piecemeal knowledge of thisrelation, and do not understand that it holds for any number. Although generalized successor knowledge emerges around6 years of age, it is unknown when children’s item-based learning begins, and therefore when they begin learning relationsbetween number words – a critical precursor to mathematical reasoning. Here, we explore the timescale and mechanismsunderlying this knowledge in 2- to 4-year-old children. We find that these children have established item-based mappings,but that they are unrelated to count list knowledge. Instead, we show evidence that the origins of successor knowledgemay lie in mappings made between non-symbolic set representations and known number words.

On a now orthodox view, humans and many other animals areendowed with a “number sense”, or approximate number system(ANS), that represents number. Recently, this orthodox view hasbeen subject to numerous critiques, with critics maintaining eitherthat numerical content is absent altogether, or else that someprimitive analog of number (‘numerosity’) is represented as opposedto number itself. We distinguish three arguments for these claims –the arguments from congruency, confounds, and imprecision – andshow that none succeed. We then highlight positive reasons forthinking that the ANS genuinely represents numbers. The upshot isthat proponents of the orthodox view should not feel troubled byrecent critiques of their position.

Understanding a gradable adjective (e.g., big) requires mak-ing reference to a comparison class, a set of objects or entitiesagainst which the referent is implicitly compared (e.g., big fora Great Dane), but how do listeners decide upon a compari-son class? Simple models of semantic composition stipulatethat the adjective combines with a noun, which necessarily be-comes the comparison class (e.g., “That Great Dane is big”means big for a Great Dane). We investigate an alternativehypothesis built on the idea that the utility of a noun in anadjectival utterance can be either for reference (getting the lis-tener to attend to the right object) or predication (describing aproperty of the referent). Therefore, we hypothesize that whenthe presence of a noun N can be explained away by its util-ity in reference (e.g., being in the subject position: “That N isbig”), it is less likely to set the comparison class. Across threepre-registered experiments, we find evidence that listeners usethe noun as a cue to infer comparison classes consistent with atrade-off between reference and predication. This work high-lights the complexity of the relation between the form of anutterance and its meaning.

Naming a picture is more difficult in the context of a taxonomically-related picture. Disagreement exists on whether non-taxonomic relations, e.g., associations, have similar or different effects on picture naming. Past work has reported facilitation, interference and null results but with inconsistent methodologies. We paired the same target word (e.g., cow) with unrelated (pen), taxonomically-related (bear), and associatively-related (milk) items in different blocks, as participants repeatedly named one of the two pictures in randomized order. Significant interference was uncovered for the same target item in the taxonomic vs. unrelated and associative blocks. There was no robust evidence of interference in the associative blocks. If anything, evidence suggested that associatively-related items marginally facilitated production. This finding suggests that taxonomic and associative relations have different effects on picture naming and has implications for theoretical models of lexical selection and, more generally, for the computations involved in mapping semantic features to lexical items.

Previous research has demonstrated that Distributional Semantic Models (DSMs) are capable of reconstructing maps from news corpora (Louwerse & Zwaan, 2009) and novels (Louwerse & Benesh, 2012). The capacity for reproducing maps is surprising since DSMs notoriously lack perceptual grounding (De Vega et al., 2012). In this paper we investigate the statistical sources required in language to infer maps, and resulting constraints placed on mechanisms of semantic representation. Study 1 brings word co-occurrence under experimental control to demonstrate that direct co-occurrence in language is necessary for traditional DSMs to successfully reproduce maps. Study 2 presents an instance-based DSM that is capable of reconstructing maps independent of the frequency of co-occurrence of city names.

Recognition of the similarities between programming and nat-ural languages has led to a boom in the adoption of languagemodeling techniques in tools that assist developers. However,language model surprisal, which guides the training and eval-uation in many of these methods, has not been validated asa measure of cognitive difficulty for programming languagecomprehension as it has for natural language. We perform acontrolled experiment to evaluate human comprehension onfragments of source code that are meaning-equivalent but withdifferent surprisal. We find that more surprising versions ofcode take humans longer to finish answering correctly. Wealso provide practical guidelines to design future studies forcode comprehension and surprisal.

In the first year of life, infants’ speech perception becomesattuned to the sounds of their native language. Many accountsof this early phonetic learning exist, but computational modelspredicting the attunement patterns observed in infants fromthe speech input they hear have been lacking. A recent studypresented the first such model, drawing on algorithms proposedfor unsupervised learning from naturalistic speech, and tested iton a single phone contrast. Here we study five such algorithms,selected for their potential cognitive relevance. We simulatephonetic learning with each algorithm and perform tests onthree phone contrasts from different languages, comparing theresults to infants’ discrimination patterns. The five models dis-play varying degrees of agreement with empirical observations,showing that our approach can help decide between candidatemechanisms for early phonetic learning, and providing insightinto which aspects of the models are critical for capturing in-fants’ perceptual development.

In acquiring language, differences in input can greatly affectlearning outcomes, but which aspects of language learning aremost sensitive to input variations, and which are robust, remainsdebated. A recent modeling study successfully reproduced aphenomenon empirically observed in early phonetic learning—learning about the sounds of the native language in the firstyear of life—despite using input that differed in quantity andspeaker composition from what a typical infant would hear. Inthis paper, we carry out a direct test of that model’s robustnessto input variations. We find that, despite what the original resultsuggested, the learning outcomes are sensitive to properties ofthe input and that more plausible input leads to a better fit withempirical observations. This has implications for understandingearly phonetic learning in infants and underscores the impor-tance of using realistic input in models of language acquisition.

We present the Perceptimatic English Benchmark, an open ex-perimental benchmark for evaluating quantitative models ofspeech perception in English. The benchmark consists of ABXstimuli along with the responses of 91 American English-speaking listeners. The stimuli test discrimination of a largenumber of English and French phonemic contrasts. They areextracted directly from corpora of read speech, making themappropriate for evaluating statistical acoustic models (such asthose used in automatic speech recognition) trained on typicalspeech data sets. We show that phone discrimination is corre-lated with several types of models, and give recommendationsfor researchers seeking easily calculated norms of acoustic dis-tance on experimental stimuli. We show that DeepSpeech,a standard English speech recognizer, is more specialized onEnglish phoneme discrimination than English listeners, and ispoorly correlated with their behaviour, even though it yields alow error on the decision task given to humans.

Humans learn much about their language while still in thewomb. Prenatal exposure has been repeatedly shown to affectnewborn infants’ processing of the prosodic characteristics ofnative language speech. Little is known about whether and howprenatal exposure affects infants’ perception of speech soundsegments. Here we simulated prenatal learning of vowels intwo virtual fetuses whose mothers spoke (slightly) differentlanguages. The learners were two-layer neural networks andwere each exposed to vowel tokens sampled from an existentfive-vowel language (Spanish and Czech, respectively). Theinput acoustic properties approximated the speech signal thatcould possibly be heard in the intrauterine environment, andthe learners’ auditory system was relatively immature. Withoutsupervision, the virtual fetuses came to warp the continuousacoustic signal into “proto-categories” that were specific totheir linguistic environment. Both learners came to create twocategorization patterns and did so in language-specific ways,primarily on the basis of the vowels’ first-formantcharacteristics. Such prenatally formed proto-categories werenot adult-like in that they entirely collapsed some of the native-language contrasts. At the same time, the categories reflectedfeatures of the adult language in that they were language-specific. These results can inspire future work on speech andlanguage acquisition in real young humans.

Spatial demonstratives (words like this and that) are thought toprimarily be used for carving up space into a peripersonal andextrapersonal domain. However, when given a noun out ofcontext and asked to couple it with a demonstrative, speakerstend to use this for manipulable objects (small, harmless,inanimate), while non-manipulable objects (large, harmful,animate) are more likely to be coupled with that. Here, weextend these findings and map demonstrative use along a widespectrum of semantic features. We conducted a large-scale (N= 2197) experiment eliciting demonstratives for 506 words,rated across 65+11 perceptually and cognitively relevantsemantic dimensions. We replicated the findings thatdemonstrative choice is influenced by object manipulability.Demonstrative choice was additionally found to be related to aset of semantic factors, including valence, arousal, loudness,motion, time and more generally, the self. Importantly,demonstrative choices were highly structured acrossparticipants, as shown by a strong correlation detected in asplit-sample comparison of by-word demonstrativedistribution.

The Temporal Focus Hypothesis proposes that a person’s tendency to conceptualize either the past or the future as beinglocated in front of them depends on their temporal focus: the balance of attention paid to the past (tradition) and thefuture (progress). How general is the TFH and to what extent can cultures and subcultures be placed on a single linerelating time spatialization and temporal focus in spite of stark differences in language, religion, history, and economicdevelopment? Data from 10 Western and Middle Eastern (sub)cultural groups (N=1198) were used to derive a linearmodel relating aggregated temporal focus and proportion of future-in-front responses. This model then successfully fittednine independently collected (sub)cultural groups in China and Vietnam (N=841). A logistic mixed model computedover the whole dataset (N=2039) showed that the group-level relation arose at the individual level and allowed precisequantification of its influence. Temporal focus shapes how people around the world think of time in spatial terms.

Field scientists collect data in a noisy heterogeneous environment, where the value of additional data for characterizingthe natural system is weighed against the time and money involved in data collection. This is analogous to foraging forfood data is the resource and its collection can be optimized based on energy costs. Here we conduct a novel simulateddata foraging study to elucidate how spatiotemporal data collection decisions are made in field sciences, and how search isadapted in response to in-situ data. Expert geoscientists were asked to evaluate a hypothesis by collecting environmentaldata using a mobile robot. At any point, participants were able to stop the robot and change their search strategy ormake a conclusion about the hypothesis. We identified previously unrecognized spatiotemporal reasoning heuristics, towhich scientists strongly anchored, displaying limited adaptation in response to new data. We analyzed two key decisionfactors: variable-space coverage, and fitting error to a given hypothesis. We found that, despite varied search strategies, themajority of scientists made a conclusion as the fitting error converged. Scientists who made premature conclusions, eitherdue to insufficient variable-space coverage or before the fitting error stabilized, were more prone to incorrect conclusions.We believe the findings from this study could be used to improve field science training in data foraging, and aid in thedevelopment of technologies to support data collection decisions.

From early in life, people implicitly associate time, number,and other abstract conceptual domains with space. Accord-ing to the Generalized Magnitude System proposal, these men-tal mappings reflect a common neural system for represent-ing various magnitudes, and share a common spatial organiza-tion. In a test of this proposal, here we measured mappings ofsize, time, and number in the Tsimane’, an indigenous Ama-zonian group with few of the cultural practices (like readingand math) that spatialize size, time, and number in the expe-rience of industrialized adults. On three spatial axes, the Tsi-mane’ systematically arranged imagistic stimuli according totheir magnitudes, but they showed no directional preferencesoverall and individuals often mapped different domains in op-posite directions. The results are inconsistent with predictionsof the Generalized Magnitude System proposal but can be ex-plained by Hierarchical Mental Metaphor Theory, accordingto which mental mappings initially reflect a set of correlationsobservable in the natural world.

We present a neural dynamic architecture that grounds sen-tences in perception which combine multiple concepts throughnested spatial relations. Grounding entails that the model getsfeatures and relations as categorical inputs and matches themto objects in space-continuous neural maps which represent vi-sual input. The architecture is based on the neural principlesof dynamic field theory. It autonomously generates sequencesof processing steps in continuous time, based solely on highlyrecurrent connectivity. Simulations of the architecture showthat it can ground sentences of varying complexity. We thusaddress two major challenges in dealing with nested relations:how concepts may appear in multiple different relational roleswithin the same sentence, and how in such a scenario variousgrounding outcomes may be “tried out” in a form of hypothesistesting. We close by discussing empirical evidence for crucialassumptions and choices made when developing the architec-ture.

Successful categorization requires a careful coordination ofattention, representation, and decision making. Comprehensivetheories that span levels of analysis are key to understandingthe computational and neural dynamics of categorization. Here,we build on recent work linking neural representations ofcategory learning to computational models to investigate howcategory decision making is driven by neural signals across thebrain. We combine functional magnetic resonance imagingwith hierarchical drift diffusion modelling to show that trial-by-trial fluctuations in neural activation from regions ofoccipital, cingulate, and lateral prefrontal cortices are linked tocategory decisions. Notably, lateral prefrontal cortex activationwas also associated with exemplar-based model predictions oftrial-by-trial category evidence. We propose that these brainregions underlie distinct functions that contribute to successfulcategory learning.

Traditional models of category learning in psychology focuson representation at the category level as opposed to the stim-ulus level, even though the two are likely to interact. Thestimulus representations employed in such models are eitherhand-designed by the experimenter, inferred circuitously fromhuman judgments, or borrowed from pretrained deep neuralnetworks that are themselves competing models of categorylearning. In this work, we extend classic prototype and ex-emplar models to learn both stimulus and category represen-tations jointly from raw input. This new class of models canbe parameterized by deep neural networks (DNN) and trainedend-to-end. Following their namesakes, we refer to them asDeep Prototype Models, Deep Exemplar Models, and DeepGaussian Mixture Models. Compared to typical DNNs, wefind that their cognitively inspired counterparts both providebetter intrinsic fit to human behavior and improve ground-truthclassification.

A classic issue in the cognitive-science of human categorylearning has involved the contrast between exemplar and prototypemodels. However, experimental tests to distinguish the models haverelied almost solely on use of artificial categories composed ofsimplified stimuli. Here we contrast the predictions from the modelsin a real-world natural-science category domain – geologic rocktypes. Previous work in this domain used a set of complementarymethods, including multidimensional scaling and direct dimensionratings, to derive a high-dimensional feature space in which the rockstimuli are embedded. The present work compares the category-learning predictions of exemplar and prototype models that makereference to this derived feature space. The experiments includeconditions that should be favorable to prototype abstraction,including use of large-size categories, delayed transfer testing, andreal-world natural category structures. Nevertheless, the results ofthe qualitative and quantitative model comparisons point toward theexemplar model as providing a better account of the observedresults. Limitations and directions of future work are discussed.

How do cumulative cultural evolution and individual learningdiffer? In an abstract computational sense, both are optimisa-tion processes that search a space of possible explanations andprevious work has identified deep parallels in the mathematicalmodels used to describe them (Suchow, Bourgin, & Griffiths,2017). However, there are obvious differences as well: forexample, individual learning involves a single agent charac-terised by one set of prior beliefs, representational capabilities,and so forth, while cultural evolution involves multiple agentswho may vary along these factors. We argue that this differ-ence implies that the process of cumulative cultural evolutionshould involve searching a more restricted set of hypothesesand converge on simpler ones. In two iterated category learn-ing experiments, we test this prediction and find that transmis-sion chains composed of single individuals, who learn basedon their previous performance, consider both a wider varietyand more complex categorisation schemas than do chains in-volving multiple people.

This study intended to investigate the effects of varying factorson the use of verbal and implicit classification systems whenlearning novel categories in an interactive video gameenvironment by measuring the effects of feature type (easy vsdifficult to describe verbally). Verbal and implicitclassification were operationalized by measuring rule-basedand family resemblance strategy use respectively. Thisexperiment found that participants presented with stimuli thatwere easy to describe verbally were more likely to use rule-based classification, while participants presented with stimulithat were difficult to describe verbally showed no preferencefor one form of classification. The results of this study open upa novel field of research within category learning, furtherexploring the effects of feature verbalizablity.

Article production difficulties in Spanish-speaking children with specific language impairment (SLI) are well documented. However, evidence on article comprehension is scarce. In an eye tracking experiment, we compared online comprehension of definite and indefinite articles in monolingual Spanish- speaking children with SLI and children with typical language development (TLD) matched for age. Children listened to simple phrases while inspecting a visual context with four images. The article in the phrase agreed in number and gender with the target image only. Visual target preference was monitored as the phrase unfolded. Eye movements revealed that children with SLI showed a weak preference for the target on indefinite article trials only after hearing the noun, although no significant effects of definiteness were observed. In contrast, children with TLD were able to use the article to anticipate the noun. These findings contribute to reducing the gap between article production and comprehension in children with SLI.

Electrical brain potentials in response to violation of expectations in language processing have revealed that people usesentence context to facilitate word recognition and integration. Less is known about the interaction between the qualityof visual information in reading and the use of contextual information. In the current study we manipulated the visualfield (foveal vs. parafoveal) in which a sentence-final expected word, orthographic neighbor of an expected word, orunexpected word is presented and recorded event-related potentials (ERPs) to investigate the role of visual clarity. We findevidence that earlier stages of semantic retrieval indexed by the N400 are resilient to visual information presented at greatereccentricity, but that later, integration-related processes indexed by a posterior late positive complex (LPC) may depend onunambiguous, foveally presented visual information. These findings have implications for parafoveal processing duringnatural reading.

We explore low-level, behavioural universals in reading,across English and Chinese. We investigated binocularcoordination in terms of the small non-alignments betweenthe two eyes’ fixations in time. We define a typology of ninesuch asynchronies and report the different spatial distributionsof these types across the screen of text. We interpret them interms of their implications for ocular prevalence—theprioritizing of the input from one eye over the input from theother eye in higher perception/cognition, after binocularfusion. The results show striking similarities of binocularreading behaviours across the two very differentorthographies. Asynchronies in which one eye begins thefixation earlier and/or ends it later occur most frequently inthe hemifield corresponding to that eye. We propose that suchsmall asynchronies in binocular fixations prioritize the higherprocessing of the input from that eye, after binocular fusion.

Four structural priming experiments investigated the lexical boost effect in structural priming. In two experiments, wetested whether repeating the subject in prepositional object or double object ditransitive structures boosted structuralpriming. In two other experiments, we manipulated the repetition of the verb. Repetition of the subject noun affectedstructural priming, but only when the prime remained visible while participants produced the target sentence. In contrast,repetition of the verb boosted priming regardless of whether participants could see the prime and target simultaneously.We conclude that the subject noun repetition effect is more strategic in nature than the verb boost effect. Structures areautomatically associated with the verb, their syntactic head, whereas repetition of the subject noun only affects priming ifthe presentation method makes the repetition highly explicit.

The paper investigates the existence of position-independent segments in written and typed word production. In two experiments, we employed the segmental interference effect to first replicate past findings that naming a picture is more difficult in the context of another picture with which it shares segments in the same position (e.g., glow-flow) compared to an unrelated word (e.g., glow-cave). We then tested a new condition, in which the same target word is paired with an anagram of the original competitor (glow-wolf). Critically, the anagram shared the same number of segments with the target word, but never in the same position. Both experiments found robust interference for targets produced in the context of anagrams, with a magnitude comparable to the interference induced by the position-overlapping word. The results suggest that not only are position-independent segments represented in the production system, but they also play a critical role in activating segmentally related words and creating competition during word production.

Generic language (e.g., “Women are nurturing”; “Women donot like math”) is prominent in child-directed speech, and hasbeen shown to promote essentialist beliefs about the relevantkind, supporting stereotyping and prejudice. Here weinvestigate a theoretically-motivated intervention to break thelink between generics and essentialist assumptions. In a studywith 223 3-8-year-old children who learned about novel socialgroups from generic language, we demonstrate that a structuralconstrual of generics (attributing the category-propertyassociation to stable external constraints) mitigates essentialistassumptions about social categories. We discuss practicalapplications for reducing stereotype endorsement, andtheoretical implications regarding the meaning of genericlanguage and the development of social kind representations.

Children learn language from the speech in their home envi-ronment. Recent work shows that more infant-directed speech(IDS) leads to stronger lexical development. But what makesIDS a particularly useful learning signal? Here, we expandon an attention-based account first proposed by R ̈as ̈anen etal. (2018): that prosodic modifications make IDS less pre-dictable, and thus more interesting. First, we reproduce thecritical finding from R ̈as ̈anen et al.: that lab-recorded IDS pitchis less predictable compared to adult-directed speech (ADS).Next, we show that this result generalizes to the home lan-guage environment, finding that IDS in daylong recordings isalso less predictable than ADS but that this pattern is muchless robust than for IDS recorded in the lab. These results linkexperimental work on attention and prosodic modifications ofIDS to real-world language-learning environments, highlight-ing some challenges of scaling up analyses of IDS to largerdatasets that better capture children’s actual input.

We report two experiments investigating how the interpretationof subject-position pronouns is guided by the referentialstructure of the pronoun-containing clause, and how thisinformation interacts with information available in the clausethat precedes the pronoun. Thus, we consider information thatis available to the language processing system before thepronoun is encountered (pre-pronominal information), as wellas information that comes after the pronoun (post-pronominalinformation). In particular, we test how implicit causalitybiases of verbs that precede the pronoun-containing clauseinteract with the referential structure of the pronoun-containingclause, i.e., whether or not the clause with the pronoun containsanother ambiguous pronoun. We report two offline studieswhose results reveal significant effects of both pre- and post-pronominal referential information on pronoun resolution: Inaddition to replicating effects of implicit causality biasesobserved in prior work, we also show that people’s referentialbiases depend on whether the clause contains only a subject-position pronoun or also a second pronoun in object position.

A recent large-scale wug-task study found that non-nativespeakers of English tend to produce fewer regular past-tense-ed inflections than native speakers (Cuskley et al., 2015). Inthis paper we present a model that can account for this dif-ference in behaviour as resulting from a difference in inputamounts and distributions. This model attends to both fre-quency, using Bayesian non-parametric methods, and phono-logical similarity between words, using a neural model of wordforms, and unifies these factors within a single probabilisticframework. We show that the general pattern of over-use ofirregular inflections in non-native speakers can result simplyfrom exposure to a smaller amount of input and does not re-quire any model-internal distinction of native and non-nativespeakers. Our model also captures the interaction betweenclass frequency and phonological similarity that was evidentacross all participant productions.

As we transition from child to adult, we navigate the worlddifferently. In this world, many of the relationships betweenevents are unclear or uncertain because they are probabilisticin nature. We wanted to know how learning about probabilis-tic relationships changes with development and to interrogatethe underlying processes. We investigated these questions in aprobabilistic reinforcement learning task (The Butterfly Task)with 302 participants aged 8-30. We found performance in thistask increased with age through early-twenties, then stabilized.Using hierarchical Bayesian methods to fit computational rein-forcement learning models, we showed that this performanceincrease was driven by 1) an increase in learning rate (i.e. de-crease in integration time horizon); 2) a decrease in exploratorychoices. By contrast, forgetting rates did not change with age.We discuss our findings in the context of other studies and hy-potheses about adolescent brain development.

When learning to partition the world into categories, peoplerely on a set of assumptions (overhypotheses) about possi-ble category structures. Here we propose that the nature ofthese overhypotheses depends on the presence of a verbal la-bel associated with a given category. We describe a computa-tional model that demonstrates how labels can either acceler-ate or hinder category learning, depending on whether or notthe prior beliefs imposed by their presence align with the truecategory structure. This account provides an explanation forthe phenomena described in prior experimental work (Lupyan,Rakison, & McClelland, 2007; Brojde, Porter, & Colunga,2011) that have remained unexplained by other models. Basedon these results, we argue that the overhypothesis theory of la-bel effects provides a way to formalize and quantify the effectof language on category learning and to develop a more precisedelineation between linguistic and non-linguistic thought.

An elaborate repertoire of emotions is one feature that dis-tinguishes humans from animals. Language offers a criticalform of emotion expression. However, it is unclear whetherthe meaning of an emotion word remains stable, and what fac-tors may underlie changes in emotion meaning. We hypothe-size that emotion word meanings have changed over time andthat the prototypicality of an emotion term drives this changebeyond general factors such as word frequency. We developa vector-space representation of emotion and show that thismodel replicates empirical findings on prototypicality judg-ments and basic categories of emotion. We provide evidencethat more prototypical emotion words have undergone lesschange in meaning than peripheral emotion words over the pastcentury, and that this trend holds within each family of emo-tion. Our work extends synchronic theories of emotion to itsdiachronic development and offers a computational character-ization of emotion semantics in natural language use.

How do learners acquire languages from the limited data avail-able to them? This process must involve some inductivebiases—factors that affect how a learner generalizes—but it isunclear which inductive biases can explain observed patternsin language acquisition. To facilitate computational model-ing aimed at addressing this question, we introduce a frame-work for giving particular linguistic inductive biases to a neu-ral network model; such a model can then be used to em-pirically explore the effects of those inductive biases. Thisframework disentangles universal inductive biases, which areencoded in the initial values of a neural network’s param-eters, from non-universal factors, which the neural networkmust learn from data in a given language. The initial statethat encodes the inductive biases is found with meta-learning,a technique through which a model discovers how to acquirenew languages more easily via exposure to many possible lan-guages. By controlling the properties of the languages that areused during meta-learning, we can control the inductive biasesthat meta-learning imparts. We demonstrate this frameworkwith a case study based on syllable structure. First, we specifythe inductive biases that we intend to give our model, and thenwe translate those inductive biases into a space of languagesfrom which a model can meta-learn. Finally, using existinganalysis techniques, we verify that our approach has impartedthe linguistic inductive biases that it was intended to impart.

Anthropologists and psychologists have long studied how liv-ing kinds are organized into categories, and a recurring themeconcerns the relationship between folk categories and thestructure of the environment. We ask whether the frequencyand physical size of a species affect how it is classified, andaddress this question by linking frequency data from eBird (anonline database of bird observations) with an existing taxon-omy of Zapotec bird names. A first set of analyses exploreswhether frequency and size predict whether a bird is namedand how many other birds it is grouped with. A second setexplores whether frequency and size predict the word formsused as category labels. We find some evidence that frequencyaffects both category extensions and naming, but the resultshint that frequency may be dominated by other factors such asperceptual similarity.

Probability matching—where subjects given probabilistic in-put respond in a way that is proportional to those inputprobabilities—has long been thought to be characteristic ofprimate performance in probability learning tasks in a vari-ety of contexts, from decision making to the learning of lin-guistic variation in humans. However, such behaviour is puz-zling because it is not optimal in a decision theoretic sense;the optimal strategy is to always select the alternative with thehighest positive-outcome probability, known as maximising(in decision making) or regularising (in linguistic tasks). Whilethe tendency to probability match seems to depend somewhaton the participants and the task (i.e., infants are less likelyto probability match than adults, monkeys probability matchless than humans, and probability matching is less likely inlinguistic tasks), existing studies suffer from a range of defi-ciencies which make it difficult to robustly assess these dif-ferences. In this paper we present three experiments whichsystematically test the development of probability matchingbehaviour over time in simple decision making tasks, acrossspecies (humans and Guinea baboons), task complexity, andtask domain (linguistic vs non-linguistic). In Experiments 1and 2 we show that adult humans and Guinea baboons exhibitsimilar behaviour in a non-linguistic decision-making task and,contrary to the prevailing view, a tendency to maximise (ba-boons) or significantly over-match (humans) rather than prob-ability match, which strengthens over time and more so withgreater task complexity; our non-human sample size (N = 20baboons) is unprecedented in the probability-matching litera-ture. Experiment 3 provides evidence against domain-specificprobability learning mechanisms, showing that human subjectsover-match high positive-outcome probabilities to a similar de-gree across linguistic and non-linguistic tasks. Our results sug-gest that previous studies may simply have insufficient trials toshow maximising, or be too short to show maximising strate-gies which unfold over time. We thus provide evidence ofshared probability learning mechanisms not only across lin-guistic and non-linguistic tasks but also across primate species.

Stimulus recency has a strong effect on both behavior and neural responses. Its effects on neural responses have been most closely studied in the visual system in inferotemporal cortex (IT) in which recency gives rise to suppressed responses by a phenomenon known as repetition suppression. This observation has led to many possible explanations of how repetition suppression arises in the visual system. Here, we explore three of them: (1) top-down, (2) bottom-up and (3) independently in each brain region. Each of these accounts makes different predictions about the pattern of effects at different stages in visual processing for cases in which the stimulus either is or is not a match for the location or the identity of the preceding stimulus. We tested these predictions by recording from neurons in IT and V2, two separate stages of processing, while monkeys viewed displays of repeated and non-repeated image sequences.

Do people hold different kinds of beliefs about gods and spiritsthan they do about the everyday world? Many say no: that tothe faithful, gods and spirits are real in the same way that tablesand chairs are real. Yet experimental studies have found thatspeakers of American English tacitly distinguish between twocognitive attitudes—one for factual beliefs and one forreligious credences—through their differential use of the words“think” and “believe” (Heiphetz, Landers, and Van Leeuwen,2018). In three large-scale studies—conducted in fivestrikingly different linguistic and cultural-religious contexts(from west to east: the US, Ghana, Thailand, China, andVanuatu)—we demonstrate that such linguistic differentiationof factual belief and religious credence is cross-culturallyrobust. This lends support to the hypothesis that human theoryof mind includes nuanced distinctions among differentvarieties of “belief.”

Adaptive generation of spacing intervals in learning usingresponse times improves learning relative to both adaptivesystems that do not use response times and fixed spacingschemes (Mettler, Massey & Kellman, 2016). Studies haveoften used limited presentations (e.g., 4) of each learningitem. Does adaptive practice benefit learning if items arepresented until attainment of objective mastery criteria? Doesit matter if mastered items drop out of the active learning set? We compared adaptive and non-adaptive spacing underconditions of mastery and dropout. Experiment 1 comparedrandom presentation order with no dropout to adaptivespacing and mastery using the ARTS (AdaptiveResponse-time-based Sequencing) system. Adaptive spacingproduced better retention than random presentation.Experiment 2 showed clear learning advantages for adaptivespacing compared to random schedules that also includeddropout. Adaptive spacing performs better than randomschedules of practice, including when learning proceeds tomastery and items drop out when mastered.

Prevalence-induced concept change describes a cognitivemechanism by which someone’s definition of a concept shiftsas the prevalence of exemplars of that concept changes. Forinstance, in a task where people have to judge whether thecolour of an ambiguously-coloured dot is blue or purple, if thefrequency of objectively blue dots in the environmentdecreases, people expand their concept of blueness and judgemore dots to be blue than they did initially. In a series ofexperiments, Levari et al. (2018) demonstrated that thisphenomenon extends to higher-order decision-making, suchas ethical judgments as well. What these findings suggest isthat conceptual spaces (whether it’s about colours or ethicalstatements) in humans are not fixed, but are sensitive tochange. While Levari et al. (2018) established thisphenomenon in young adults, it is unclear how it affects olderadults: do they outsource control and become moresusceptible to concept change or are they rigid enough in theirbeliefs to be resistant to it? In the current study, we explorehow prevalence-induced concept change affects older adults’lower-level, perceptual, and higher- order, ethical,decision-making. We find that older adults are less sensitiveto prevalence-induced concept change than younger adultsacross both domains. A computational model reveals thatthese differences might in part be explained by older adults’tendency to perseverate (repeat responses). Our resultssuggest that older adults’ concept space may be less flexiblethan younger adults’ when faced with a changing world.

Do people perceive shapes to be similar based purely on theirphysical features? Or is visual similarity influenced by top-down knowledge? In the present studies, we demonstrate thattop-down information – in the form of verbal labels that peopleassociate with visual stimuli – predicts visual similarity asmeasured using subjective (Experiment 1) and objective(Experiment 2) tasks. In Experiment 1, shapes that werepreviously calibrated to be (putatively) perceptuallyequidistant were more likely to be grouped together if theyshared a name. In Experiment 2, more nameable shapes wereeasier for participants to discriminate from other images, againcontrolling for their perceptual distance. We discuss what theseresults mean for constructing visual stimuli spaces that areperceptually uniform and discuss theoretical implications ofthe fact that perceptual similarity is sensitive to top-downinformation such as the ease with which an object can benamed.

Thick terms and concepts, such as honesty and cruelty, are atthe heart of a variety of debates in linguistics, philosophy oflanguage, and metaethics. Central to these debates is thequestion of how the descriptive and evaluative components ofthick concepts are related and whether they can be separatedfrom each other. So far, no empirical data on how thick termsare used in ordinary language has been collected to informthese debates. In this paper, we present the first empiricalstudy, designed to investigate whether the evaluativecomponent of thick concepts can be separated. Our study mightbe considered to falsify the view that evaluation isconversationally implicated. However, our study also revealsan effect of valence, indicating that people reason differentlyabout positive and negative thick terms. While evaluationscannot be cancelled for negative thick terms, they can be forpositive ones. Three follow-up studies were conducted toexplain this effect. We conclude that the effect of valence isbest accounted for by a difference in the social norms guidingevaluative language.

Natural languages partition meanings into labelled categoriesin different ways, but this variation is constrained: languagesappear to achieve a near-optimal trade-off between simplicityand informativeness. Across 3 artificial language learning ex-periments, we verify that objectively simpler kinship systemsare easier for human participants to learn, and also show thatthe errors which occur during learning tend to increase sim-plicity while reducing informativeness. This latter result sug-gests that pressures for simplicity and informativeness operatethrough different mechanisms: learning favours simplicity, butthe pressure for informativeness must be enforced elsewhere,e.g. during language use in communicative interaction.

Recent work has shown that people use temporal informationincluding order, delay, and variability to infer causality be-tween events. In this study we build on this work by investi-gating the role of time in dynamic systems, where causes takecontinuous values and also continually influence their effects.Recent studies of learning in these systems explored short in-teractions in a setting with comparatively rapidly evolving dy-namics and modeled people as relying on simpler, resource-limited strategies to grapple with the stream of information(Davis et al., 2020). A natural question that arises from such anaccount is whether interacting with systems that unfold moreslowly might reduce the systematic errors that result from thesestrategies. Paradoxically, we find that slowing the task indeedreduced the frequency of one type of error, but increased the er-ror rate overall. To capture the differences between conditions,we introduce a novel Causal Event Segmentation model basedon the notion that people compress the continuous scenes intoevents and use these to drive structure inference.

Predictive processing, or predictive coding,1 is transforming our knowledge of perception (Knill & Richards, 1996; Rao & Ballard, 1999), the brain (Friston, 2018; Hohwy, 2013; Knill & Pouget, 2004), and embodied cognition (Allen & Friston, 2018; Clark, 2016; Gallagher & Allen, 2018; Seth, 2015). Predictive processing is a hierarchical implementation of empirical Bayes, wherein the cognitive system creates generative models of the world and tests its hypotheses against incoming data. It is hierarchical insofar as the predictions at one level are tested against incoming signals from the lower level. The resulting prediction error, the difference between the expectation and the incoming data, is used to recalibrate the model in a process of prediction error minimization. Predictions may be mediated by pyramidal cells across the neocortex (Bastos et al., 2012; Hawkins & Ahmad, 2016; Shipp et al., 2013). Andy Clark has characterized predictive processing as creating a “bootstrap heaven” (2016, p. 19), enabling the brain to develop complex models of the world from limited data. This enables us to extract patterns from ambiguous signals and establish hypotheses about how the world works. The training signals that we get from the world are, however, biased in all the same unsightly ways that our societies are biased: by race, gender, socioeconomic status, nationality, and sexual orientation. The problem is more than a mere sampling bias. Our societies are replete with prejudice biases that shape the ways we think, act, and perceive. Indeed, a similar problem arises in machine learning applications when they are inadvertently trained on socially biased data (Avery, 2019; N. T. Lee, 2018). The basic principle in operation here is “garbage in, garbage out”: a predictive system that is trained on socially biased data will be systematically biased in those same ways. Unfortunately, we are unwittingly trained on this prejudiced data from our earliest years. As predictive systems, we bootstrap upwards into more complex cognitive processes while being fed prejudiced data, spiraling us into a “bootstrap hell.” This has repercussions for everything from higher-order cognitive processes down to basic perceptual processes. Perceptual racial biases include perceiving greater diversity and nuance in the faces of racial ingroup faces (the cross-race effect; Malpass & Kravitz, 1969), misperceiving actions of racial outgroup members as hostile (Pietraszewski et al., 2014), and empathetically perceiving emotions in racial ingroup (but not outgroup) faces (Xu et al., 2009), among other phenomena. They are particularly worrying due to their recalcitrance to conscious control or implicit bias training. We may be able to veto a prejudiced thought (but see Kelly & Roedder, 2008), but we cannot simply modify our perceptual experience at will. Recalcitrant predictions such as this are “hyperpriors” and are unamenable to rapid, conscious adjustment. I begin with an overview of predictive processing. I explain that the same principles that allow us to bootstrap our way into full cognition also allow for biases to develop. These biases include perceptual racial biases, which are visual and affective rather than cognitive. I explain how sampling biases in infancy and emotion perception contribute to perceptual racial biases (although many other factors certainly play a role). Finally, I hypothesize that traditional implicit bias training may not be enough to disentangle the web of hypotheses that contribute to perceptual racial bias.

Overconfidence and Hindsight Bias are two well-knowncognitive biases. Herein, it is argued these biases may berelated to one another and human memory limitations;specifically, that memory limitations result in hindsight bias,causing people to recall being right more often than theyactually were, which leads to overconfidence as people applythis misremembered confidence to future events. Analysescomparing three types of overconfidence (overestimation,overplacement and overprecision) and hindsight bias confirmstrong, positive correlations between the different types ofoverconfidence – from 0.488 up to .807 and moderatecorrelations (.331 to .398) between all of these and hindsightbias. Comparisons between bias scores and five broadcognitive abilities (from the CHC model) suggests hindsightbias is more pronounced in people with worse memories andgenerally, lower cognitive ability. Overall, results are arguedto support the proposed links between memory, hindsight biasand overconfidence and future directions are suggested.

Science denial is a pressing social problem, contributing toinactivity in the face of climate change, or to a resurgencein outbreaks of preventable diseases. Cognitive factors are asignificant driver of science denial, in addition to social fac-tors such as political ideology. Biases pertaining to judgmentsof contingency (i.e., inferring causal relationships where noneexist) have been associated with misbeliefs, such as belief inthe paranormal and conspiracy theories. Here, we examinewhether contingency biases likewise predict science denial.We show that (a) various tasks used to study relevant biases doin fact load on a single latent ‘contingency illusion’ factor; (b)this contingency illusion bias is associated with increased sci-ence denial; (c) the contingency illusion bias mediates the re-lationship between intuitive (vs. analytic) cognitive style andscience denial; and (d) this holds even when accounting forpolitical ideology.

A longstanding conjecture that has been difficult to test holds that social interactions amplify the effects of people’s biases.We tested this conjecture in a perceptual decision-making paradigm. First, we formalized the algorithmic structure of de-cision making in networked crowds when individuals’ perceptions are biased by their utilities. Our analysis predicts thateven weak cognitive biases can be amplified by social interaction. We tested this prediction in a large networked behav-ioral experiment. Using a monetary incentive structure to induce a bias known as motivated perception, we manipulatedthe presence of a weak cognitive bias in social and asocial populations. Social decision making increased participants’perceptual accuracy relative to an asocial baseline. However, social decision making also led to significantly amplifiedrates of motivated perception, confirming the prediction that shared cognitive biases can be amplified in social networks.

Humans are remarkably adept at abstract rule learning, but little is known about when learners apply this knowledge. We investigated a fundamental constraint in rule generalization: attention to featural similarity (object bias). Across two experiments in different domains, we asked whether adults’ abstract rule generalization is constrained by superficial matches to the concrete exemplars present during learning, as is known to be the case for analogical reasoning (Gentner & Toupin, 1986). In the present studies, participants were exposed to a series of sequences following a simple rule and were asked to generalize to novel instances of either the same rule or a new rule. In one condition, an individual element present during initial learning was inserted into the new, unfamiliar pattern. Results showed that adults often chose this object match over the rule match, suggesting that abstract rule generalization, like analogical reasoning, is impacted by concrete features of the input.

The faces and hands of caregivers and other social partners offer a rich source of social and causal information thatmay be critical for infants cognitive and linguistic development. Previous work using manual annotation strategies andcross-sectional data has found systematic changes in the proportion of faces and hands in the egocentric perspective ofyoung infants. Here, we examine the prevalence of faces and hands in a longitudinal collection of nearly 1700 headcamvideos collected from three children along a span of 6 to 32 months of agethe SAYCam dataset (Sullivan, Mei, Perfors,Wojcik, & Frank, under review). To analyze these naturalistic infant egocentric videos, we first validated the use of amodern convolutional neural network of pose detection (OpenPose) for the detection of faces and hands. We then appliedthis model to the entire dataset, and found a higher proportion of hands in view than previous reported and a moderatedecrease the proportion of faces in childrens view across age. In addition, we found variability in the proportion offaces/hands viewed by different children in different locations (e.g., living room vs. kitchen), suggesting that individualactivity contexts may shape the social information that infants experience.

Do children use objects to infer the people and actions that created them? We ask how children judge whether designswere socially transmitted (copied), asking if children use asimple perceptual heuristic (more similar = more likelycopied), or make a rational, flexible inference (Bayesianinverse planning). We found evidence that children use inverseplanning to reason about artifacts’ designs: When children sawtwo identical designs, they did not always infer copyingoccurred. Instead, similarity was weaker evidence of copyingwhen an alternative explanation ‘explained away’ thesimilarity. Thus, children inferred copying had occurred lessoften when designs were efficient (Exp1, age 7-9; N=52), andwhen there was a constraint that limited the number of possibledesigns (Exp2, age 4-5; N=160). When thinking about artifacts,young children go beyond perceptual features and use a processlike inverse planning to reason about the generative processesinvolved in design.

Psychologists frequently use response time to study cognitive processes, but response time may also be a part of the commonsense psychology that allows us to make inferences about other agents’ mental processes. We present evidence that by age six, children expect that solutions to a complex problem can be produced quickly if already memorized, but not if they need to be solved for the first time. We suggest that children could use response times to evaluate agents’ competence and expertise, as well as to assess the value and relevance of information.

Cognitive interference is a classic cognitive phenomenon:processing one stimulus while ignoring another is morechallenging when the two are related. Recently, andsurprisingly, it has been shown that an individual’s cognitiveinterference can be removed by the people around them. In thepicture-word interference paradigm, participants respond to atarget picture and ignore distractor words. If the words aresemantically related to the target, interference slows responses.We found that this cognitive interference was removed, orsocially offloaded, when participants believed that they wereworking together with another person. In contrast to previousstudies we found it did not matter if the other person workedon the distractor words or on task irrelevant, coloured squares.Furthermore, the time course of this effect suggests that thesocial offloading of semantic interference is underpinned bylate inhibitory mechanisms rather than early distractor filtering.

Group polarization is the widely-observed phenomenonin which the opinions held by members of a small groupbecome more extreme after the group discusses a topic.For example, conservative individuals become even moreconservative, while liberal individuals become even moreliberal. Social psychologists have offered competing ex-planations for this phenomenon. These typically re-quire questionable assumptions about human psychol-ogy. Here, we posit a more parsimonious explanation:the stubbornness of extreme opinions. Using agent-based modeling, we demonstrate that such “stubbornextremism” gives rise to group polarization, as well asother trends observed across the literature on polariza-tion. Our study revealed a further methodological prob-lem for the study of group polarization: reporting opin-ions as categories (e.g. on a Likert scale) inflates theobserved increase in opinion extremity. We concludewith a call for deeper integration of opinion dynamicsmodeling with the cognitive science of communicationand influence.

Recent advances in Deep Learning (DL) and ReinforcementLearning (RL) make it possible to train neural network agentswith raw, first-person visual perception to execute language-like instructions in 3D simulated worlds. Here, we inves-tigate the application of such deep RL agents as cognitivemodels, specifically as models of infant word learning. Wefirst develop a simple neural network-based language learningagent, trained via policy-gradient methods, which can inter-pret single-word instructions in a simulated 3D world. Tak-ing inspiration from experimental paradigms in developmentalpsychology, we run various controlled simulations with the ar-tificial agent, exploring the conditions in which established hu-man biases and learning effects emerge, and propose a novelmethod for visualising and interpreting semantic representa-tions in the agent. The results highlight the potential util-ity, and some limitations, of applying state-of-the-art learningagents and simulated environments to model human cognition.

Intelligent agents coordinate and cooperate flexibly when rules and dynamics of interaction can change over time and across different tasks and environmental conditions. Loose coupling emerges among agents when the rules of interaction are weak enough for agents to act independently or interdependently, and patterns of interaction vary as a function of conditions. Here, we examine collective foraging among simulated agents with and without human intervention. We find that loose coupling among search agents improved group foraging success, and that human players improved performance partly by subtle, indirect effects on group interactions. Analyses of movement patterns showed that loose coupling enabled collections of agents to self-organize and reorganize into a greater diversity of ad hoc groupings.

Collaboration requires agents to coordinate their behavior onthe fly, sometimes cooperating to solve a single task togetherand other times dividing it up into sub-tasks to work on in par-allel. Underlying the human ability to collaborate is theory-of-mind, the ability to infer the hidden mental states that driveothers to act. Here, we develop Bayesian Delegation, a decen-tralized multi-agent learning mechanism with these abilities.Bayesian Delegation enables agents to rapidly infer the hid-den intentions of others by inverse planning. These inferencesenable agents to flexibly decide in the absence of communi-cation when to cooperate on the same sub-task and when towork on different sub-tasks in parallel. We test this model ina suite of multi-agent Markov decision processes inspired bycooking problems. To succeed, agents must coordinate boththeir high-level plans (e.g., what sub-task they should work on)and their low-level actions (e.g., avoiding collisions). BayesianDelegation bridges these two levels and rapidly aligns agents’beliefs about who should work on what. Finally, we testedBayesian Delegation in a behavioral experiment where partici-pants made sub-task inferences from sparse observations of co-operative behavior. Bayesian Delegation outperformed heuris-tic models and was closely aligned with human judgments.

From an early age, humans are capable of learning about theirsocial environment, making predictions of how other agentswill operate and decisions about how they themselves will in-teract. In this work, we address the problem of formalizing thelearning principles underlying these abilities. We construct a cu-rious neural agent that can efficiently learn predictive models ofsocial environments that are rich with external agents inspiredby real-world animate behaviors such as peekaboo, chasing,and mimicry. Our curious neural agent consists of a controllerdriven by γ-Progress, a scalable and effective curiosity signal,and a disentangled world model that allocates separate networksfor interdependent components of the world. We show that ourdisentangled curiosity-driven agent achieves higher learning ef-ficiency and prediction performance than strong baselines. Cru-cially, we find that a preference for animate attention emergesnaturally in our model, and is a key driver of performance. Fi-nally we discuss future directions including applications of ourframework to modeling human behavior and designing earlyindicators for developmental variability.

Humans live in an uncertain world and often rely on socialinformation in order to reduce uncertainty. However, therelationship between uncertainty and social information use is notyet fully understood. In this work we argue that previous studieshave often neglected different degrees of uncertainty that need tobe accounted for when studying social information use. Weintroduce a novel experimental paradigm to measure risky decisionmaking, wherein social information and uncertainty aremanipulated. We also developed a Bayesian model of socialinformation use. We show that across different levels ofuncertainty; social influence follows similar principles. Socialinformation is more impactful when individuals are moreuncertain. Notably, this relationship holds for experimentalmanipulations of uncertainty but also for subjective uncertaintywithin experimental conditions. We conclude with discussing thatsocial influence can be better understood when paying credit tosubjective uncertainties and preferences.

One of the major ways that people engage in adaptive problemsolving is by copying the solutions of others. Most of the workon this field has focused on three questions: when to copy, whoto copy from, and what to copy. However, how much to copyhas been relatively less explored. In the current research, weare interested in the consequences for a group when its mem-bers engage in social learning strategies with different tenden-cies to copy entire or partial solutions and different complex-ities of search problems. We also consider different networktopologies that affect the solutions visible to each member.Using a computational model of collective problem solving,we demonstrate that strategies where social learning involvespartial copying outperform strategies where individuals copyentire solutions. We analyze the exploration/exploitation dy-namics of these social learning strategies under the differentconditions.

Social stereotypes impact individuals’ judgement about different social groups. One area where such stereotyping has acritical impact is in hate speech detection, in which human annotations of text are used to train machine learning models.Such models are likely to be biased in the same ways that humans are biased in their judgments of social groups. Inthis research, we investigate the effect of stereotypes of social groups on the performance of expert annotators in a largecorpus of annotated hate speech. We also examine the effect of these stereotypes on unintended bias of hate speechclassifiers. To this end, we show how language-encoded stereotypes, associated with social groups, lead to disagreementsin identifying hate speech. Lastly, we analyze how inconsistencies in annotations propagate to a supervised classifier whenhuman-generated labels are used to train a hate speech detection model.

Though extreme events, riots are key signals of well-being in societies. Without understanding the psychological mecha-nisms behind them, however, it is difficult to discern the social changes that need to be made in order to reduce both theriots themselves and their underlying causes. In this work we use computational models to test both relative deprivationand social identity as explanations for data from a novel experimental framework, Parklife, which provides data on howand when individuals riot in the laboratory. Our models show that whilst norm formation and distinctiveness are importantfactors in explaining the behaviour of participants in Parklife, relative deprivation is a key and necessary mechanism inthe increase in anti-social behaviour observed in disadvantaged groups. This work offers the first direct test of relativedeprivation within a specialised system, and shows the power of computational simulations in connecting theories withdata, helping us to test hypotheses.

For language-capable interactive robots to be effectively in-troduced into human society, they must be able to naturallyand efficiently communicate about the objects, locations, andpeople found in human environments. An important aspect ofnatural language communication is the use of pronouns. Ac-cording to the linguistic theory of the Givenness Hierarchy(GH), humans use pronouns due to implicit assumptions aboutthe cognitive statuses their referents have in the minds of theirconversational partners. In previous work, Williams et al. pre-sented the first computational implementation of the full GHfor the purpose of robot language understanding, leveraging aset of rules informed by the GH literature. However, that ap-proach was designed specifically for language understanding,oriented around GH-inspired memory structures used to assesswhat entities are candidate referents given a particular cogni-tive status. In contrast, language generation requires a modelin which cognitive status can be assessed for a given entity.We present and compare two such models of cognitive sta-tus: a rule-based Finite State Machine model directly informedby the GH literature and a Cognitive Status Filter designedto more flexibly handle uncertainty. The models are demon-strated and evaluated using a silver-standard English subset ofthe OFAI Multimodal Task Description Corpus.

Humans readily generalize, applying prior knowledge to novelsituations and stimuli. Advances in machine learning have be-gun to approximate and even surpass human performance, butthese systems struggle to generalize what they have learnedto untrained situations. We present a model based on well-established neurocomputational principles that demonstrateshuman-level generalisation. This model is trained to play onevideo game (Breakout) and performs one-shot generalisationto a new game (Pong) with different characteristics. The modelgeneralizes because it learns structured representations that arefunctionally symbolic (viz., a role-filler binding calculus) fromunstructured training data. It does so without feedback, andwithout requiring that structured representations are specifieda priori. Specifically, the model uses neural co-activation todiscover which characteristics of the input are invariant and tolearn relational predicates, and oscillatory regularities in net-work firing to bind predicates to arguments. To our knowledge,this is the first demonstration of human-like generalisation ina machine system that does not assume structured representa-tions to begin with.

The cognitive control of action is thought to be mediated byconscious effort as reflected by changes in frontal theta activity. Wemeasured frontal theta during a response inhibition task in 16healthy adults who implicitly learned repeated patterns of go/switchcues, resulting in unaware differences in cognitive demand fordifferent cues. Learning was reflected by reduced reaction times(RT) to probable compared to unexpected switch cues. In the rareabsence of behavioural (RT) differences, concurrent measures ofpupil diameter revealed changes in effort with stimulus probability,while effort was accompanied by parametric increases intheta. Additionally, theta predicted pre-response sensorimotorgamma, suggesting interactions between frontal and sensorimotorcortex during cognitive control. These results provide furtherevidence for a functional role of theta in cognitive effort duringresponse preparation, inhibition and execution, even in the absenceof conscious awareness.

In this work, we investigate how two factors, metacognitiveskills and motivation, would impact student learning acrossdomains. More specifically, our primary goal is to identify thecritical, yet robust, interaction patterns of these two factors thatwould contribute to students’ performance in learning logicfirst and then their performance on a subsequent new domain,probability. We are concerned with two types of metacognitiveskills: strategy-awareness and time-awareness, that is, whichproblem-solving strategy to use and when to use it. Our datawere collected from 495 participants across three consecutivesemesters, and our results show that the only students who con-sistently outperform their peers across both domains are thosewho are not only highly motivated but also strategy-aware andtime-aware.

Designing artificial agents that can closely imitate human behavior, might influence humans in perceiving them as intentional agents. Nonetheless, the factors that are crucial for an artificial agent to be perceived as an animated and anthropomorphic being still need to be addressed. In the current study, we investigated some of the factors that might affect the perception of a robot's behavior as human-like or intentional. To meet this aim, seventy-nine participants were exposed to two different behaviors of a humanoid robot under two different instructions. Before the experiment, participants' biases towards robotics as well as their personality traits were assessed. Our results suggest that participants’ sensitivity to human-likeness relies more on their expectations rather than on perceptual cues.

Play is a universal behavior widely held to be critical for learning and development. Recent studies suggest children’sexploratory play is consistent with formal accounts of learning. This ”play as rational exploration” view suggests thatchildren’s play is sensitive to costs, rewards, and expected information gain. By contrast, here we suggest that a definingfeature of human play is that children subvert normal utility functions in play, setting up problems where they incurneedless costs to achieve arbitrary rewards. Across three studies, we show that 4-5-year-old children not only infer playfulbehavior from observed violations of rational action (Experiment 1), but themselves take on unnecessary costs and performinefficient actions during play, despite acting efficiently in non-playful, instrumental contexts (Experiments 2-3). We endby discussing the value of apparently utility-violating behavior and why it might serve learning in the long run.

A great deal of work argues that people demand impartial,evidence-based reasoning from others. However, recentfindings show that moral values occupy a cardinal position inpeople’s evaluation of others, raising the possibility that peoplesometimes prescribe morally-good but evidentially-poorbeliefs. We report two studies investigating how peopleevaluate beliefs when these two ideals conflict and find thatpeople regularly endorse motivated reasoning when it can bemorally justified. Furthermore, we document two ways thatmoral considerations result in prescribed motivated reasoning.First, morality can provide an alternative justification forbelief, leading people to prescribe evidentially unsupportedbeliefs to others. And, second, morality can affect how peopleevaluate the way evidence is weighed by lowering or raisingthe threshold of required evidence for morally good and badbeliefs, respectively. These results illuminate longstandingquestions about the nature of motivated reasoning and thesocial regulation of belief.

Multiple cognitive theories make conflicting explainations forhuman reasoning on syllogistic problems. The evaluation andcomparison of these theories can be performed by conceiv-ing them as predictive models. Model evaluation often em-ploys static sets of predictions rather than full implementationsof the theories. However, most theories predict different re-sponses depending on the state of their internal parameters.Disregarding the theories’ capabilities to adapt parameters todifferent reasoners leads to an incomplete picture of their pre-dictive power. This article provides parameterized algorithmicformalizations and implementations of some syllogistic theo-ries regarding the syllogistic single-response task. Evaluationsreveal a substantial improvement for most cognitive theoriesbeing made adaptive over their original static predictions. Thebest performing implementations are PHM, mReasoner andVerbal Models, which almost reach the MFA benchmark. Theresults show that there exist heuristic and model-based theo-ries which are able to capture a large portion of the patterns insyllogistic reasoning data.

We explore whether children’s strategies on a causal learningtask show a bias observed in adults towards “exploitative” hy-pothesis revision. Adults and children (ages 4–6) were pre-sented with evidence which initially seemed to conform to asimple, salient rule (e.g. blue blocks activate a machine), butthen encountered evidence that violated this rule. The truerule in the “near” condition was more complex, but could bereached through iterative revision of the salient rule, while inthe “distant” condition, the true rule was comparatively sim-ple, but incremental revision could not yield the true rule. Par-ticipants then predicted the behaviour of a set of new blocks.Adults performed better in the near condition, while in the dis-tant condition adults did not appear to revise their initial hy-pothesis significantly. Unlike adults, children’s overall perfor-mance in both conditions was similar, while condition differ-ences may reflect a broader search for alternative solutions.

Conditionals pervade every aspect of our thinking, from themundane and everyday such as ‘if you eat too much cheese,you will have nightmares’ to the most fundamental concernsas in ‘if global warming isn’t halted, sea levels will rise dra-matically’. Many decades of research have focussed on thesemantics of conditionals and how people reason from condi-tionals in everyday life. Here it has been rather overlookedhow we come to such conditionals in the first place. In manycases, they are learned through testimony: someone warns usabout the ill-effects of cheese. Any full account of the condi-tional must consequently incorporate such learning. Here, weprovide a new formal account of belief change in response to atestimonial conditional.

Our beliefs change with learning, and much of what we learncomes from the testimony of other people. How much our be-liefs change may depend on how many people are the sourcesof a given piece of information, and how reliable their expertisemakes them. It is not clear, however, what exactly the effectsof reliability or number of speakers will be when the testimonyhas the form of an indicative conditional. Here, we test the hy-pothesis that learning a conditional amounts to increasing thedegree to which the antecedent of that conditional is relevantfor its consequent. Furthermore, we investigate whether this isaffected by number of speakers and by their expertise.

A hallmark of human language is the ability to effectively andefficiently convey contextually relevant information. One the-ory for how humans reason about language is presented in theRational Speech Acts (RSA) framework, which captures prag-matic phenomena via a process of recursive social reasoning(Goodman & Frank, 2016). However, RSA represents idealreasoning in an unconstrained setting. We explore the idea thatspeakers might learn to amortize the cost of RSA computationover time by directly optimizing for successful communicationwith an internal listener model. In simulations with groundedneural speakers and listeners across two communication gamedatasets representing synthetic and human-generated data, wefind that our amortized model is able to quickly generate lan-guage that is effective and concise across a range of contexts,without the need for explicit pragmatic reasoning.

Many theories of communication claim that perspective-taking is afundamental component of the successful design of utterances for aspecific audience. We investigated perspective-taking in aconstrained communication situation: Participants played a wordguessing game where each trial required them to communicate atarget word without context. In each game, pairs of participants tookturns giving and receiving clues to guess target words, bothreceiving feedback after each trial. In Experiment 1, none of themeasures of participants’ performance improved over rounds,suggesting either that participants were unable to improve theirperspective-taking or that the task was simply too demanding forother reasons. In Experiment 2, we tested whether this lack ofimprovement was due to overall difficulty rather than inability totake perspective. While the success rate in Experiment 2 didimprove over the course of the game, our analyses indicated that theimprovement was due to participants discovering a frequencyheuristic (using rarer clue words) rather than improved perspective-taking per se. The results of these two experiments show thatimproving perspective-taking adaptively is very difficult when thereis no context to ground either signal choice or interpretation.

Studies suggest that people use the least possible effort to gen-erate natural language descriptions of sets of objects. Thismeans that they base descriptions on what is perceptually avail-able to them. For instance, people can subitize, i.e., rapidlyassess the exact quantity of small numbers of objects, so whenthe quantity of objects in the visual scene is beneath this thresh-old, they give numeric descriptions; when the quantity is abovethis threshold, they generate non-numeric descriptions. How-ever, no research examines how people describe visual scenesof items in groups. As such, it is unclear how people will formdescriptions of scenes that contain a large total number of itemsin groups. We report on a novel experiment designed to in-vestigate how people produce quantified descriptions of scenescomposed of salient visual groups. The results corroborate theleast effort hypothesis, and suggest that people’s incrementalperception of quantity drives their descriptions.

In this study, we investigate people’s ability to predict andadapt to the behavior of others in order to make plans of theirown, a cornerstone of cooperative and competitive behavior.Participants played 300 rounds of rock, paper, scissors againstanother human player. We investigate the degree to which par-ticipants are able to identify patterns in their opponent’s be-havior in order to exploit them in subsequent rounds. We findstrong evidence that participants exploit their opponents overthe course of 300 rounds, suggesting that people identify de-pendencies in their opponent’s move choices during the game.Nonetheless, analysis of dependencies across participant movechoices reveals that people exhibit a number of regularities intheir own moves. Based on these dependencies, we argue thatparticipants are far from optimal in their exploiting, suggestingthat there are substantial constraints on people’s ability to iden-tify and adapt to patterned opponent behavior across repeatedinteractions.

Adults readily form pacts, or temporary agreements about ref-erent names, over the course of conversation. Young childrenfail to do so with peers, but recent evidence suggests that ex-plicit feedback from adults may improve their performance(Matthews, Lieven, & Tomasello, 2007). Do parents natu-rally provide such structure in their conversations with chil-dren? Using a director-matcher paradigm, we first show thatparents and children (ages 4, 6, 8) converge on increasinglyaccurate and efficient conversational pacts. Further, parents ofyounger children provide more interactive feedback. Finally,we analyze asymmetries in parents’ and children’s contribu-tions, finding that pacts tend to originate with the parent, butare simplified by younger children. Together, these resultssupport the idea that parents sensitively adapt their languageto their children’s developmental level to scaffold successfulcommunication.

Human infants have the remarkable ability to learn the asso-ciations between object names and visual objects from inher-ently ambiguous experiences. Researchers in cognitive scienceand developmental psychology have built formal models thatimplement in-principle learning algorithms, and then used pre-selected and pre-cleaned datasets to test the abilities of the mod-els to find statistical regularities in the input data. In contrast toprevious modeling approaches, the present study used egocen-tric video and gaze data collected from infant learners duringnatural toy play with their parents. This allowed us to capturethe learning environment from the perspective of the learner’sown point of view. We then used a Convolutional Neural Net-work (CNN) model to process sensory data from the infant’spoint of view and learn name-object associations from scratch.As the first model that takes raw egocentric video to simulateinfant word learning, the present study provides a proof of prin-ciple that the problem of early word learning can be solved,using actual visual data perceived by infant learners. More-over, we conducted simulation experiments to systematicallydetermine how visual, perceptual, and attentional properties ofinfants’ sensory experiences may affect word learning.

Statistical learning is an active process wherein information isactively selected from the learning environment. As currentinformation is integrated with existing knowledge, it shapesattention in subsequent learning, placing biases on which newinformation will be sampled. One statistical learning task thathas been studied recently is cross-situational word learning(CSL). In CSL, statistical learners are able to learn the cor-rect mappings between novel visual objects and spoken labelsafter watching sequences where the two are paired togetherin referentially ambiguous contexts. In the present paper, weuse a computational method called Tensor Component Analy-sis (TCA) to analyze real-time gaze data collected from a set ofCSL studies. We applied TCA to learners’ gaze data in orderto derive latent variables related to real-time statistical learningand to examine how selective attention is organized in time.Our method allows us to address two specific questions: a) thesimilarity in attention behavior across strong vs. weak learn-ers as well as across learned vs. not-learned items and b) howthe structure of attention relates to word learning. We mea-sured learners’ knowledge of label-object pairs at the end of atraining session, and show that their real-time gaze data can beused to predict item-level learning outcomes as well as decodepretrained item knowledge.

This study investigated how caregivers modulate their speaking rate according to children’s lexical knowledge and the context of the interaction, and how such adjustments affect children’s word learning. We studied a semi- naturalistic corpus where caregivers talked about different toys with their 3-4 years old children. The toys were known or unknown to the child, and present or absent from the environment. We found that caregivers talked about unknown toys with a slower speaking rate than known ones. When toys were absent, caregivers also tended to slow down for the toy’s name, although they produced the whole utterance faster. Crucially, the results of a subsequent recognition task for children showed that caregivers’ greater adjustment in speaking rate between known and unknown words predicted better immediate learning. Our findings suggest that caregivers modify their speaking rate in a helpful manner when the situation is more demanding, which assists children in word learnin

Models of cross-situational word learning typically character-ize the learner as a passive observer, but a language learn-ing child can actively participate in verbal and non-verbalcommunication. We present a computational study of cross-situational word learning to investigate whether a curious wordlearner who actively influences linguistic input in each contexthas an advantage over a passive learner. Our computationalmodel learns to map words to objects in real images by self-supervision through simulating both word comprehension andproduction. We examine different curiosity measures as guid-ing input selection, and analyze the relative impact of eachmethod. Our results suggest that active learning leads to higheroverall performance, and a formulation of curiosity which re-lies both on subjective novelty and plasticity yields the bestperformance and learning stability.

Vocabulary growth and syntactic development are known tobe highly correlated in early child language. What determineswhen words are acquired and how can this help us understandwhat drives early language development? We train an LSTMlanguage model, known to detect syntactic regularities that arerelevant for predicting the difficulty of words, on child-directedspeech. We use the average surprisal of words for the model,which encodes sequential predictability, as a predictor for theage of acquisition of words in early child language. We com-pare this predictor to word frequency and others and find thataverage surprisal is a good predictor for the age of acquisitionof function words and predicates beyond frequency, but notfor nouns. Our approach provides insight into what makes agood model of early word learning, especially for words whosemeanings rely heavily on linguistic context.

Most work on attention (in terms of both psychophysical experiments and computational modeling) involves selection instatic scenes. And even when dynamic displays are used, performance is still typically characterized with only a singlevariable (such as the number of items correctly tracked in Multiple Object Tracking; MOT). But the allocation of attentionin daily life (e.g. during foraging, navigation, or play) involves both objective performance and subjective effort, and canvary dramatically from moment to moment. Here we attempt to capture this sort of rich temporal ebb and flow of attentionin a novel and generalizable adaptive computation architecture. In this architecture, computing resources are dynamicallyallocated to perform partial belief updates over both objects (in space) and moments (in time) flexibly and according totask demands. During MOT this framework is able to explain both objective tracking performance and the subjective senseof trial-by-trial effort.

Foraging over land for resources was central to the evolutionof search processes and decision-making for many organisms,including humans. The processes underlying natural foragingbehaviors are foundational to cognition. However, in the field,it is difficult to collect detailed and accurate measures of searchbehaviors and hard to manipulate search conditions. We usedGoogle Earth and the Unity 3D platform to recreate a patchof the Himalayan foothills with ancient temples used as way-points for travelers on foot. Two hundred players recruited viaMTurk moved over the landscape with realistic speed, energyusage, and perceptual conditions to find as many temples aspossible given a limited energy budget. Half were constrainedby the need to return to a home base to report found temples,and half were not. When search paths were analyzed in termsof segment distributions, players who found relatively moretemples (high scorers) more closely followed the theoreticallyoptimal L ́evy walk that balances exploration and exploitation,regardless of the home base. This intrinsic pattern was alsofound in perceptual search intervals, with high scorers lean-ing more towards exploration. By contrast, when search pathswere analyzed as wholes, an extrinsic pattern was found in thatplayers ranged farther without a home base, and this differ-ence was more pronounced for high scorers. We conclude thatL ́evy-like patterns are intrinsic and effective in terms of pathsegments and perceptual intervals, but overall search behavioradapts to extrinsic factors and constraints.

Humans are profoundly social creatures. We depend on others for survival and crave social interactions. Faces are thegateway to many typical social interactions. One of the most replicable results in cognitive neuroscience is the selectiveresponse of some cortical regions to faces in humans and other social primates. Specifically, the fusiform face area (FFA)is a region in the ventral temporal cortex (VTC) that is selectively responsive to faces. To determine whether infantsshow early cortical responses to faces, we recruited 86 human infants (2.1-11.9 months) to participate in an awake infantfunctional resonance imaging (fMRI) experiment. We obtained usable fMRI data from 49 infants (2.1-9.7 months) whilethey watched videos of faces, bodies, objects, and scenes, 30 of whom (2.5-9.4 months) had enough data for a functionalregion of interest (fROI) analysis. A group random effects analysis revealed significantly higher responses to faces thanobjects in the medial prefrontal cortex (MPFC), superior temporal sulcus (STS), ventral temporal cortex (VTC), andsubcortical areas of the infant brain. Additionally, the fROI analysis revealed face selective responses in the STS, MPFC,and VTC but not lateral occipital cortex or subcortical areas of the infant brain. Thus, we provide the first evidence offace selective responses in the infant brain and demonstrate that social regions (STS and MPFC) respond selectively at thesame time as perceptual regions (VTC).

Are gender associations in general language reflected in thewords spoken to and by children? Previous work has sug-gested that language reveals gender differences in discourse,speech style, language use and acquisition. Work in artificialintelligence has shown that word embeddings trained on largecorpora reflect human gender associations. We connect thiswork to developmental psychology by exploring whether gen-der associations in word embeddings are present in the linguis-tic input and output of children, and if so, how early genderedlanguage emerges. We present a computational method thatquantifies the gender associations of words and use a corpus ofchild-caretaker speech to show that these gender associationscorrelate significantly with those in word embeddings. Wediscover that gendered word use emerges in English-speakingchildren around age 2, and the gender associations cannotbe explained solely by variables including word length, fre-quency, concreteness, and valence.

The mind is a prediction machine, using prior experiences and current information to constantly make predictions aboutthe future. This feature of the cognitive system has numerous consequences for long-term memory. Here, we are interestedin the effects these predictions have on memory when invalidated (i.e., prediction errors) during social interactions witheither similar or dissimilar social sources. We designed an experiment in which both similar and dissimilar social sources(speakers) recounted experiences similar with those of the listeners but with a different outcome than those of the listeners.We measured participants memory for both their own and the two social sources experiences. In two experiments, we foundthat context-based prediction errors triggered during social interactions dont affect the listeners memory for their ownexperiences but decrease the listeners memory for the similar speakers experiences compared to the dissimilar speakersexperiences. This finding has important implications for close relationships.

There has been a debate on the process of insight problem solving. The special-process view posits that insight problemsolving processes are implicit unlike non-insight problem solving. The business-as-usual view, on the other hand, assumesthat the same processes as non-insight problem solving are involved in insight problem solving. To reconcile them, we canrely on the evidence on the verbal overshadowing effect on insight problem solving. However, there is a methodologicalproblem on how to determine whether the verbal overshadowing effect has emerged. The purpose of the present studywas to solve the problem using Bayes Factors. We reanalyzed the data presented in the previous studies examining theeffects of verbalization on insight problem solving. The results showed that some studies inappropriately concluded thatthe verbal overshadowing effect was not obtained. We also discussed possible moderating variables of the effect.

Some people experience sequences like numbers allocated to a specific part of space which is well-known as sequencespace synesthesia. On the other hand, covert attention is orienting of attention without the head, eyes, or body movementis the (mental) moving of attention toward a stimulus. Here, We used previous findings in sequence space synesthesia byusing an auditory number sequence numbers and covert spatial attention together with cognitive grammar theory includingprofiling to assess the possibility to shift covert attention towards a specific part of a bistable picture. Our participants were14 years old adolescents learning English at the pre-intermediate level in a school in Tehran which went through within-subject experiment. Results showed shorter reaction time for a sentence with trajectory congruent with covert attended partof the bistable picture compared to the condition without such attentional shift by t value as -4.466 within 95% confidenceinterval.

Humans possess a remarkable capacity to create and understand abstract representations, such as pictures. U.S. 18-month-olds understand that pictures refer to objects; however, less is known about how this understanding develops. We testthe hypothesis that understanding the representational nature of pictures requires frequent experience with pictures, byworking with rural Kenyan toddlers with few visual symbols in their early environments.We taught rural Kenyan toddlers a novel word (dax) for a picture of a novel object. We then presented the picture and theobject to toddlers and asked them to point to the dax, reasoning that toddlers would select the object if they understoodthat pictures are representations of objects.Surprisingly, only half the sample learned the novel word. Moreover, the toddlers who learned the word selected be-tween the picture and object randomly. We discuss follow-up studies to continue exploring the development of pictorialcompetence across early environments.

Recent advances in Artificial Intelligence have produced agents that can beat human world champions at games like Go,Starcraft, and Dota2. However, most of these models do not seem to play in a human-like manner: People infer others’intentions from their behaviour, and use these inferences in scheming and strategizing. Here, using a Bayesian Theory ofMind (ToM) approach, we investigated how much an explicit representation of others’ intentions improves performancein a cooperative game. We compared the performance of humans playing with optimal-planning agents with and withoutToM, in a cooperative game where players have to flexibly cooperate to achieve joint goals. We find that teams with ToMagents significantly outperform non-ToM agents when collaborating with all types of partners: non-ToM, ToM, as well ashuman players, and that the benefit of ToM increases the more ToM agents there are. These findings have implications fordesigning better cooperative agents.

Some theories propose that human cumulative culture is dependent on System 2 cognitive processes. We aimed to restrictaccess to adults executive functions via a dual-task paradigm, to assess whether this reduced their ability to improveupon information provided by a computer model. 206 participants completed a grid-search task in conjunction with aworking-memory task and a matched control, with the aim of outperforming an example attempt observed vicariously,presented on the computer. Participants behaviour was then used to simulate the outcome if the task was iterated overmultiple generations. Simulations run using the data showed that, across all conditions, participant behaviour would leadto cumulatively increasing scores over successive generations. However, scores plateaued without reaching the maximum.Overall, the task did not provide clear evidence that working-memory directly facilitates cumulative cultural evolution.However, differences between conditions may have been masked by offloading task demands to the concurrent working-memory task.

To learn from others, children rely on cues (e.g., familiarity) toinfer who will provide useful information. We extend thisresearch to ask whether children will use an informant’sinclination to gesture as a marker of whether they are a goodperson to learn from. Children (N=459, ages 4-12 years)watched videos in which actresses made statementsaccompanied by meaningful iconic gestures, beat gestures, orno gestures. After each trial, children were asked “Who do youthink would be a good teacher?” (good teacher- experimentalcondition) or “Who do you think would be a good friend?”(good friend-control condition). Results show children dobelieve that someone who produces iconic gesture would makea good teacher over someone who does not, but this is only laterin childhood and only if a child has the propensity to seegesture as meaningful. The same effects were not found in thegood-friend condition.

In a Linear Associative Net (LAN), all input settles to a singlepattern, therefore Anderson, Silverstein, Ritz, and Jones (1977)introduced saturation to force the system to reach othersteady-states in the Brain-State-in-a-Box (BSB). Unfortunately,the BSB is limited in its ability to generalize because itsresponses are restricted to previously stored patterns. We presentsimulations showing how a Dynamic-Eigen-Net (DEN), a LANwith Short-Term Plasticity (STP), overcomes thesingle-response limitation. Critically, a DEN also accommodatesnovel patterns by aligning them with encoded structure. We traina two-slot DEN on a text corpus, and provide an account oflexical decision and judgement-of-grammaticality (JOG) tasksshowing how grammatical bi-grams yield stronger responsesrelative to ungrammatical bi-grams. Finally, we present asimulation showing how a DEN is sensitive to syntacticviolations introduced in novel bi-grams. We propose DENs asassociative nets with greater promise for generalization than theclassic alternatives.

Children benefit from comparing events when learning verbs, but it is unclear whether variability across events is helpfulor harmful. Additionally, no prior study has tested childrens visual attention to specific objects under different variabilityconditions. A Tobii x30-120 tracked 21/2-year-olds(n=36) and 31/2-year-olds(n=34) visual attention as they watchedevents that showed no change (control), events with varied tools (Tool condition) or events with varied affected objects(Affected Object condition) when learning a verb. Children pointed to one of two new events at test; repeated for two moreverbs. Results showed children could extend the verbs, but were more successful with age. Analyses of looking patternsin the learning phase show childrens attention to specific objects varied by condition, and that reduced looking to the toolwas linked to less success at test. Results are important to better understand processes that underlie verb learning, andlanguage development as a whole.

Perceivers view facial configurations as belonging to emotion categories, though the features of facial cues to emotionvary continuously. Little is understood about what factors beyond facial musculature influence these categorizations. Weinvestigated how an emoters gender influences how emotional cues are perceived. Eighty-four adults categorized morphedemotional faces of male and female models sampled from a neutral-angry continuum. Participants had a lower thresholdfor categorizing female faces as upset (X2=16.618, p¡.001), particularly for configurations that were closer to the angryend of the continuum. Even when provided explicit feedback on their responses, participants continued to be more likelyto identify a face as angry for female, as compared to male, models (X2=11.561, p¡.001). Therefore, judgments of emotionwere influenced by both the emotional cues displayed by a model and also the models identity. These results highlighthow the social context influences how individuals readand therefore respond toanger.

Human cognition is routinely challenged by today’s multitasking demands which require continuous attentional deployment to multiple task components in parallel. While practice-based multitasking training has been shown to improve multitasking performance, little is known about how attention should be best deployed for optimal training. To this end, we leveraged a large-scale dataset from an online cognitive-training platform to investigate individual differences in task learning across long-term training. We developed an index of attentional deployment that specifies the temporal dynamics of learning for each component of the multitask and calculate distance maps between clusters of users to specify distinct learning styles. While long-term practice improved the multitasking performance of all participant groups, participants who focused on learning one task component earlier and more emphatically, benefited from superior learning gains throughout the entirety of training.

What makes an explanation satisfying? Much work hasinvestigated explanatory preferences for things like animalsand artifacts, but how do explanation preferences manifest ineveryday life? Here, we focus on the criminal justice system asa case study. In this domain, outcomes critically depend on howmembers of the system (e.g., lawyers, jurors) generate andinterpret explanations. We investigate lay preferences for twodifferent classes of explanations: those that appeal to‘mechanistic’ aspects of a crime (i.e., how the culpritcommitted the crime) vs. ‘teleological’ aspects of that crime(i.e., the purpose of the crime). In two experiments, wedemonstrate that people have a systematic preference for'motive' accounts of crimes (analogous to a teleologypreference) at different stages of the investigative process. Wediscuss these findings in light of a broad literature on thecognitive basis of explanation preferences. We also discussimplications for the criminal justice system.

Sustaining focus is difficult, but it is under our control.Previous research has found that people’s ability to sustainattention depends on external incentives and changes overthe lifespan. However, previous research has made limitedprogress in characterizing the specific cognitive mechanismsinvolved in sustained attention. These mechanisms areinvestigated in the current experiment, which uses driftdiffusion modeling to re-analyze a series experiments onsustained attention. In Experiment 1, we found that incentivesinfluence information processing (noise) but not decisionstrategy (threshold). In Experiment 2, we found that noiseand threshold have distinct development trajectories, andthat while older adults have noisier accumulation, they arebetter at sustaining attention. These results help providemechanistic insight into recent findings in sustained attention.

The current study examined the role of working memory in language activation during visual processing. Twenty-sixnative English speakers searched for a visual target while completing a concurrent linguistic memory task, a concurrentspatial memory task, or in the absence of dual-task demands. Linguistic activation was measured by comparing visualfixations to phonologically-overlapping items and control items whose names did not overlap with the target. Participantsexperienced significant phonological competition across all conditions, but memory load impacted the timing of competitorco-activation (delayed and more sustained under spatial load), as well as the magnitude (attenuated under both spatial andlinguistic loads) compared to the no-load condition. We conclude that linguistic representations are accessed duringvisual search even with concurrent cognitive loads, but that working memory influences the degree of language-basedcompetition, possibly by modulating the activation and maintenance of linguistic and spatial information.

Extensive statistical learning literature suggests that regularities between co-occurring items can be learned implicitly.However, little is known whether higher-order statistics, such as paradigmatic relations, can be learned implicitly. Paradig-matic relations link items that may not co-occur but share each others patterns of co-occurrence. For example, A and Bare paradigmatically related when they both co-occur with C (e.g., A-C, B-C). Therefore, paradigmatic relations could,in principle, be implicitly learned through co-occurrence regularities. Here, we modified a contextual cueing task, wheresome of the targets independently appeared within distractors that had the same spatial configuration. We found that onlyparticipants who noticed the repeating distractor patterns tend to learn the paradigmatic relations, while most participantswere able to learn the simple co-occurring regularities. Our findings imply that the ability to learn simple co-occurrenceregularities is not sufficient for forming paradigmatic relations and that explicit attention may be critical.

The impact of play on childrens cognitive skills has gained interest lately. This study examines the efficacy of a pre-tend play intervention on the self-regulation and language skills of four- to five-year-olds with English as an additionallanguage. During pretend play an individual uses ones imagination to project a mental representation onto reality. Thesample included 151 children who were randomized into three groups: (a) Pretend play; (b) Art activities; (c) Typicalcurriculum. The intervention included sixteen 30-minute sessions in groups of six children. The design of the pretendplay intervention is based on storybook reading with an adult, and subsequent role-playing with props related to the story.During storybook reading explicit phonological awareness and vocabulary instruction were provided for the target words.In terms of the results, the children in the pretend play group had significantly higher post-test phonological awarenessscores than children who were exposed to typical curriculum.

A requirement definition document (RDD) in software development should define the necessary and sufficient conditionfor the software to satisfy. It is preferable to review and guarantee the quality of the RDD. It is, however, not easyto evaluate the goodness of the reviewer, due to various review styles and the logical complexity of such a document.Therefore, we developed a test set for the review task of the RDD and investigated the reviewers gaze behavior. The testset includes the four logical relationships between the definition and instances, and our analysis revealed that validation ofthe necessary condition is relatively easier than validation of the sufficient condition. Moreover, reviewers gaze patternswere concentrated more on a certain part of sentences when the review was successful. It may suggest that the reviewsuccess can be predicted by the reviewing eye gaze fixations on sentences with the relatively higher information gain.

In the present study, we tested the effects of anodal tDCS deliveredover the Fp3 (for 10mins at 1.5mA) on the face inversion effect(better recognition for upright vs inverted faces) while participantsperformed an old/new recognition task. We recruited three groupsof participants (n=72) and randomly assigned them toexperimental conditions. In the anodal Study Phase conditionparticipants received the tDCS stimulation during the learningphase only. In the anodal Recognition Phase condition,participants received the anodal stimulation during the recognitiontask only. In the control group participants received shamstimulation (during the study or recognition phase). Consistentwith previous research, the results showed that anodal stimulationreduced the inversion effect by impairing recognition of uprightfaces. Critically, in both anodal conditions the inversion effect wassignificantly reduced compared to sham, and no difference wasfound between the two anodal conditions. Upright faces in eachanodal condition were recognized significantly worse than sham.This suggests that the tDCS-induced effects on face recognitionare immediate and affect both learning and performance. Weinterpret the results based on the account of perceptual learningand previous work on tDCS and the inversion effect.

Category learning is a fundamental process in human cognition. Recent efforts have attempted to adapt theories developedin vision to the auditory domain. However, no study has directly compared auditory and visual category learning in thesame individuals. Using a fully within-subjects approach, we trained participants on non-speech auditory, visual, andnon-native speech categories in a single day. By comparing category learning behavior, the ability to generalize to novelcategory exemplars, and leveraging decision bound computational models, we found that while individuals demonstratedsimilar learning across the auditory and visual modalities, there were distinct perceptual biases that influenced learning ofnon-speech auditory categories. Further, there were substantial individual differences in performance across the three tasks.This study presents a novel comparison of category learning across modalities in the same individuals and demonstratesthat although commonalities exist, there is some domain-specificity to category learning.

Disfluency leads listeners to expect an upcoming reference tounfamiliar objects. In two experiments, we examined if thisexpectation is adapted based on the way disfluency has beenused in the discourse. Participants listened to instructions tolook at an object on a screen containing familiar and novelimages. We manipulated the co-occurrence of disfluency andreference to novel vs. familiar objects. In the predictivecondition, disfluent expressions referred to novel objects, andfluent expressions referred to familiar objects. In the non-predictive condition, fluent and disfluent trials referred toeither familiar or novel objects. Participants’ gaze revealed thatlisteners more readily predicted familiar images for fluent trialsand novel images for disfluent trials in the predictive conditionthan in the non-predictive condition. Listeners adapted theirexpectations about upcoming words based on recentexperience with disfluency. Disfluency is not invariablyprocessed, but is a cue adapted within the local context.

Humans can successfully interpret images even when they have been distorted by significant image transformations. Suchimages could aid in differentiating proposed computational architectures for perception because while all proposals predictsimilar results for typical stimuli (good performance), they differ when confronting atypical stimuli. Here we study twoclasses of degraded stimuli – Mooney faces and silhouettes of faces – as well as typical faces, in humans and severalcomputational models, with the goal of identifying divergent predictions among the models, evaluating against humanjudgments, and ultimately informing models of human perception. We find that our top-down inverse rendering modelbetter matches human percepts than either an invariance-based account implemented in a deep neural network, or a neuralnetwork trained to perform approximate inverse rendering in a feedforward circuit.

Language processing is predictive in nature. But how do people balance multiple competing options as they predict upcoming meanings? Here, we investigated whether readers generate graded predictions about grammatical gender of nouns. Sentence contexts were manipulated so that they strongly biased people's expectations towards two or more nouns that had the same grammatical gender (single bias condition), or they biased multiple genders from different grammatical classes (multiple bias condition). Our expectation was that unexpected articles should lead to elevated reading times (RTs) in the single-bias condition when probabilistic expectations towards a particular gender are violated. Indeed, the results showed greater sensitivity among language users towards unexpected articles in the single-bias condition, however, RTs on unexpected gender- marked articles were facilitated, and not slowed. Our data confirm that difficulty in sentence processing is modulated by uncertainty about predicted information, and suggest that readers make graded predictions about grammatical gender.

Studies have shown that temporal chunk measures in transcrip-tion tasks can be used to assess competence in various domains.However, in other tasks, chunking strategies and thus perfor-mance differences can be highly variable across participants.If such individual differences are also large in transcriptiontasks this would undermine the use of chunk-based competencemeasures. Using four stimuli with fixed spatial structures thisexperiment demonstrates that there is good consistency inchunking strategy across 52 participants in two types of tran-scription tasks. The experiment spans 16,000 data points.

We propose an agent model consisting of perceptual and pro-prioceptive pathways. It actively samples a sequence of per-cepts from its environment using the perception-action loop.The model predicts to complete the partial percept and propri-ocept sequences observed till each sampling instant, and learnswhere and what to sample from the prediction error, withoutsupervision or reinforcement. The model is exposed to twokinds of stimuli: images of fully-formed handwritten numer-als/alphabets, and videos of gradual formation of numerals.For each object class, the model learns a set of salient locationsto attend to in images and a policy consisting of a sequence ofeye fixations in videos. Behaviorally, the same model givesrise to saccades while observing images and tracking whileobserving videos. The proposed agent is the first of its kindto interact with and learn end-to-end from static and dynamicenvironments to generate realistic handwriting with state-of-the-art performance.

Neuro-cognitive models of word learning propose a role for sleep in consolidating new words, yet evidence for sleep-associated memory benefits outside of experimental contexts is scarce. This study compared wake- and sleep-associated memory changes in data from Memrise, a publicly available language-learning app. Memory for foreign words and phrases remained very high in accuracy across a 7-12 hour delay, and there were no differences in forgetting between wake and sleep. However, learners were quicker to arrive at the correct translation after a period of sleep compared to wake. This sleep-associated benefit was seen for words but not phrases, and could not be fully accounted for by circadian differences in completion time. As such, we demonstrate that the behavioural benefits of sleep on vocabulary can be observed in real-world language learning, and discuss the promise for combining small- scale lab studies with naturally occurring datasets to understand learning outcomes.

Explanations can be divided into two categories: those that appeal to general principles (abstractive) and those that tella concrete story (experiential) (Aronowitz & Lombrozo, 2019). Most psychological research has focused on abstractiveexplanations, identifying the benefits of abstraction for transfer (Ratterman, Genter & DeLoache, 1987;1989), prediction(Pacer & Lombrozo, 2017), and even cooperation (Burgoon, Henders & Markman, 2013). So why do we sometimesexplain in a less abstract, more narrative mode? Study 1 (N = 195) and Study 2 (N = 843) explore scientific explanationsand find that abstractive and experiential explanations (matched for quality) are transmitted along a chain of people withcomparable fidelity. However, over repeated transmission, experiential explanations become significantly more abstract -whereas abstractive explanations do not drift. Study 3 turns from science to human behavior to test the hypothesis thatexperiential explanations have mnemonic and other cognitive advantages in more narrative domains.

Video games are failure-rich spaces that provide a unique lens into how individuals react to failure in challenging environments. In this study, we utilize Cuphead, a notoriously challenging video game to demonstrate a unique behaviorally driven approach to understanding how an individual reacts to failure. Using measures of mastery orientation and data-driven retrospective interviews, we show that individuals who exhibit more mastery-oriented behaviors and more mastery-oriented behaviors before a helpless-behavior are more likely to show a higher game mastery orientation score, and that individuals that abandon a level before completion are more likely to show a lower game mastery orientation score. This introduces video games as a fruitful environment for understanding mastery orientation, a behaviorally driven approach to understanding how individuals react to failure, and provides a glimpse into how individuals react to failure in a challenging video game.

How has the diffusion of online communities changed howtheir users construct, view, and define their identity? In thispaper, we choose to approach this issue by considering twoparticular philosophical problems related to personal identity:1) The Characterization Question, namely “which actions, ex-periences, beliefs, values, desires, character traits, and so oncan we attribute to a given person?” 2) “How do self-other re-lations affect the ethical implication of identity construction?”To address them, we adopt a comprehensive framework com-posed of cognitive niches and cognitive niche construction the-ories, and we discuss different philosophical and technologicalnotions. In particular: the Filter Bubble problem, the conceptof affordances, and the Sartrean idea of Bad Faith.

Recent studies of meaning-sound systematicity haveconsistently found a small but significant positive correlationbetween semantics and phonology. The current study addsfurther evidence from an etymologically distinct language,Korean. Through multiple methods, the study shows thatsimilar sounds tend to have similar meanings in Koreanmonosyllables. Several cultural aspects of the language are alsoquantified. Pure Korean words return stronger meaning-soundcorrelation than Sino-Korean words, which is attributable tothe higher portion of homonyms in Sino-Korean. The mostfrequent words show the strongest systematicity, whichpermeates all of the monosyllables. Certain types of vowelsseem to contribute to this effect.

Statistical learning may be a key component of language learning in infancy, yet its neural basis is not well established. Thegoal of this study was to measure prefrontal cortical activity during auditory statistical learning, and to determine whetherthis activity predicted infants learning of statistical structure. Using non-invasive functional near-infrared spectroscopy(fNIRS), we recorded changes in blood oxygenation in lateral and medial prefrontal cortex in 8.5-10.5 month old infants(n=34) while they were exposed to statistical speech patterns. The stimuli consisted of 20-second videos of infant-directedspeakers speaking in either a statistical pattern or in a repeated syllable string. We found a positive association betweenright lateral prefrontal cortex activation during exposure to novel statistical speech structures, and subsequent learning ofthese patterns. These results contribute to growing evidence that prefrontal cortical activity during infancy is measurableand correlated with learning.

Fractions are a critical building block for the development ofhuman mathematical cognition, but the origins of this conceptare not well-understood. Recent work has found that a wholenumber sense is present in deep convolutional neural networks(DCNNs) pre-trained for object recognition and uses them asa model for investigating human numerical cognition. Do DC-NNs also have a fraction sense? If so, is it dependent or in-dependent of whole number processing? We investigated theneural sensitivity of a pretrained DCNN to both whole num-bers and fractions. We replicated and extended previous re-search that the sense of whole number emerges in a differentDCNN architecture. Further, we showed that DCNN is alsosensitive to fraction value, i.e., the ratio of numerosities. Test-ing this model, our results suggest that the fraction sense relieson the whole number sense.

Performance on spatial tests is not only a matter ability; it is also influenced by peoples confidence and belief of ability.Although we know that training can improve spatial performance, we know relatively little about the influences of beliefsand expectations on the efficacy of training. Here we investigated men and womens performance on a mental rotationtask and their prediction of their performance. We also examined whether providing information about different strate-gies influenced performance. The results demonstrate a spatial Dunning-Kruger effect; both men and women consistentlyoverestimated their performance. Womens estimates were lower than mens estimates were. Importantly, training influ-enced men and womens predictions of their performance in opposite directions; training increased mens confidence (butnot their performance), whereas training decreased womens confidence (but not their performance). The results suggestthat expectations and beliefs about spatial performance need to be considered when explaining training effects and sexdifferences

The purpose of this study was to identify associative processes for words in a second language and their referents. ThirtyJapanese participants learned associative conditions for novel words in Chinese and pictorial referents (CP), as well asnovel words in Chinese and words in Japanese (CJ), against a condition of only novel words in Chinese (C). After thelearning phase, participants conducted 2 retrieval tasks for word recognition and 3 recognition tasks for source-monitoringof the referents. Correct answers for each recognition task were provided to participants after each trial. Although correctanswers in all the conditions gradually increased in both the recognition and retrieval tasks, there were no significantdifferences among these conditions. In contrast, recognition and retrieval speeds were faster for CP than CJ. These findingssuggest that pictures contribute to recognition and retrieval speeds of associations between words in a second languageand referents.

It is not clear how cheering chants affect time perception which can be critical for sport performance. Here we measuredthe performance participants estimated second-order length of duration using a conventional psychophysical task. In thecontrol condition, five participants were required to produce 1, 3, 5 or 10 seconds of target durations by pressing a button ina gymnasium where nobody except for experimenters came in. In the testing condition, a group of cheerleaders appearedand chanted for 20 seconds after each block. The participants were required to complete otherwise the same task as thecontrol condition. The order of conditions was counterbalanced. The percentages of errors of estimated time was 4.229.58,-24.318.81, -24.9117.06, -22.4921.83 for 1, 3, 5 and 10 second of target durations in the control condition. Those valueswere 16.1322.54, -10.7716.22, -11.878.95 and -12.385.74 in the testing condition. In summary, the chants increased theduration participants produced.

Although artificial deep learning based neural networks have recently achieved impressive results on a range of realisticpattern recognition problems, it is still not completely clear how this problem is solved by the hierarchy of spiking neuronsin the brain which has inspired the deep learning approach in the first place. To achieve high accuracy on real-worldproblems artificial deep neural networks are trained using backpropagation, which is known to be biologically implausible.Recently Lillicrap et al. have proposed Feedback Alignment as a more biologically realistic algorithm able to train a deephierarchy of spiking neurons. In this work we examine whether a spiking deep neural network using such a biologicallyplausible learning algorithm is able to achieve good recognition accuracy on realistic motion recognition tasks.

Classical models of the emergence of compositionality in communication focused on the compositional nature of the en-vironment (Cangelosi, 2001; Cornish et al., 2008). Here we advance a model in which compositional structure emergesfrom integrating environments properties with agents actions. We take as a starting point Cangelosis (2001) model, wherea population of agents searched for edible mushrooms. Given opportunity to communicate, they evolved a system inwhich combinations of signs were sensorily grounded in combinations of mushroom properties. We modify this modelby grounding the communication also in agents’ actions. With this, we are able to evolve communication systems con-taining meaningful compositions of mushroom properties and agent actions. We investigate how such compositions canfacilitate a) learning the communication protocol, b) learning the adequate behavior policy. This kind of sensory-motorcompositionality seems better suited for coordinating navigation in dynamic environments.

Probability influences our social inferences. Here, we explored whether preschoolers use probabilistic information to inferothers desires. Sixty 3-year-olds were shown stories where one character went to a gumball machine with mostly redgumballs and just a few purple ones and another character went to a machine with the reverse distribution. Both charactersreceived a red gumball. Children in one between-subjects condition were asked who wanted a red gumball and children ina control condition were asked who knew they would get a red gumball. Children mostly selected the character who wentto the machine with more red gumballs when asked about desires but not when asked about knowledge. This suggests that3-year-olds can use proportions to infer others desires.

Recent work suggests that speakers’ lexical networks in theirnative and secondary languages are organized somewhatdifferently, with native languages showing greatersystematicity. We here test this claim in a new way, bymaking use of the “Retrieval-induced forgetting” effect(RIF). Specifically, practicing previously encodedinformation through rehearsal is expected to result in bettermemory for that information, regardless of which languagethe information is encoded. The RIF effect involves thesuppression of information that is associated with thepracticed information but is itself unpracticed. Since RIF isunderstood to rely on the association between the practicedand unpracticed memories, we predict it will be weaker whenapplied in a language with weaker or less systematicallyorganized lexical associations. Results confirm that while theexpected practice effect was evident in participants’ nativeand second languages, the RIF effect was only significant inparticipants’ native language. We discuss the relevance andimplications of this finding for second language speakers.

A long-standing question in cognitive development asks how young children learn non-obvious conceptual information(i.e., information that is not directly perceptible). For artifacts, this non-obvious information includes the categories itemsfall into (Rhodes, Gelman & Karuza, 2014), and their functions (Matan & Carey, 2001). We investigated how childrenlearn non-obvious information about novel artifacts from their caregivers during naturalistic interactions in a living his-tory museum. Forty caregiver-child dyads (Ages: R=4;22-8;0,Mage=5.98 years) visited two exhibits for 8 minutes each(i.e., a heritage store and house). Using a series of GEEs and correlational analyses, we found caregivers used differentpedagogical techniques to teach their children about different artifact properties. Namely, they used causal (rs=.49,p¡.001)and procedural information (rs=.60,p¡.001) to describe an artifacts function, but used questions (rs=.79,p¡.001) and com-parisons (rs=.64,p¡.001) to discuss an artifacts identity. These patterns are compatible with the broader literature on howchildren learn non-obvious information best (Gelman, 2009).

Offloading cognition to external stores is practiced ubiquitously in daily life (e.g., counting on fingers, writing lists), yetis a relatively new area of investigation within cognitive science. Previous experiments have assessed the benefits anddownfalls, including participants lowered memory for offloaded information that is no longer available (Gardony et al.,2013; Sparrow et al., 2011). In addition, when offloading, individuals appear susceptible to manipulations of their externalstore (Risko et al., 2019). Here we report an experiment investigating how the perceived reliability of an external storeaffects individuals susceptibility to manipulation of that store. Consistent with previous research, results suggest thatthe majority of participants do not notice an item inserted into their external store. However, once cued to this event,individuals do become more likely to subsequently notice a manipulation of their external store. Implications of thisresearch for our understanding of distributed memory systems will be discussed.

Teaching is an intuitive social activity that requires reason-ing about and influencing the mind of others. A good teacherforms a belief about the knowledge of their student, asks clar-ifying questions, and gives instructions or explanations to tryto induce a target concept in the student’s mind. We proposePartially Observable Markov Decision Processes (POMDPs)as a model of intuitive human teaching. According to this ac-count, teachers make pedagogical decisions with uncertaintyabout the knowledge state of their student. In two behavioralexperiments, human participants were tasked with balancingassessments (asking questions) and instructions to help teach astudent to build a tower of colored blocks. Human behavior inthe task was compared to the performance of a computerizedteaching algorithm optimized to solve the equivalent POMDP.Our results show that humans favor asking questions and estab-lishing common ground during teaching even at an economiccost and increase question asking as uncertainty grows.

Studies addressing the question of whether scalar inferencesgenerally incur a processing cost have yielded conflicting re-sults. Constraint-based accounts, which seek to unify theseconflicting results, make a prediction which we test here: theprobability of an interpretation and the speed with which it isprocessed depends on the contextual support it receives. Wemanipulated contextual support for the scalar inference in twotruth-value judgment experiments by manipulating a lexicalfeature (presence of partitive “of the”) and a pragmatic fea-ture (the implicit Question Under Discussion). Participants’responder type – whether their majority response was prag-matic (reflecting the inference) or literal (reflecting its absence)– was the main predictor of response times: pragmatic re-sponses were faster than literal responses when generated bypragmatic responders; the reverse was true for literal respon-ders. We interpret this as further evidence against costly infer-ence accounts and in support of constraint-based accounts ofpragmatic processing.

Previous studies have hypothesized that Japanese sound-symbolic words with voiced initial consonants (SSWV; e.g.,boroboro) rather than those with voiceless initial consonants (SSWVL; e.g., horohoro) or semi-voiced initial consonants(SSWSV; e.g., poroporo) induce stronger evaluations of the quality of psycholinguistic features. To investigate this hy-pothesis, we asked 36 Japanese participants to evaluate 13 psycholinguistic features (familiarity, visual imagery, auditoryimagery, haptic imagery, arousal, preference, disgust, hardness, softness, heaviness, lightness, fastness, and slowness) withSSWV, SSWVL, and SSWSV using 5-point semantic differential scales. All the initial consonants involved h (f; SSWVL),p (SSWSV), or b (SSWV). The experimental results showed that SSWV included higher levels of visual imagery, auditoryimagery, haptic imagery, arousal, disgust, hardness, and heaviness over SSWVL or SSWSV (ps ¡ .05). Taken together,these findings suggest that SSWV induces psychological and physical quality evaluations more than SSWVL and SSWSV.

Nonlinear probability weighting allows cumulative prospect theory (CPT) to account for seminal phenomena in riskychoice (e.g., the certainty effect). The attentional drift diffusion model (aDDM) formalizes that attentional biases canshape preferences as a sequential sampling process. We simulated choices between safe and risky options using the aDDMwith varying attentional biases to safe or risky options and modeled these choices with CPT. Changes in preferences dueto attentional biases were systematically reflected in the parameters of CPT’s weighting function (curvature, elevation).We demonstrate that this also holds empirically, in the sampling paradigm in decision from experience. Hence, nonlinearprobability weighting can arise from option-specific attentional biases in information search. This challenges commoninterpretations of probability-weighting parameters, suggests novel attentional explanations for empirical phenomena as-sociated with characteristic shapes of CPT’s probability-weighting function, and adds to the integration of two prominentcomputational frameworks for decision making.

When caregivers talk to their children, they can also look, point or manipulate the objects they are talking about. Thesemultimodal indexical cues can help the child disambiguate the referred object from potential targets in the environmentduring word learning. In fact, most naming episodes are modulated by some multimodal cues. In the work we presenthere, we use data from a semi-naturalistic corpus of caregiver-child interactions (ECOLANG corpus) where caregiverstalk to their children about objects that are new or known to the child. We focus on caregivers production and ask: (i)how often caregivers use any of the multimodal cues when naming the referent for new vs. known objects; (ii) what thetemporal relationship between multimodal cues and naming episodes is; (iii) whether there is a relationship between thecue usage (and its temporal distribution) and word learning.

Implicit statistical learning, whereby regularities between stimuli are detected without conscious awareness, is importantfor language acquisition. This form of learning has often been assessed using measures that require conscious decisionmaking or explicit reflection (e.g., 2AFC tasks). We aimed to measure statistical learning more implicitly. We leveraged thefact that frequently co-occurring stimuli may be chunked into a single cognitive unit, reducing working memory demands.We developed an artificial grammar in which sequences contained pairs of stimuli which always co-occurred (chunks)and more variable between-chunk transitions. In a novel visual recall paradigm, participants were asked to rememberand recreate sequences of serially presented images. Recall of predictable sequences improved over the course of theexperiment. However, recall dropped to initial levels when participants were presented with random sequences containingno predictable chunks. This approach represents a valuable method to measure statistical learning implicitly, withoutrequiring conscious reflection.

Debates assessing the merits of independent exploration and pedagogical instruction have been extensive. We compareeach of these learning environments against exploration followed by instruction to assess benefits to procedural learningand abstract rule-learning. Ninety-nine six-year-olds learned about novel locks and keys by either independently exploringprior to receiving instruction, proceeding to instruction without exploration, or acting without instruction. Children whoreceived instruction did not differ in procedural knowledge. However, children who explored prior to instruction were sig-nificantly more likely to learn the rules than children who did not explore or did not receive instruction. Childrens visualattention during instruction indicated that those who explored looked proportionally more to the stimuli as the experi-menter demonstrated. This suggests that the value of exploration is perhaps in preparing the learner for later information.Therefore, these results suggest that there is particular value for conceptual learning in the combination of exploration withinstruction.

In both professional domains and everyday life, people mustintegrate their own experience with reports from social networkpeers to form and update their beliefs. It is therefore importantto understand to what extent people accommodate the statis-tical dependencies that give rise to correlated belief reportsin social networks. We investigate adults’ ability to integratesocial evidence appropriately in a political scenario, varyingthe dependence between the sources of network peers’ beliefs.Using a novel interface that allows participants to express theirprobabilistic beliefs visually, we compare participants against anormative Bayesian standard. We find that they distinguish thevalue of evidence from dependent versus independent sources,but that they also treated social sources as substantially weakerevidence than direct experience. The value of our elicitationmethodology and the implications of our results for modelinghuman-like belief revision in social networks are discussed.

Verb metaphor has received little attention compared to noun metaphor. But verbs may be more likely to take onmetaphoric meanings than are nouns. One indication of this is verb mutability verbs are more prone to adapt theirmeanings under semantic strain than nouns (Gentner & France, 1988; King & Gentner, 2019). We tested the minimalsubtraction hypothesis (Gentner & France, 1988), which proposes (1) domain-specific dimensions of a verbs meaning areadjusted before abstract relational structure, and (2) degree of adjustment increases with strain. In three experiments, wecollected paraphrases of simple sentences and using word2vec found progressively greater abstraction of verb (but notnoun) meaning with strain. For example, a typical paraphrase of The wagon limped was The cart creaked along; a typicalparaphrase of The fantasy limped was The imagination faltered, reflecting greater abstraction of limped. These findingssupport the minimal subtraction account of verb metaphoric extension.

Adults represent events in terms of abstract participant roles (e.g., when Edith eats chocolate, Edith is an agent and thechocolate is a patient) (Rissman & Majid, 2019). English, however, lacks commonly-known labels for these roles, whichmay make the distinction less accessible to people. We presented 42 English-speakers with 24 pictures of an agent actingon a patient (e.g., one person kicking another). A red dot marked the agent in half the pictures and marked the patientin the other half. We asked participants to sort the pictures into two piles using whatever criteria they liked. After threeopportunities to sort the pictures, only 55% of participants sorted into agent/patient piles. When the remaining 45% weregiven the agent/patient piles, only half were able to explain the basis for the sort. This suggests a disconnect betweenthe robustness of agent/patient categories in implicit processing and the availability of this seemingly basic distinction toexplicit reasoning.

The method of loci is a powerful mnemonic technique for memorizing a list of unrelated items. With a pre-specified routein a familiar ”memory palace”, one can encode material by attaching items to loci along this route, and later effectivelyrecall them by mentally walking along the same route. Despite its efficacy, there is no existing model that explains whythe method of loci promotes memory improvement during memory search. To fill this gap, we provide a rational accountof why the method of loci improves memory. We define memory search as a task with the goal of minimizing retrievalcost, and demonstrate that the method of loci gives an optimal policy for this task. We discuss the implications of thisresult, and compare it with models of memory search without using mnemonic techniques.

We ask questions about everything from why clocks tick towhy the sky is blue. Although people sometimes preferteleological explanations over mechanistic explanations inresponse to ‘why’ questions, why questions are ambiguous–referring either to a ‘how’ question or a ‘for what purpose’question. In this paper, we examine the relation between theseimplicit questions and explanation preferences. First, we askedwhether people have specific expectations regarding ‘why’questions: How do they interpret these ambiguous cases anddoes this vary across domains? Indeed, people have strong,domain-specific expectations that mirror well-documentedexplanation preferences. People also have preferences aboutwhich specific question they would prefer to have answered. Inother words, ‘why’ questions are ambiguous but not treated assuch — and this has consequences for downstream explanationpreferences. We explore these consequences in light of both thephilosophical and psychological literature on explanation.

Groups of interacting individuals are often found to have an advantage over individuals in contexts of complex problem solving. We suggest that social interaction allows group members to share diverse introspections, perspectives and strategies, promoting the formation of more abstract problem representations, which – in turn – apply more flexibly to new problem contexts. In a reinforcement learning task inspired by the Wisconsin Card Sorting Test (WCST), participants categorized aliens as friendly or dangerous based on an underlying rule specifying feature combinations. After a number of correctly categorized trials, the rule would change (without explicit notification). Participants could solve the task by learning every new rule, but could also discover an underlying abstract rule, which would facilitate faster recovery from local rule changes. We compared pairs of participants individually trained on different rules (diversity pairs), with pairs trained on the same rule (non-diversity pairs), and individuals. We found that diversity pairs outperformed non- diverse pairs and individuals. Our findings suggest that diversity in prior experience benefits groups, likely due to processes of abstraction and cognitive flexibility.

We report a series of artificial language learning experiments designed to test child and adult learners abilities to acquirethree types of phonological variation. Previous work on experimental morphology (Hudson Kam & Newport 2005, 2009;Schuler, Yang & Newport, 2016) has found that young children turn inconsistent input into an invariant rule, while adultsreproduce and match variation in their input. Here we investigate whether phonological variation of three different types(deterministic conditioning, unconditioned variation, and probabilistic variation) exhibits a similar age pattern. We finda clear effect of age in grammatically-conditioned variability, with the youngest children showing a strong tendency toregularize to the stem form, adults probability-matching, and intermediate-aged children learning correct conditioning butnot matching the input probabilities. These results suggest, in accord with previous findings on morphology, that variationis not readily learned by young children and may instead be acquired as a separate process.

Impact of effort exertion on cognitive flexibility and stabilityAnna Mini Jos, Myles LoParco, A. Ross Otto*Department of Psychology, McGill University, Montral, CanadaEfficient task execution requires attention to task requirements while inhibiting distractors (cognitive stability) and adapt-ing to changes (flexibility). Previous studies have shown that individuals differ in their application of stable versus flexibleprocessing modes. Our study examined the impact of prior effort exertion on flexibility/stability trade-off.Participants performed a stability-flexibility paradigm, with pupil recording, before and after effort and no-effort manipula-tions were induced using different tasks. We analyzed the resulting change in preferences for stability/flexibility (voluntaryswitch rate).We found that the no-effort condition evoked a higher voluntary switch rate than baseline or after effort exertion. Partici-pants in the effort condition also showed higher response times and lower accuracy across trials. Pupil data shows that aftereffort exertion participants exert less effort in spontaneous switches and repeats. Additionally, the relationship betweenswitch cost (on forced-switch trials) and spontaneous switching rate increased after effort exertion. These results suggestthat stability/flexibility preferences can vary with prior effort exertion.

Children display an early sensitivity to implicit proportions (e.g., 1 of 5 apples vs 3 of 4 apples) but have considerabledifficulty in learning the explicit, symbolic proportions denoted by fractions (e.g., 1/5 vs 3/4). Theoretically, reducingthe gap between representations of implicit vs explicit proportions would improve understanding of fractions, but littleis known about how the representations develop and interact with one another. To address this, we asked 163 third tofifth graders to estimate the position of proportionally-equivalent integers and fractions on number lines (e.g., 3 on 0-8number line vs 3/8 on 0-1 number line). We found that, with increasing age, children were more accurate and linear inrepresenting both integers and fractions. More importantly, childrens estimates of implicit and explicit proportions becamemore coherent, such that a childs estimates of fractions on a 0-1 number-line was a linear function of the same childsestimates of equivalent integers. This representational coherence independently predicted childrens fraction proficiency inother tasks, suggest- ing that building a coherent understanding of proportions is an educationally-important goal.

Research examining mechanisms underlying human categorization has reported that when learning novel categories, adultstend to selectively attend to the diagnostic features, whereas young children allocate attention to multiple features. Thisstudy further investigated mechanisms underlying children and adults category learning by measuring their accuracy andresponse time in classification tasks. Participants were trained with categories that have a single deterministically predic-tive feature and multiple probabilistic features, and they were tested with items varying in the number of features. Theresults indicated that with sufficient training, both adults and children relied exclusively on the deterministic feature regard-less of overall similarity. Importantly, a deterministic feature is both sufficient and efficient for learning new categories.Participants were as accurate and fast when classifying items with most probabilistic features missing as when classifyingitems with all features present. However, when the deterministic feature was inaccessible, their accuracy dropped, andresponse times slowed.

People often over value their current property. For example,even young children will choose to keep their current propertyover trading it for property of similar utility (Hartley & Fisher,2018). In two experiments (N = 180), we examined howchildren aged 3 and 4 weigh the potential loss of existingproperty against the gain of property in their reasoning aboutothers’ actions. We found that by 4-years-old, children expectothers to prioritize the retention of existing property over theacquisition of new property. We suggest that this expectationreflects an understanding that people often value what theyalready own more than what they can potentially gain. Wediscuss the implications of our findings for competing theoriesof ownership reasoning, and for children’s reasoning about lossaversion.

Native speakers can judge whether a sentence is an acceptableinstance of their language. Acceptability provides a means ofevaluating whether computational language models are pro-cessing language in a human-like manner. We test the abilityof language models, simple language features, and word em-beddings to predict native speakers’ judgments of acceptabil-ity on English essays written by non-native speakers. We findthat much sentence acceptability variance can be captured by acombination of misspellings, word order, and word similarity(r = 0.494). While predictive neural models fit acceptabilityjudgments well (r = 0.527), we find that a 4-gram model isjust as good (r = 0.528). Thanks to incorporating misspellings,our 4-gram model surpasses both the previous unsupervisedstate-of-the art (r = 0.472), and the average native speaker(r = 0.46), demonstrating that acceptability is well capturedby n-gram statistics and simple language features.

People use objects to make social judgments about traits of owners. Do people seek social information in a rational waysuggestive of Bayesian inverse planning? In two experiments, participants aimed to learn about a stranger. Each trialshowed two sets of objects; the stranger had chosen one from each set, but their choice was hidden. Participants judgedwhich would help them learn more about the stranger: Revealing their choice from set A or B? Participants selected setsrationally, identifying sets with a greater range of options as more informative: Larger sets over smaller; sets varying instyle over sets varying only in color (Exp.1). Participants also took into account constraints: They chose sets as moreinformative when all options were functional vs. when some were not (Exp.2). People consider the generative processbehind objects selection, using inverse planning to reason about the informational value of others objects.

Fundamental theories of causal cognition suggest that causal inferences are guided by domain-specific knowledge inaddition to domain-general strategies used to draw causal conclusions. In particular, a divide seems to exist betweencausal judgments on physical versus psychological events. In line with these assumptions, domain-specific expectationsof causal patterns have been observed for psychological and physical events in a US-American context. The present studyintended to augment these findings by integrating (a) a cross-cultural perspective and by including (b) biological events aspart of an additional domain. Results replicated previous findings of domain-specific causal expectations in German andTurkish cultural contexts, but at the same time they indicated causal expectations for the biological domain to be partiallyless distinct.

Good-Enough production is the case where lexical selection between alternative words is subject to accessibility effects.Recent evidence suggests the name for a picture depends partly on the phonological form of the last word said. If Good-Enough production reflects variability in name activation and competition among alternative names, then non-adjacentmanipulations should also affect picture naming probability and latency. Participants read aloud printed words and namedpictures one at a time, including target pictures with two highly probable names (Dominant and Secondary). In theRepetition (vs Control) condition, Secondary names were read aloud in early trials, and target pictures were named at least50 trials later. Earlier reading resulted in a higher probability of Secondary names for target pictures, compared to Controlparticipants, suggesting that lexical selection is subject to non-adjacent influences. Dominant naming latency was greaterunder earlier Secondary name reading, supporting an interactive, competitive lexical selection mechanism.

During learning, humans recruit multiple cognitive mechanisms, including value-based reinforcement learning and ex-ecutive functions, like working memory. Recent research has begun to unmask connections between these two systems,proposing roles for attention and working memory in shaping underlying learning computations. Here, using a simpleinstrumental learning task, we provide evidence that working memory plays a role in establishing the correct state spacethat reinforcement learning operates over. We show that reinforcement learning is impaired when executive functioning istaxed by a secondary task and that this effect is especially pronounced when the two tasks are performed simultaneouslyrather than alternated. Computational modeling suggests that when the executive function is occupied, the reinforcementlearning system forms policy over a confused state-space. This study adds to a growing body of research proposing a morefundamental role for high-level executive processes in low-level reinforcement learning computations.

Research on empirical aesthetics has only recently included the study of literary texts, and not yet addressed the roleplayed by metaphors. We first created alternative versions of modern poems devoid of literary metaphors but equal inother properties to the original poems. The former are perceived as more beautiful. Second, we had participants readsentences extracted from poems and recorded their reading times and beauty ratings. Sentences could be literal, containdead metaphors, conventional, novel or extremely novel metaphors. Increasingly more novel and creative metaphors leadto increasing aesthetic appreciation, showing a clear linear relationship. Even extremely novel metaphors are appreciated,despite being more difficult to read. These results are only partially in line with current theories. Finally, aestheticappreciation is only partially driven by increasing cognitive load: metaphoricity plays a more important and substantialrole. The contribution of our results to extant theories will be discussed.

We present a novel framework for assessing the fit of cogni-tive models. Using this framework, we highlight limitationsof existing methods of model evaluation, and derive new ap-proaches to validating cognitive models. Tests of topographi-cal consistency emphasize how a model’s structure constrainsbehavior on pairs of coupled stimuli, even when point predic-tions on individual stimuli depend on estimates of the model’sfree parameters. By carefully selecting these coupled stim-uli such that they follow the distinct topography of the model,researchers can overcome some limitations of existing meth-ods. Finally, we provide a proof-of-concept example of how touse our approach to assess a model of multi-alternative, multi-attribute choice.

Childrens exploration is driven by opportunities for learning, and past research has suggested rational explanations for howearly home experiences may affect childrens active learning (Yu et al., 2020) or willingness to wait for rewards (Kidd etal., 2013). However, less work has characterized the relationship between childrens environmental contexts and play. Wepooled exploratory play data from past experiments in our lab (M(age)=56mos; N=278), and correlated play behavior withmedian income and education in the childs home zipcode. Children from lower SES areas played significantly longer,more variably, and spent a lower proportion of time focusing on demonstrated functions which traded off with lengthand variability of play exclusively for children from lower educated areas. Importantly, home income is confounded withdaycare income; future work will disentangle distinct influences of family SES and daycare environment. This work layscritical groundwork for understanding early active learning across developmental contexts.

One of the biggest puzzles in language acquisition is concerned with how children retreat from overgeneralization errorsin valence alternations, for example the ditransitive alternation. Pinker (1989) proposes that children are susceptible toovergeneralization when they acquire broad verb semantic classes initially and they recover when they acquire narrow verbclasses later. To empirically test this hypothesis, we devised a computational framework that automatically induces verbclasses from text data, by combining state-of-art word embeddings (Pennington, Socher & Manning, 2014) with graphalgorithms (Steyvers & Tenenbaum, 2005; Von Luxburg, 2007). We selected three representative valence alternationsfrom Levin (1993) and tested Pinkers hypothesis on five naturalistic language production corpora. Our results demonstratethat contrary to Pinkers predictions, broad verb classes are harder to induce than narrow classes and that semantic classesmay not be the primary mechanism that accounts for childrens retreat from overgeneralization errors.

Utility and probability have been considered independentconstructs for decision making under uncertainty. However,many studies have suggested that people assume there is acorrelation between probability and utility. Some studies havedemonstrated that people appear to estimate the utility ofevents depending on their probabilities, and other studiesrecently indicated the existence of “risk-reward heuristics” thatassume a negative correlation between probability and utilityin the real world when inferring winning probabilities frompayoffs during decisions made under uncertainty. This studyaimed to explore the relationship between probability andutility by requiring participants to estimate both probabilitiesfrom payoffs and payoffs from probabilities under a gain orloss situation. The results indicated that when estimating valuesof payoffs from probabilities, participants’ judgments showedclear negative correlations between probability and utility bothin the gain and loss condition. However, when estimatingprobabilities from payoffs, this negative correlation betweenutility and probability was found only in a gain situation. Theseresults support the existence of risk-reward heuristics, and atthe same time, suggest a possibility that people have differentintuitions for the probability-utility relationship between thegain and loss domains.

Adults parse continuous experience into meaningful events, a process referred to as event segmentation. This segmentationin turn colors how experiences are construed content experienced within an event is held mentally in an accessible state,which is then dropped after an event boundary. However, little is known about whether children are similarly influencedby event boundaries. Here, we tested seven- to nine-year-old childrens and adults recognition of objects experienced eitherwithin or across event boundaries of two cartoons. We found that children and adults were both more accurate and fasterto correctly recognizing objects that last occurred within events than across an event boundary. We, however, additionallyobserved an interaction such that childrens access to recent experience was less influenced by event boundaries than adults.Thus, while the spontaneous segmentation of complex events emerges by middle childhood, event structure less reliablyshapes the active contents of childrens minds than adults.

The coarse-to-fine hypothesis posits that, in the Human visualsystem, a coarse representation of visual information is propa-gated quickly through the retina to the cortex, whereas a finer,more detailed representation is propagated more slowly. In aprevious study we showed that recurrent synaptic connectionshelp predict low intensity EFEs. Furthermore, a feedback loopcoming from coarser information processing is postulated toinfluence later processing of finer features. In this paper, weintend to examine the value of coarser information and recur-rence in the processing of dynamic Emotional Facial Expres-sions (EFE). In a step forward in studying the importance ofrecurrent connectivity in the coarse-to-fine model, we testedits advantage for discriminating emotions for different spatialfrequencies and facial expression intensities. Using ArtificialNeural Networks, we modeled recurrent synaptic connectionswith a recurrent feedback loop. Using a Gabor filter bank, wecomputed different levels of spatial frequency features. Our re-sults replicate the advantage of recurrence at first facial expres-sion intensities. Our main finding is that the recurrent model isalso better when predicting high spatial frequencies features.Additionally, mid-to-low spatial frequencies are more usefulto the prediction of EFEs. We conclude that feature process-ing feedback has a significant effect in disambiguating facialexpressions when information is particularly complex, i.e., athigh spatial frequencies and low EFE intensities.

How do we assess learning in complex technology-mediated practices? How does the coordination of technological af-fordances and constraints mediate immediate performance and individual learning? In the technology-mediated practiceof programming, the compiler functions as a source of both affordances and constraints to the human cognitive agent.The compiler affords the compilation of executable programs and dynamically informative compiler feedback, while thecompiler also constrains acceptable code to a specific syntax. In this in-class experimental study, I investigate the contri-bution of compiler affordances and constraints to performance and learning in programming. The study results indicateaffordances as important facilitators of immediate performance. Conversely, constraints appear important mediators ofconceptual learning, which in turn facilitates internalized thinking decoupled from the original technological resource.The findings imply a need for teaching and learning activities that emphasize practicing resource-coordination and foran assessment practice that intelligently combines technology-mediated resource-rich tasks with decoupled resource-poortasks.

An accumulating body of evidence has demonstrated a tightcoupling between individual differences (ID) in statisticallearning ability (SL) and variation in language performancein child and adult native speaker populations, with some ini-tial evidence that this coupling extends to second language(L2) speakers. However, surprisingly little work has been con-ducted to assess potential interactions between SL and otherexperience-related and affective ID factors. Using a within-subjects design embedded in an ID framework, the presentstudy attempts to fill this gap by investigating whether the im-pact of SL ability on language is moderated by individual dif-ferences in personality traits and the amount of experience anindividual has had with the L2. The results of the study re-vealed a complex interplay between ID factors and variationin L2 comprehension of different types of complex sentencesindicating that the effect of SL ability on language comprehen-sion is moderated by personality traits.

General slowing of mental processing speed is hallmark of brain and cognitive aging. Thus far it has been limited under-standing in neural mechanisms underlying mental states during fluctuations between efficient versus inefficient cognitiveperformance within individual older adults. Here we examined electrophysiological responses during visual working mem-ory retrieval trials that are fast versus slow reactions. Wireless EEG along with accuracy and reaction times were recordedduring a modified delayed match-to-sample task in 17 cognitively normal older adults (age 65-95) from North America.Compared to trials that are faster than averaged (mean 584 ms), the late positive potentials during trials that are slowerthan average (mean 747 ms) showed increased responses to memory nonmatch distractors than those to object matchingmemory targets in frontal sites, as previously reported in older brains. Interestingly, the brainwaves during efficient andaccurate memory retrievals resemble those typically seen in younger adults.

We face a challenge when inferring what others know. Actions do not transparently reveal epistemic states: ignorantagents routinely ignore information too costly to obtain, and knowledgeable agents often confirm what they already knowwhen its convenient. We hypothesized that epistemic inferences are sensitive both to agents actions, and the underlyingutilities that best explain them. We tested this possibility in a simple task. Adults watched an explorer decide whetherto collect a map before searching an island for treasure. Participants (n=40) were asked to jointly infer how much theexplorer knew about the treasures location, and how much information the explorer believed the map had. Participantjudgments matched a computational model of epistemic inference structured around an expectation that agents rationallytradeoff information gain with information cost (r=0.86; 95%: 0.740.93, p¡.001). Our results suggest that adult Theory ofMind supports nuanced and graded epistemic inferences from observable action.

Modeling cognitive processes is one of the major tasks of cognitive science. This work presents a model of a studydescribed in Flexible Strategy Use in Young Childrens Tic-Tac-Toe (Crowley & Siegler, 1993) in which the authorsmade an attempt to characterize decision-making in a conflict-of-interests-like environment. In the experiments, kinder-garten/primary school children and an algorithm-based opponent played a series of games in Tic-Tac-Toe. The outcomesseemed to indicate the existence of a hierarchy of rules that is constructed with experience. Although already tested al-gorithmically, the simulation detailed in the paper was applicable to a narrow class of problems only. The model shownin this work was built using a cognitive architecture, i.e. computer-based structure mimicking the general functioning ofthe human mind. Concretely, we used a rule-based system Soar that operates in mental rules paradigm and in most partreplicated the results of the mentioned study.

Current theories of knowledge acquisition suggest that newlylearned knowledge does not always supplant prior knowledge,even when newly learned knowledge repairs errors. Newknowledge may suppress prior knowledge, particularly foroverlearned, explicit responses, creating internal competitionbetween knowledge elements. Competition between new and priorknowledge may be one reason misconceptions are highly resistantto repair. The present study examines misconceptions in a specificdomain: pre-service educators’ beliefs about neuromyths.Addressing misconceptions in pre-service educators is importantbecause these misconceptions are likely to be transmitted tostudents and may reduce the effectiveness of instruction. Acomputer mouse-tracking paradigm measured explicit beliefs inneuromyths as well as implicit uncertainty during thedecision-making process. The findings demonstrated thatpre-service educators often endorsed neuromyths but wereuncertain about the veracity of neurofacts. These findings add toour knowledge of misconceptions, their durability, anddemonstrate a need to address misconceptions in educatorpreparation.

Humans efficiently find near-optimal (i.e., near-minimum-length) tours when solving small instances of the TravelingSalesperson Problem (TSP), a problem hard for computers. We hypothesize that this is possible because they use thefollowing strategy: cluster the points, solve the smaller TSPs for each cluster, and then solve the TSP defined by theclusters. This study focused on the antecedent process of human clustering. 42 participants clustered 56 sets of 15 to 40points on two occasions. We found that human clustering is generally reliable (M Fowlkes-Mallows Index = 0.75) forall problem sizes. Reliability was higher for problems that showed statistical evidence of cluster structure versus no suchstructure, and was not affected when the problem was flipped for the second presentation. Thus, humans are sensitive tocluster structure, and clustering is a stable foundation for solving TSP instances. This sets the stage for future research onclustering-based TSP strategies.

Is the human representation of time symmetrical or asymmetrical toward the past and the future? Some studies suggestthat we perceive the future as being closer, more attended and more valued than the past (indicating a future asymmetry).By contrast, asymmetries toward the past have been found in past-focused cultures. Yet, available evidence is still limitedand mixed. In the present work we searched for asymmetry in several temporal tasks (temporal distance, time discounting,temporal depth, and self-continuity) in a set of cultures that vary widely in their temporal focus (American, Spanish,Turkish, Chinese, Moroccan, Serbian, Bosniak and Croatian; total N=1075). The results supported an overall asymmetrytoward the future in all tasks, although it failed to be significant in most cultures when considered on their own. However,only self-continuity showed variations that can be explained by the contrast between past-focused versus future-focusedcultures.

The meaning of a natural language utterance is largely determined from its syntax and words. Additionally, there isevidence from theories in semantics and neuroscience that humans process an utterance by separating some amount ofknowledge about the lexicon from the knowledge of word order. In this paper, we propose neural units that can enforcethis constraint over an LSTM encoder and decoder. We demonstrate that our model achieves competitive performanceacross a variety of domains including semantic parsing, syntactic parsing, and English to Mandarin Chinese translation. Inthese cases, our model outperforms the standard LSTM encoder and decoder architecture on many or all of our metrics. Todemonstrate that our model achieves a desired partial separation between the lexicon and syntax, we analyze its weightsand explore its behavior when different neural modules are damaged. When damaged, we find that the model displays theknowledge distortions that aphasics are evidenced to have.

The vocabulary of human languages has been argued to support efficient communication by optimizing the trade-offbetween complexity and informativeness (Kemp and Regier, 2012). The argument has been based on cross-linguisticanalyses of vocabulary in semantic domains of content words such as kinship, color, and number terms. The present workextends this analysis to a category of function words: indefinite pronouns (e.g. someone, anyone, no-one, cf. Haspelmath,2001). We establish the meaning space and feature-based representations for indefinite pronouns, and show that indefinitepronoun systems across languages optimize the complexity/informativeness trade-off. This demonstrates that pressuresfor efficient communication shape both content and function word categories, thus tying in with the conclusions of recentwork on quantifiers (Steinert-Threlkeld, 2019). Furthermore, we argue that the trade-off may explain some of the universalproperties of indefinite pronouns, thus reducing the explanatory load for linguistic theories.

Creative potential in childhood predicts creative achievementslater in life. But relatively little is known about the factors andprocesses that promote creativity in children. A theoreticalframework by Carr, Kendal, and Flynn (2016) identified sev-eral factors, including curiosity and exploration, that might fa-cilitate creativity and innovation. Building on this framework,we propose another factor – children’s feeling of competence– that might affect both curiosity and creativity. In the presentstudy, 5- to 7-year-olds were induced feelings of high or lowcompetence by solving math problems. Next, they completedthree tasks that measured their curiosity and creativity. Thefindings showed that children who felt more competent ex-plored more on a novel toy and showed better creative prob-lem-solving abilities.

Memory is a crucial component of everyday decision making, yet little is known about how memory and choice processesinteract, and whether or not established memory regularities persist during memory-based decision making. In this paper,we introduce a novel experimental paradigm to study the differences between memory processes at play in standard listrecall versus in preferential choice. Using computational memory models, fit to data from two pre-registered experiments,we find that some established memory regularities (primacy, recency, semantic clustering) emerge in preferential choice,whereas others (temporal clustering) are significantly weakened relative to standard list recall. Notably, decision-relevantfeatures, such as item desirability, play a stronger role in guiding retrieval in choice. Our results suggest memory processesdiffer across preferential choice and standard memory tasks, and that choice modulates memory by differentially activatingdecision-relevant features such as what we like.

I present a novel method of conceptualizing metacognition in a computational hierarchy. Metacognition is commonlydescribed as cognition acting on itself, and correlates with enhanced performance in memory, reasoning, emotional reg-ulation, and motor skills. Understanding metacognition requires surmounting two barriers: its high-level abstraction anddisputed terminology. To overcome these barriers I employ a computational cognitive architecture to first define the baseunits of cognition and how they come to act on themselves. Well-defined computational units are built up into a hierarchyof cognitive processes. These forms of cognition are then connected back to clarify the research literature. Each formis built into working models within ACT-R to support this hierarchical systems viability. The intention of this hierar-chical model is to help clarify the nature of metacognition by supplementing verbal cognitive definition with rigorouscomputational terminology.

Our ability to link related events could be supported either byconnecting their representations in memory, or by storing themseparately but integrating their content when later drawinginferences. Here, we adapted classic memory contingencyanalyses to develop and validate an integration index designedto tap stored representations. We conducted three pre-registered experiments adopting this metric. We found positiverecall dependency for associations experienced both within thesame and across different events. Compared to a conventionalinference test, we found that recall dependency was moresensitive to a manipulation of memory integration. Leveragingrecall dependency to investigate individual differencesrevealed that better memory for contextual detail wasassociated with faster inference judgments, consistent withhigh-fidelity representations of related memories—but only forpeople who tended to store memories separately. Ourapproach, thus, provides an important tool to illuminate howrelated events are represented in memory.

The current study investigated the impact of sleep deprivation (SD) on the use of third-person self-talk, a relatively effort-less strategy, to regulate emotion. Twenty-four participants (age = 22.75 2.68, 54.17% male, 33.33% good sleepers) com-pleted a cue-picture ERP paradigm after normal sleep and SD conditions, in which they viewed negative or neutral stimuliand reflected on their feelings using either the pronoun I or their name (third-person). We calculated post-instruction latepositive potential (LPP) that has been found sensitive to emotion regulation strategies and closely related to amygdalaactivity. While poor sleepers showed greater LPP amplitudes overall, F(1,7) = 17.50, p = .004, SD only increased the LPPfor negative picture trials among good sleepers but not poor sleepers, F(1,7) = 5.37, p = .054, suggesting that the effect ofSD on emotion regulation using third-person self talk was moderated by habitual sleep quality.

Theoretical models of analogical retrieval implicitly assumethat the cognitive system continuously scans long-term memorybased on the contents of working memory (WM). Experiment 1revealed that when a target analog is presented in the context ofa problem-solving activity, a prompt to search for analogoussituations adds nothing over-and-above the probabilities ofbeing spontaneously reminded of an analogous problem.More exploratory in nature, Experiment 2 presents the firstexperimental evidence of analogical retrieval during hypothesisgeneration. Our prompt to search for analogous phenomenaincreased access to distant analogs, suggesting that hypothesis-generation does not reliably elicit a search for analogousphenomena. Results suggest that a search for analogous casesis not automatically triggered by the contents of WM, and thatthe nature of the tasks in which the analogs are embeddeddetermines whether a search for analogs will be initiated.

Human dialogue is governed by communicative norms thatspeakers are expected to follow in order to be viewed as coop-erative dialogue partners. Accordingly, for language-capableautonomous agents to be effective human teammates they mustbe able to understand and generate language that complieswith those norms. Moreover, these linguistic norms are highlycontext sensitive, requiring autonomous agents to be able tomodel the contextual factors that dictate when and how thosenorms are applied. In this work, we consider three key lin-guistic norms (directness, brevity, and politeness), and exam-ine the extent to which adherence to these norms varies underchanges to three key contextual factors (potential for harm, in-terlocutor authority, and time pressure). Our results, based ona human-subject study involving 5,642 human utterances, pro-vide strong evidence that speakers do indeed vary their adher-ence to these norms under changes to these contextual factors.

A nascent understanding of absent reference emerges around 12months: provided with rich contextual support, infants look and pointto the location of a displaced object. When can infants understandabsent reference without contextual support? Using a proceduremodified from Hendrickson and Sundara (2017), 13- and 16-month-olds first listened to utterances containing familiar target words, whileviewing a checkerboard. Then, two objects – a referent and a distractor(e.g., a cup and a shoe) – appeared on the screen. Only 16-month-oldsdemonstrated a reliable looking preference for the referents, suggestingthat listening to the utterances activated their mental images of thereferents. These results establish that at 16 months, infants comprehendreference to absent entities without any contextual support.

Throughout development, humans infer unobserved properties of the objects they encounter. However, it is often ambigu-ous whether these inferences result from category-based reasoning or overall similarity to previously observed objects. Inthis study, we examined inferences about hidden properties in four-year-old children (N=36) and adults (N=44). We taughtparticipants three categories of artificial creatures. Each category had one critical feature, where one of its variations wasmore common to members of the category, while the other was more common overall. We found that, on average, bothgroups used within-category frequency to predict the value of an unseen critical feature. However, individual differencesrevealed distinctions between the groups. While adults who used within-category frequency for critical items used overallfrequency for other items, this correlation was qualitatively reversed in children. This suggests that some children weresensitive to category knowledge when predicting unseen features, but others likely used a novelty heuristic.

The relevance of Broca’s area to both language and non-linguistic sequence processing is well established. However, manyof the previous fMRI studies on artificial grammar learning use letter sequences as their non-linguistic stimuli. Since theletters are linguistic in nature, these may inadvertently activate language circuits independent of the artificial grammar. Inaddition, participants have been explicitly told before testing that they needed to classify sentences as either grammaticalor ungrammatical. Thus, it is possible that part or all of the activation of Broca’s reported in these studies is an artifact ofthese manipulations. In our current study, we used sequences of human faces instead of letters, and tested participants insuch a way that they were never aware they were even being tested. Nevertheless, most participants still showed evidenceof learning the non-linguistic artificial grammar, and their Broca’s area was also differentially active for ’grammatical’ vs.’ungrammatical’ sequences.

This study demonstrates a new approach for boosting peoples decision-making abilities. Previous studies have demon-strated that those who have less knowledge are more likely to make a scientifically biased decision. Therefore, we canexpect that providing them with more knowledge can reduce their biases. However, since people have difficulty changingtheir minds in response to knowledge that contradicts their opinions, it is unclear whether people accept and appropriatelyunderstand the provided knowledge. To more efficiently help these people, this study focused on the illusion of explanatorydepth, which means that the knowledge people think they have is greater than the knowledge they actually possess. Weconducted two experiments and demonstrated that (i) those who had a stronger illusion of explanatory depth were morelikely to make a logically biased decision, and (ii) by informing them of their illusion and providing them with objectiveknowledge, we could reduce their bias.

Decisions are often better when pursued after deliberation and careful thought. So why do we so often eschew deliberation, and instead rely on more intuitive, gut responses? We suggest that in addition to well-recognized factors (such as the costs of deliberation), people hold normative commitments concerning how decisions ought to be made. In some cases (e.g., when choosing a romantic partner), relying on deliberation (over intuition) could be seen as inauthentic or send a problematic social signal. In Experiment 1 (N = 654), we show that people in fact hold such domain-sensitive processing commitments, that they are distinct from reported descriptive tendencies, and that they contribute to predicting reported choice. In Experiment 2 (N = 555), we show that choosing intuitively vs. deliberately supports different inferences concerning confidence and authenticity, with the domain variation in inferences in Experiment 2 closely tracking the domain variation in normative commitments observed in Experiment 1. In Experiment 3 (N = 1002), we rule out an alternative explanation. These findings inform theories of judgment and decision-making, as well as efforts towards improving decision-making through critical thinking.

This study investigates whether incongruency of valences between emoji and text in texting will promote stronger negative inference in readers. An experiment assessed participants’ judgments of the text messages by recording their response times and perceived valence from the messages (either positive or negative) under the following manipulations: positive or negative messages paired with an emoji that convey positive, negative or ambiguous/neutral emotions (i.e. the pairing of emojis and test may be congruent or incongruent in their valences). Compared with congruent text messages, we found that incongruency between emojis and texts promoted stronger negative inference and elicited a longer processing time, even in texts that conveyed a positive meaning or when the emoji itself was ambiguous/neutral. These results suggest that texts and emojis jointly influence the perceived mood of messages, hinting the importance of the effective use of emojis in order to convey intended meanings and emotions efficiently.

Sampling-based models, which assume that people remember or simulate instances of events and count outcome propor-tions to make probability judgments, account for many apparent biases in human judgment. The success of such modelsis generally dependent on sample size, as particularly large or particularly small samples are often required for a modelto reproduce effects observed in data. Thus, systematically exploring the actual number of instances that people tend tosample is an important criterion in evaluating model credibility. Here we propose a method of estimating sample size byway of inter-judgment variance. We show through model recovery that this method will reliably recover the correct samplesize and subsequently apply the method to two well-supported models of human probability judgment, Probability TheoryPlus Noise and the Bayesian Sampler. Results indicate, in both cases, that human probability judgments are based on arelatively plausible (¡ 10) number of sampled instances.

Participants show faster and more accurate processing for arbitrary geometrical stimuli if they are paired with a self- rel-evant label (triangle = you). We ask whether participants only form self associations with specific exemplars (triangle,circle, square), or whether they analyse the stimuli in terms of visual features, (for e.g. no. of vertices = 3), and cangeneralise the learned associations with the entire category of the stimuli (say, all triangles). In our experiments, partici-pants showed the self referential advantage not only to previously exposed exemplars of the same category, but also novelstimuli that could be categorised on the basis of similar visual features. Interestingly, they could generalise not only on thebasis of a single rule, but also on the basis of a conjunction of more than one rule. These findings could be extended toexplain social categorisation in the real world through group memberships.

We investigated how people examined the self or anothers learning history using a complex dynamic control task. Thirty-eight undergraduates were assigned to self or self-as-instructed-other conditions. Participants were asked to performthe task twice and describe their thinking as they examined the learning history provided in the second session. Theparticipants in the self condition were provided with their own learning history, whereas those in the self-as-instructed-other condition were presented with their learning history as if it were anothers. We compared their performance on thecontrol and structure tests between conditions. The results showed that performance on the control test improved oversessions regardless of the condition. The results also showed that the participants in the self-as-instructed-other conditionengaged in evaluation or guessing more often than those in the self condition, suggesting that there are differences in howpeople examine a persons learning history depending on its source.

When learning to count, children actively engage with a varietyof counting tasks and observe demonstrations by more knowl-edgeable others. We investigate how a single neural network-based agent, situated in a multimodal learning environment,can learn from observing such demonstrations to perform mul-tiple number tasks such as counting temporally and spatiallydistributed objects, and a variant of the give-N task. We findthat i. the agent can learn different tasks that require counting,ii. learning progresses in similar stages for different tasks, iii.sequential learning of subtasks aids learning of the full task ofcounting spatially distributed objects, and iv. a mechanism forupdating memory when each object is counted emerges fromlearning the task. The work relies on generic deep learningprocesses in widely used neural network modules rather thanmechanisms specialized for mathematics learning, and pro-vides an architecture in which aspects of a sense of numberemerge from learning several different number related tasks.

Systems of nominal classification act as a functional means of categorisation, yet the number and type of categorieswithin these systems vary considerably across languages. The impact of vastly different classification systems on thecognitive representations of concepts is intriguing. We designed a suite of experiments to compare classifier systems in sixOceanic languages, chosen because their inventory of classifiers ranges from two to 23. Effective categorisation needs tobe informative to maximise communicative efficiency, but also simple to minimise cognitive load. Our sample languagesallow us to investigate the trade-off between the two principles of informativeness and simplicity to shed light on therelative optimality of their classification systems. Results from 122 participants across three experiments (free listing,card sorting, video vignettes) indicate that cognitive salience varies as a function of classifier inventory. We discuss theimplications of these results for the nature of nominal classification.

This experiment explored how attributions of agency to the Earth (psychological and vitalist) and design-based views ofnature impact adults degree of environmental concern. Undergraduates (N=133) were randomly assigned to watch differentvideos. In the Person condition, the video described the Earth as a person with beliefs and desires. In the Animal condition,the Earth was described as a living being with non-intentional survival goals. The Control condition described the Earthas a physical-mechanical object. No significant differences were found between conditions in psychological attributionsto the Earth. However, analyses controlling for condition, gender and design attributions revealed a significant interactionbetween the Person Condition and psychological attributions to the Earth (=.29, p¡0.01): Relative to the Animal condition,participants in the Person condition who described the Earth in more psychological ways also had harsher judgements ofenvironmentally damaging acts. Analyses of the biocentric nature of these justifications are still ongoing.

In this project we show how the cognitive processes, and the learning patterns, of participants performing a typicalmultiple-cue learning (MCL) task is affected by the format (numeric or verbal) of the cues and the criterion. In twoexperiments we investigated the hypothesis that the reliance on linear additive integration in MCL-tasks is especiallypronounced when cues are presented in a numeric rather than verbal format. The results support the hypothesis. Withnumeric cues, we replicate previous findings supporting a systematic shift from cue-abstraction and additive integrationof cues when the task is additive, to reliance on exemplar memory when the task is non-additive. However, when cuesare verbal, no systematic shift in cognitive process is evident, with participants in general relying on exemplar memoryregardless of the task structure. Consequently, the numerical format is advantageous for learning in the additive task butat times disadvantageous in the non-additive task.

Using a novel dataset from the TopCoder platform we investigate solvers search for solutions as well as the role of expertisein shaping their problem solving process. We find that while some solvers on the platform do act according to the win-stay, lose-shift rule, skilled solvers are less likely to rely on this meta-heuristic. Somewhat counter-intuitively we find thatexperts make more smaller changes, that is, they change their solutions more often than non-experts, but when they do,they make smaller changes. This can be explained by the fact that experts seem to be able to come up with a good problemrepresentation early on, that doesnt require large adjustments.

Imagine discussing yesterdays dinner with a friend: It wasn’t particularly tasty. Your friend concurs, it was very salty!Thinking you were talking about the appetizer (which wasnt salty at all), youre forced to reconsider which course yourfriend was talking about. Was the appetizer salty to her? Was she talking about the main course? People encounter mis-understandings in everyday conversation, yet quickly and seamlessly resolve them. How people do this is an explanatorychallenge: the thing being talked about (i.e., the referent) is often not physically present during the conversation. Hence,theres no easy way for interlocutors to establish common ground via ostensive signaling (e.g., by pointing at the dish). Wedevelop a model of speakers that use pragmatic reasoning to infer the referent inferred by listeners. We explore the perfor-mance of this model using agent-based simulated conversations. The results imply necessary and sufficient conditions forsuccessful updating.

This paper investigates the emergence and development ofchildren’s ability to adapt their information search to differentgoals. In Study 1, 3- to 7-year-olds had to decide whether tostudy the arms or legs of two monsters to predict which wouldsucceed at a throwing vs. jumping challenge. Children’s abil-ity to adaptively select the relevant piece of information andtailor their search to the given goal increased with age, surpass-ing chance level around 4;6. Study 2 investigated additionaladaptation to distributions of, e.g., long arms in the search do-main. Preliminary results confirm the observed developmentaltrend in search adaptiveness and effectiveness, suggesting anability to tailor information search to the relevant distributionsin the environment. These studies provide first insights intothe development of adaptive information search given complexgoals, deepening our understanding of this key aspect of learn-ing, judgment and decision-making.

Much of cognitive science is based on constructing, validating, and comparing formal models of the mind. Whereascoming up with new and useful models requires expertise and creativity, validating the proposed models and comparingthem against the state-of-the-art mainly requires a systematic, rigorous approach. The task of model validation is thereforeparticularly well-suited for the types of automation that have propelled other research fields (cf. impact of bioinformaticson biology). Here we propose a model benchmarking framework implemented as an open-source Python package namedCogniBench. Given a set of candidate models (which can be implemented in various languages), experimental obser-vations, and scoring criteria, CogniBench automatically performs model benchmarks and reports the resulting matrix ofscores. We demonstrate the potential of the proposed framework by applying it in the domain of learning and decisionmaking, which poses unique requirements for model validation.

From founding a new start-up to applying for a big grant, many activities involve pursuing risky goals with stark all-or-nothing outcomes and high uncertainty about the chances of succeeding in ones goal. These endeavors require patientperseverance, where time invested towards achieving a rewarding risky goal also implies the opportunity cost of forgoingsafer alternatives, such as working for a reliable wage with immediate rewards. How do people behave when choosingbetween such risky endeavors and safe alternatives, where the dynamic nature of the task has implications beyond expectedutility maximization? We present a new experimental paradigm, where by manipulating the relative rewards, task uncer-tainty, and the success threshold for achieving the risky goal, we are able to identify the environmental factors influencingperseverance. We then compare human behavior to the optimal strategy, along with a variety of boundedly rational policiesand heuristics that trade-off efficiently between complexity and performance.

This study investigates interference effects in sentenceprocessing. A parade case involves agreement attraction,where the processing of a number mismatch between a verband its subject is eased by a number-matching lure (*Thekeytarget to the cabinetslure were rusty), relative to sentenceswhere neither noun matches the verb (*The key to the cabinetwere rusty). Existing accounts claim that this effect reflectserror-prone retrieval or misrepresentation of the target.Recently, a third account has been proposed which claims thatthe contrast between the two configurations reflects increaseddifficulty in the second sentence due to feature overwriting inthe encoding (both nouns are singular). We provide resultsfrom two self-paced reading experiments that isolate theeffects of feature overwriting and attraction by manipulatingthe presence of an agreement cue. Results showed a largerdifference within the configurations with a cue, which suggestthat attraction cannot be reduced to feature overwriting.

While anticipated rewards have been shown to impart enhancements on memory performance, it remains unclear whetherthese benefits reflect improved encoding or more cautious decision-making. In two experiments, participants (N=47, each)encoded complex videos depicting everyday episodes and were tested for their memory of various details. Importantly,participants were informed that each video was associated with either high (25 cents) or low (1 cent) reward at eitherencoding or retrieval. We found participants were more accurate for questions relating to high reward videos only whenreward information was presented at encoding. Memory performance and response-times were modeled using a driftdiffusion model to assess the effects of reward on decision parameters. The drift rate was found to be significantly largerfor high reward videos when compared to low reward videos, only when reward was presented at encoding. These resultssuggest that reward at encoding enhances memory selectivity for detailed episodic information.

Research on causal learning suggests that people are capable of learning nondeterministic causal relations, but might expectcausal relations to be deterministic in certain contexts. In two experiments, we demonstrated that peoples expectations ofcausal determinism are context-sensitive and can influence causal judgments in a sequential learning task. When the datawere deterministic (100% success) and participants expected the cause to be deterministic, their causal judgments wereat ceiling. When participants expectations were nondeterministic, causal ratings increased with accumulating positiveevidence. When the data were probabilistic (75% success), participants exhibited a high violation-of-expectation effectupon seeing the first failed event when they expected the causal relation to be deterministic, and much less so whentheir expectation was nondeterministic. We built a simple Bayesian model to explain participants violation-of-expectationeffect as a selection between two distinct hypotheses: that the causal relation in question is deterministic, and that it isnondeterministic.

Representing numbers spatially allows us to more quickly and accurately compute average magnitudes. For instance, abar graph lets us quickly estimate the average height of several values. What algorithm might we implement to find theaverage position of observations in space, and how might we leverage this algorithm for quick numeric estimates? Weasked subjects to estimate either the average spatial location of points on a line or the average value of written integers.We propose an iterative algorithm where the subject 1) makes a noisy estimate of the distance of each observation to avisual reference point, 2) infers the posterior of the average of those distances, and 3) updates the reference point to thenew posterior mean. Our algorithm correctly predicts that subjects accuracy and confidence decrease with the varianceof observations. We further investigate similarities and differences between the fitted models for spatial vs. numericaveraging.

Adults (Keysar et al, 2000) and children (Epley et al, 2004) sometimes commit egocentric errors when interpreting otherscommunication, if the self-perspective differs from the speakers perspective. Training imitation inhibition reduces egocen-tric error in adults (Santiesteban et al., 2011), presumably because it makes salient the distinction between self and other.As managing the self-other perspective difference may undergo developmental changes during preschool years (South-gate, in press), we tested whether a social imitation inhibition training may reduce egocentric mistakes in 3-6-year-oldchildren. Results with n=47 (of n=50 preregistered) children show that the imitation inhibition group selected the object towhich the speaker referred more often than children in a control condition (F(1,35)=5.346, p=.026). However, there wasan interaction with age (F(2,35)=3.805, p=.032): only 4-year-olds, but neither 3- nor 6-year-olds, were more accurate inthe inhibition group. Childrens reaction times and hesitation will be analyzed on the final sample.

A variety of conceptualizations of psychological uncertaintyexist. From an information-theoretic perspective, probabilisticuncertainty can be formalized as mathematical entropy. Cog-nitive emotion theories posit that uncertainty appraisals andmotivation to reduce uncertainty are modulated by emotionalstate. Yet little is known about how people evaluate proba-bilistic uncertainty, and about how emotional state modulatespeople’s evaluations of probabilistic uncertainty and behaviorto reduce probabilistic uncertainty. We tested intuitive entropyevaluations and entropy reduction strategies across four emo-tion conditions in the Entropy Mastermind game. We used theunified Sharma-Mittal space of entropy measures to quantifyparticipants’ entropy evaluations. Results suggest that manypeople use a heuristic strategy, focusing on the number of pos-sible outcomes, irrespective of the probabilities in the proba-bility distribution. This result is surprising, given that previouswork suggested that people are very sensitive to the maximumprobability when choosing queries on probabilistic classifica-tion tasks. Emotion induction generally increased participants’heuristic assessment. The uncertainty associated with emo-tional states also affected game play: participants needed fewerqueries and spent less time on games in high-uncertainty thanin low-uncertainty emotional states. Yet entropy perceptionswere not related to subjectively reported uncertainty, numer-acy or entropy knowledge, suggesting that entropy perceptionsmay form an independent psychological construct.

With 10 to 20 sounds per second, fluent speech requires extremely skilled motor coordination. Therefore, young speakerswith an immature or malfunctioning speech production system may be particularly challenged by the temporal aspects offluent speech. In the present study, we examine nested temporal bout structure (Abney et al., 2014) to investigate howyoung speakers (children 9-12; adolescents 13-17 years old) who do and do not stutter might differ in their temporalorganization of speech during reading. Allan Factor analyses show that nested clustering of peak amplitudes at shorttime-scales (¡ 300 ms) differs between children and adolescents, pointing to developmental differences in the temporalorganization of syllabic structure. Greater nested clustering at longer timescales (¿ 300 ms 10 s) was characteristic ofstuttering, particularly in adolescents whose stutter risks to persist into adulthood. We discuss these findings in light oftheories of stuttering and the acquisition of fluent speech

The translation problem in moral AI asks how insights into human norms and values can be translated into a form suitablefor implementation in artificial systems. I argue that if my answer to a question about the human mind is right, thenthe translation problem is more tractable than previously thought. Specifically, I argue that we can use principles fromreinforcement learning to study human moral cognition, and that we can use principles from the resulting evaluative moralpsychology to design artificial systems capable of passing the Moral Turing Test (Allen, 2000). I illustrate the core featuresof my proposal by describing one such environment, or gridworld, in which an agent learns to trade-off between monetaryprofit and fair dealing, as characterized in behavioral economic paradigms. I conclude by highlighting the core technicaland philosophical advantages of such an approach for modeling moral cognition more broadly construed.

It is now well-established that long-term memory (LTM) knowledge, such as semantic knowledge, supports the temporary maintenance of verbal information in working memory (WM). This is for instance characterized by the recall advantage observed for semantically related (e.g. leaf - tree - branch) over unrelated (e.g. mouse - wall - sky) lists of items in immediate serial recall tasks. However, the exact mechanisms underlying this semantic contribution remain unknown. In this study, we demonstrate through a convergent approach involving computational and behavioral methods that semantic knowledge can be efficiently used to save attentional WM resources, thereby enhancing the maintenance of subsequent to-be-remembered items. These results have critical theoretical implications, and support models considering that WM relies on temporary activation within the LTM system.

There is large agreement among vision scientists that biolog-ical perception is capacity-limited and that attentional mecha-nisms control how that capacity is allocated. Despite the factthat Bayesian models generally do not include capacity limits,many researchers model perceptual attention as the result ofoptimal Bayesian inference. This inconsistency arises becausevision science currently lacks a feasible and principled com-putational framework for characterizing optimal attentional al-location in the presence of capacity constraints. Here, weintroduce such a framework based on rate-distortion theory(RDT), a theory of optimal lossy compression developed in theengineering literature. Our approach defines Bayes-optimalperformance when an upper limit on information processingrate is imposed. Here, we compare Bayesian and RDT ac-counts in a visual search task, and highlight a typical shortcom-ing of unlimited-capacity Bayesian models that is not sharedby RDT models, namely that they often over-estimate task-performance when information-processing demands are in-creased. In this study, we asked human subjects to find eitherone or two targets in a collection of distractors in a single-fixation search task. We predicted relative performance be-tween one- and two-target conditions based on both RDT andBayesian models. Performance differed between conditions ina way that was well accounted for by the capacity-limited RDTmodel but not by the capacity-unlimited Bayesian model.

In this experiment, we investigated how a robot’s violation ofseveral social norms influences human engagement with andperception of that robot. Each participant in our study (n = 80)played 30 rounds of rock-paper-scissors with a robot. In thethree experimental conditions, the robot violated a social normby cheating, cursing, or insulting the participant during game-play. In the control condition, the robot conducted a non-normviolating behavior by stretching its hand. During the game,we found that participants had strong emotional reactions toall three social norm violations. However, participants spokemore words to the robot only after it cheated. After the game,participants were more likely to describe the robot as an agentonly if they were in the cheating condition. These results implythat while social norm violations do elicit strong immediate re-actions, only cheating elicits a significantly stronger prolongedperception of agency.

Tracking one’s performance over time is essential to efficientself-guided learning but it is not clear whether young childrencan accurately monitor their past performance. Here, welooked at whether 4-6-year-olds can use the trajectory of theirpast performance to allocate future resources. Across fourexperiments (N = 274), we found that children were sensitiveto their rate of change in past performance: Children assignedto a condition in which they got better over time were morelikely to take on challenges and teach others than children inconditions where they got worse or stayed the same.Furthermore, children privileged their rate and direction ofchange more than their total or final score. These resultssuggest that young children monitor their rate of improvementand can use this information to guide their future efforts.

The practical relevance of neonatal imitation for social development has remained largely unaddressed as most studieshave been conducted in highly controlled, laboratory conditions. Utilizing the Lookit online infant experiment platform,we aim to demonstrate the feasibility of measuring neonatal imitation of caregivers in the home environment. Our between-subjects design, adapted from Meltzoff and Moore (1983), focuses on two of the most commonly studied neonatal gestures,tongue protrusion and mouth opening. Caregivers and their newborn are videotaped as caregivers model either gestureto their newborn. Coders, who are blind to condition, record newborns gesture frequencies. To analyze these data,we ultimately plan to specify a Bayesian hierarchical log-linear model testing whether the frequency of each neonatalgesture increased when caregivers modeled that specific gesture. Pilot data collection and behavioral coding are currentlyunderway and will focus on inter-rater reliability, attrition, and recruitment rates of online data collection for neonatalimitation.

The naïve utility calculus theory of early social cognitionargues that by relating an agent’s incurred effort to the expectedvalue of a goal state, young children and infants can reasonabout observed behaviors. Here we report a series ofexperiments that tested the scope of such utility-basedreasoning adopted to choice situations in the first year of life.We found that 10-month-olds (1) did not expect an agent toprefer a higher quantity of goal objects, given equal action cost(Experiment 1) and (2) did not expect an agent to prefer a goalitem that can be reached at lower cost, given equal rewards(Experiment 2a and 2b). Our results thus suggest that younginfants’ utility calculus for action understanding may be morelimited than previously thought in situations where an agentfaces a choice between outcome options.

Reasoning about analogical arguments is known to be subjectto a variety of cognitive biases, and a lack of clarity aboutwhich factors can be considered strengths or weaknesses ofan analogical argument. This can make it difficult both to de-sign empirical experiments to study how people reason aboutanalogical arguments, and to develop scalable tutoring toolsfor teaching how to reason and analyze analogical arguments.To address these concerns, we describe WG-A (Warrant Game— Analogy), a framework for people to analyze analogical ar-guments based on Bartha’s (2010) Articulation Model of ana-logical argumentation. We carry out two experiments designedto probe WG-A’s effectiveness in improving participants’ abil-ity to reason about analogical arguments and argumentation ingeneral, and argue that WG-A is a promising approach, thoughit is in need of further development.

The validity of verbal working memory depends on language experience-independent capacities. We tested how grammat-ical knowledge impacts memory in the absence of overt language production and while controlling for semantic meaningof word pair stimuli. Native English speakers (n=129) completed: (1) ratings of unattested noun-noun compounds (e.g.ice-wallet) on meaningfulness; the (2) Author Recognition Test, measuring language experience; and (3) an old/newrecognition task, where previously presented noun-noun compounds appeared in either old (ice-wallet) or new (wallet-ice)orders. Order of nouns in compounds either resembled order consistently found in English (i.e. typical noun modifier +typical head noun) or was reversed. If grammatical knowledge affects verbal working memory, consistency with natu-ral language should predict old ratings, controlling for meaningfulness ratings and old status. As predicted, participantswere more likely to rate consistent compounds as old compared to reversed. All analyses pre-registered on OSF prior toexperimenter access to data.

The quality of parent speech has been argued to impact child language growth above and beyond quantity. One potential mechanism tying online experience to long-term vocabulary development is sustained attention to targets of parent speech. We recruited thirty-five parent-toddler dyads to participate in free toy play while wearing head-mounted eye trackers. Parent speech was categorized based on its referential nature, syntax, and communicative intent. Parent referential speech positively related to both vocabulary size and online patterns of sustained attention. Speech categorized based on communicative intent also showed relations with vocabulary size and sustained attention, but specific types of speech impacting each differed. These results support the hypotheses that qualitative characteristics of parent speech relate to both long-term language growth and online sustained attention and provide tentative evidence for the broader hypothesis that sustained attention is the mechanism tying online experience to long- term language growth.

There are two strategies that can be employed to solve a problem: analysis and insight. Analysis is the incremental,conscious search for a solution, as in hypothesis testing; insight involves the unconscious restructuring of a problemrepresentation followed by the sudden, conscious realization of the solution (Aha! phenomenon). We attempted to discoverfeatures of neural activity during problem solving that could predict which type of cognitive strategy people used on eachtrial of an anagram task. We used Multivariate Pattern Analysis (MVPA) on 64-channel pre-solution EEG recording thathas been time-frequency transformed. Searchlight was employed in which neighboring time-frequency points within asliding window were used to train a Naive-Bayesian classifier across electrodes to determine the features with the bestclassification accuracy. In addition, Support Vector Machine was trained using principal components, which resulted inimproved classification accuracy than Searchlight, suggesting more distributed nature of informative features in the data.

Children often judge that strange and improbable events are impossible, whereas adults usually accept the possibility of such events. This shows that children’s reasoning about possibility is immature, but it remains unclear how children reason about the possibility of improbable events. We explore whether children use a novel event’s similarity to a known event to infer whether the event can happen. We told 4- to 6- year-olds (N=120) either ordinary or improbable facts and then asked if a related improbable event was also possible. The facts contained no causal information that could be extended to the occurrence of a similar event. Children who heard improbable facts more often agreed that similar improbable events were possible than children who heard ordinary facts. This suggests that the mere knowledge that an event can happen influences children’s beliefs about the possibility of other unfamiliar-but- similar events.

Prior laboratory research has shown the positive benefits of answering pre-questions on learning. Specifically, pre-questions have been shown to increase learning from subsequent pre-questioned material presented either in a readingor in a lecture format compared to a non-pre-questioned group. However, it is not yet clear whether these learning bene-fits translate into larger lecture-based classrooms and whether they can facilitate transfer to non-pre-questioned material.Moreover, there are few classroom studies, utilizing pre-questions, that explore these effects. We investigated the effectof pre-questions on learning during a large lecture course. Students who received pre-questions performed better on endof lecture quiz questions compared to students who did not receive pre-questions. Consistent with prior laboratory andclassroom studies, this effect was primarily for the pre-questioned information and there was no immediate effect onnon-pre-questioned information. We discuss the implications of the results for theories of learning and applications toeducation.

A Self-Organizing Map (SOM) is a type of artificial neural network. Artificial neurons in the SOM form local assembliesthat become specialized in responding to categories of stimuli. Assemblies emerge through competition and cooperationbetween artificial neurons. Here we present a SOM aimed to model autism by means of increasing cooperation betweenneurons in the map. Descriptions of local hyperconnectivity in neuronal circuits in ASD make our implementation bio-logically sound. Remarkably, the change in low-level processing of our model, led to high level atypicalities mirroringASD behavior. Increasing cooperation produced deficient organization of neuronal assemblies accounting for fragmentedrepresentations of perceptual categories, idiosyncratic use of word labels, and atypical shape bias in lexical development.The results of our model successfully matched the behavioral performance of children with ASD in a categorization task,and shed light on how to understand the atypical development of the neurocognitive profile of ASD.

We present several experiments demonstrating the efficiencyand scalability of a biologically inspired spatial representationon navigation tasks using artificial neural networks. Specifi-cally, we demonstrate that encoding coordinates with SpatialSemantic Pointers (SSPs) outperforms six other proposed en-coding methods when training a neural network to navigate toarbitrary goals in a 2D environment. The SSP representationnaturally generalizes to larger spaces, as there is no definitionof a boundary required (unlike most other methods). Addition-ally, we show how this navigational policy can be integratedinto a larger system that combines memory retrieval and self-localization to produce a behavioural agent capable of findingcued goal objects. We further demonstrate that explicitly incor-porating a hexagonal grid cell-like structure in the generationof SSPs can improve performance. This biologically inspiredspatial representation has been shown to be able to producespiking neural models of spatial cognition. The link betweenSSPs and higher level cognition allows models using this rep-resentation to be seamlessly integrated into larger neural mod-els to elicit complex behaviour.

Research on human collaboration has suggested thatknowledge diversity improves group performance in complextasks such as design, problem solving and forecasting.However, in educational settings it is important to also askwhether learning and transfer for individuals within the groupis enhanced or hindered by diversity in collaborative workgroups. We compare performance in a transportation networkdesign task for two types of collaborative groups, andcompare their performance to that of individuals. In onegroup condition (Distributed Knowledge) each dyad memberhas been trained on a different subtask of a complex jointdesign problem in advance of the collaborative activity. Thesedifferent training tasks should predispose the two dyadmembers to adopt different perspectives, issues, and designstrategies, thus generating greater cognitive diversity for thegroup. In the other group condition (Shared Knowledge) bothdyad participants experienced the same training involvingboth subtasks. Task performance results show a group versusindividual advantage in performance, but a non-significantdifference in performance between the two group knowledgediversity conditions. The group knowledge manipulation didaffect group process, as measured by time spentcollaborating, number of turns taken, and number of wordsspoken. The findings suggest that group diversity canpromote individual learning and transfer when sufficient timeis allowed for discussion and group work.

Visual comparison is a key process in everyday learning.Matlen et al. (2020) recently proposed the Spatial AlignmentPrinciple, based on the broader work of structure-mappingtheory in comparison. According to the principle, visualcomparison is more efficient when pairs are arranged in directplacement: i.e., so that the visuals are juxtaposed orthogonallyto their structural axes. In this placement (a) the intendedrelational correspondences are readily apparent, and (b) theinfluence of potential competing correspondences isminimized. Thus, this placement should make the relationalalignment maximally easy to notice. The results of a same-different task in adults supported this claim. The current studyasks whether the Spatial Alignment Principle applies inchildren’s visual comparison. 6-year-old children performed asame-different task for visual relational patterns. The resultsindicated that direct placement led to faster and more accuratecomparison, both for concrete same-different matches(matches of both objects and relations) and for purely relationalmatches.

How the mind represents event knowledge, a persons knowledge of events and situations in the world, is the subject ofcompeting theories. Proposals range from an event being represented as a linear order of activities, to a hierarchical struc-ture of scenes of related activities, or in a more fluid computational framework. Additionally, atypical event knowledgeis thought to correlate with Autism Spectrum Disorder. 140 participants (20 per event) ordered normed activity lists for80 events (e.g., taking money out of an ATM, going to a professional baseball game, baking a cake). Network analysessuggest that the temporal structure of events is rich, not strictly linear, and varies across individuals. Furthermore, wecomputed a consensus ordering for each event from participants activity sequences. We calculated deviations from thatordering for each participant, and correlated deviations with a battery of trait inventories to further investigate differencesamong individuals representations of event structure.

Is the influence of language on attention previously found in controlled, single-task lab contexts reduced or absent whenother factors (i.e. goals) influence attention, as in everyday life? The current studies examined whether language effects oneye-movements and recall emerge in richer task conditions. Experiment 1 examined English speakers use of agentive/non-agentive language during scene description on memory for the agent, similar to Fausey and Boroditsky (2011) whilealtering scene complexity and adding eye-tracking. Experiment 2 contrasted the standard describe task with one moretypical of everyday scene processing: predicting what happens next. Eye-tracking results from Experiment 1 supportan influence of language on distribution of attention. However, the absence of a significant memory difference in bothexperiments suggests that the language effect is not robust enough to have a meaningful impact on memory in rich taskconditions. Ultimately, the data suggested even the original effects are difficult to obtain.

Most words are polysemous, denoting related but distinct senses (e.g., chicken referring to an ANIMAL or to FOOD).Jager, Green, and Clelland (2016, LCN) reported facilitatory effects of polysemy on lexical processing that interacted withword frequency and type of task. We undertook a broader investigation of interactions between polysemy and severalsublexical, lexical, and semantic properties of words, to determine whether such interactions could explain inconsistenteffects of polysemy reported in the literature. Estimating degree of polysemy using dictionary sense counts, we studied theinteraction between polysemy and these other properties when predicting performance in lexical decision and semantic cat-egorization mega-studies. We observed interactions between polysemy and both lexical and semantic, but not sublexical,variables. Our results, while not replicating the exact effects reported by Jager and colleagues, highlight the importance ofdeveloping models of semantic ambiguity that take into consideration interactions with other psycholinguistic propertiesof words.

The present study examines language patterns in theformation of common ground in collaborative action tasks.Based on the classic Clark and Wilkes-Gibbs’ (1986)paradigm for object descriptions, we examined dialoguebetween pairs of participants as they work cooperatively tomaneuver a remote control car following both manner andpath instructions. Overall, we replicated Clark andWilkes-Gibbs’ (1986) results in the domain of action in thedecline of word count, verb phrases, turn taking, and numberof errors committed, with diminishing returns after one trial.However, we also document specific language reductions inpath related actions, but not in manner related actions. Wesuggest that path actions particularly depend on compositionaldescriptors of the environment, consistent with thecontemporary conceptualization of action (Barsalou, 2009).

Distributional semantic models (DSMs) are substantially var-ied in the types of semantic similarity that they output. Despitethis high variance, the different types of similarity are oftenconflated as a monolithic concept in models of behaviouraldata. We apply the insight that word2vec’s representationscan be used for capturing both paradigmatic similarity (sub-stitutability) and syntagmatic similarity (co-occurrence) to twosets of experimental findings (semantic priming and the effectof semantic neighbourhood density) that have previously beenmodeled with monolithic conceptions of DSM-based seman-tic similarity. Using paradigmatic and syntagmatic similaritybased on word2vec, we show that for some tasks and typesof items the two types of similarity play complementary ex-planatory roles, whereas for others, only syntagmatic similar-ity seems to matter. These findings remind us that it is im-portant to develop more precise accounts of what we believeour DSMs represent, and provide us with novel perspectiveson established behavioural patterns.

Past work has made conflicting proposals about the mechanisms underlying instructed reinforcement learning (RL)specifically,that prefrontal cortex, representing instruction, either biases, attenuates, or overrides learning signals in the brain. Weleverage the sensitivity of pattern-similarity analysis of fMRI data to distinguish between the qualitative features of theseaccounts. Participants learn the value of six novel stimuli after receiving false information that one is of high value. Wetrack markers of value learning in visual cortex during a value-independent perceptual judgement task presented betweenintervals of RL. We predict that with learning, the correlation between activation patterns for similarly valued stimuli willincrease. To characterize influences on learning, we examine how the rate at and direction in which these patterns changein similarity will be influenced by explicit instruction about stimulus value. This work will help us identify the principlecognitive and neural mechanisms underlying instructed RL.

Most Americans do not view climate change as an imminent threat. The present paper harnessed the power of two cog-nitive constraints essential to belief formation and revision coherence and causal invariance to guide the developmentof educational materials to foster pro-climate actions. Building on insights from philosophy, cognitive psychology, andanthropology, our materials presented questions on a range of everyday and otherwise personally relatable events to par-ticipants in 10 U.S. states with the highest level of climate skepticism. Participants answered the questions, explainedtheir answers, and received feedback featuring scientific explanations. The latter typically deviate from participants own(invoking the causal-invariance constraint), and are more coherent (invoking the coherence constraint). In support of ourapproach, although our intervention materials did not mention climate change or mitigating actions, they raised willingnessto take pro-climate actions, but did so only when the components hypothesized to enable a coherent pro-climate-actionnarrative were included.

Research suggests learning by doing yields better outcomes than passive instructional activities (e.g., reading). Currently,the attentional demands of learning by doing are not well understood, which has important implications for youngerlearners. We investigated the developmental trajectory of learning by doing with eighty-five primary students (Mage=6.64years) who listened to a lesson about insects. Participants were presented with contrasting animal-pairs (e.g., ant—pillbug)and learned about insect features. Attention to the lesson was measured as the proportion of time fixating on the lesson. Apost-test assessed recall for lesson content and transfer. First-graders exhibited comparable recall after passive and activepractice, whereas kindergarteners benefited from passive practice. Interestingly, for transfer items first-graders benefitedfrom passive practice whereas kindergarteners benefited from active practice. Transfer performance was related to learnersattention during the task suggesting that learning by doing might depend on the development of attention.

Functioning in the world requires information about objects properties. People perceive object mass using perceptualcues when the material is observable. Here, we examine how people predict an objects motion when its material isunobservable, but predictable from cues learned via category learning. When given an ambiguous object, people tend topredict properties based on the propertys propensity in the most likely category. But, recent work has found that givenan ambiguous cue, people will integrate over categories (as rational agents should) in a variety of contexts. In our study,we investigate how uncertainty in categorization affects continuous judgments in the domain of intuitive physics. Weincorporate real materials (like wood and iron) into a category learning framework and test peoples judgments about thedistance a payload travels in two scenarios before and after category learning. Our results are equivocal, but suggest thatpeople do integrate in these scenarios.

In this study, 4-year-old children were tested in an object name generalization task with a stimulus comparison design. Performance in the generalization task was correlated with performance in a vocabulary test and three executive function tasks assessing inhibition, flexibility, and working memory. Correlational analyses revealed a significant association with flexibility but not with inhibition, working memory or vocabulary test. We interpret the results in terms of a capacity to flexibly generate novel dimensions rather than inhibiting irrelevant dimensions. Individual differences in working memory and inhibition did not significantly influence performance in the word extension task. Moreover, the absence of correlation with the vocabulary performance supports the idea that children did not rely on existing knowledge to find out the relevant dimension.

Among the popular press, excessive smartphone usage is often broadcast as being associated with adverse outcomes, in-cluding greater risk taking, poor social adjustment, and impaired cognitive functioning. However, there is scant empiricalevidence that supports these claims. Our study investigated whether the duration of smartphone ownership (exposure)affects smartphone usage pattern (screen-time), and whether their interaction is associated with risk-taking behavior (Ben-thin Risk Perception questionnaire). We found that those with lower screen-time reported engaging in a higher frequencyof risky activities like vandalism of property, B = -4.80, SE = 1.65, t = -2.91, p ¡ 0.01. Screen-time was inversely asso-ciated with risk taking among individuals characterized by less exposure, B=4.66, SE=2.01, t=2.32, p=0.03. Altogether,these early findings illustrate how the impact of screen-time on real-life behaviors may not be as one-sided as mass mediaportrays.

People often endorse explanations in terms of purposes or goals (e.g., pencils exist so that people can write with them), evenwhen these teleological explanations are scientifically unwarranted (e.g., water exists so that life can survive on Earth). Inthe present research, we explore teleological endorsement in a novel domainhuman purposeand its relationship to moraljudgments. Across two studies, we find evidence that people endorse the claim that humans exist for a purpose (e.g.,to procreate, to help others) and that these beliefs relate to moral judgments against purpose violations (e.g., condemningthose who do not procreate, or do not help others). We also find evidence of a bi-directional causal relationship: teleologicalclaims about a species result in moral condemnation of purpose violations, and stipulating that an action is immoralincreases endorsement that the species exists for that purpose.

Diagrams are a potentially valuable tool for helping students understand mathematical concepts and procedures. Onetype of diagram that is sometimes used to depict mathematical relationships is tape diagrams, which depict quantitiesin continuous strips. This study investigated whether tape diagrams with different visual features differentially supportreasoning about equations, and explored whether people have preferences for tape diagrams with different visual features.Undergraduates (N = 50) were asked (1) to generate equations to correspond with tape diagrams with varying visualfeatures, and (2) to select the diagram they preferred from pairs that differed in visual features. Variations in visualfeatures (color, presence of outer lines, and position of the constant) did not affect participants success at generatingequations to correspond to the tape diagrams. However, participants displayed systematic preferences for most visualfeatures considered. Future research should examine the effects of these visual features on performance while solvingequations.

How do children reason about people presented over video chat? Video chat is a representation, like a picture; but is also a real social interaction (the partner sees and hears you). Do children understand the nuanced affordances and limitations of video chat? We tested 4-year-old children’s reasoning, asking if a person over video chat (vs. a live person; photograph) could see, hear, feel, and physically interact through the screen. Children judged that a person over video chat can see, but cannot feel nor receive an object, through the screen. The person over video chat was judged to hear more often than a photograph, but less often than a live person. Preschool children are not limited to considering a stimulus fully representational, or fully present; instead, they understand video chat as a medium that blurs the boundaries of representation and reality, allowing for a mixture of life-like affordances and picture-like limitations.

Young childrens speech pronunciations deviate systematically from adult forms. For example, onsets are often simplified(e.g., stop becomes top), unstressed syllables frequently deleted (e.g., spaghetti becomes getti), and certain segments arecommonly replaced with other ones (e.g., rice becomes wice). The current study examined how well adults and a popularautomatic speech recognition system (i.e., Siri) deal with these deviations. The same 12 children were recorded producing32 words in isolation at three ages: 2.5, 3.5, and 5.5 years. 12 adults were also recorded. These recordings were presentedto 48 young adults, 7 mothers, and Siri for transcription. All listeners performed worst with 2.5-year-old productions,and humans outperformed Siri with all ages (p ¡ 0.001). Mothers demonstrated the highest accuracy with 2.5-year-oldproductions (86%). Additionally, Siri made distinctive transcription errors with childrens speech. These errors may reflectthe systems lack of training with young childrens voices.

In this paper, we extended the DiCarlo & Cox 2007 tangled object manifold framework of object recognition to bet-ter address the unsupervised nature of category learning. We developed a novel Markov chain-based similarity metricthat formally connects aspects of manifold untangling with trace learning. Using these developments, we replaced un-observable labels and artificial category boundaries with our observable Markov chain walk based similarity metric as atheoretically grounded target for unsupervised category untangling. Further, we developed a new rationale for how neu-ronal input windows should be chosen for an untangling algorithm using this new framework. This new framework formanifold untangling and trace learning allowed us to synthesize aspects of simple cell learning, complex cell learning,and axonal development theories, into a high-level theory of how the visual cortex learns to separate object categories at acomputational level.

Visual mental imagery and visual memory appear to utilise similar brain networks. However, limited research has in-vestigated how similar the systems are in terms of capacity limits. Capacity limits of visual working memory (VWM)and visual short-term memory (VSTM) have been the focus of considerable research, but to our knowledge none has at-tempted to ascertain the number of objects that can be simultaneously imagined. This study aimed to provide estimatesof imagery capacity and explore how this relates to the capacity of visual memory. Participants completed three tasks thatexplored imagination, VWM and VSTM, respectively. Set size was manipulated similarly in each task enabling modellingof imagination and visual memory capacity. Capacity estimates were similar in the two visual memory tasks and higherthan that of imagination. The relations between these tasks are discussed alongside the theoretical implications about themechanisms underpinning imagery and visual memory.

The statement “Girls are as good as boys at math” appears toexpress gender equality, but research has shown that peopleinfer a gender difference from such statements: the group inthe complement position (boys) is judged to be superior. Arepeople aware that the syntax of these statements influencestheir judgments and do these framing effects generalize toother groups and inferences? We addressed these questions byreplicating and extending previous work, showing that (1)syntactic framing effects extend to politically chargedinferences about religious groups and terrorism, and (2) themajority of people recognize subject-complement statementsas influential in their judgments, but framing effects are foundonly in those who fail to recognize this influence. Those whodo cite this syntax as influential tend to show a reverseframing effect, suggesting they may be sensitive to the biasimplicit in such statements and consciously act to resist it.

Spacing presentations of learning items across time improvesmemory relative to massed schedules of practice – thewell-known spacing effect. Spaced practice can be furtherenhanced by adaptively scheduling the presentation of learningitems to deliver customized spacing intervals for individualitems and learners. ARTS - Adaptive Response-time-basedSequencing (Mettler, Massey, & Kellman 2016) determinesspacing dynamically in relation to each learner’s ongoing speedand accuracy in interactive learning trials. We demonstrate theeffectiveness of ARTS when applied to chemistry nomenclaturein community college chemistry courses by comparing adaptiveschedules to fixed schedules consisting of continuouslyexpanding spacing intervals. Adaptive spacing enhanced theefficiency and durability of learning, with learning gainspersisting after a two-week delay and generalizing to astandardized assessment of chemistry knowledge after 2-3months. Two additional experiments confirmed and extendedthese results in both laboratory and community college settings.

When people think about the features of scissors, they often spontaneously recall a central feature of scissors: they cutthings. They tend not to recall other features of scissors, e.g., that they have handles. The present paper posits a novelexplanation for the behavior: the features people recall first and most often reflect semantic generalizations of kinds. Arecent taxonomy of such generalizations suggests that people represent privileged links between kinds and their featuresknown as principled connections (Prasada et al., 2013). Principled connections can reflect norms, and one way to diagnosethe presence of a principled connection is to test the acceptability of sentences of the form all normal Xs have featureY, as in all normal cars have four wheels. We tested whether participants accept generalizations about the normality offeatures produced in a semantic feature production task. Two experiments provided participants with generalizations aboutfeatures listed first and most often as well as features that people list less frequently. Both experiments found that peoplereadily accepted generalizations about the normality of frequently produced features. The results corroborate the view thatprincipled connections help people recall the features of conceptual categories.

When integrating information in real time from multiplemodalities or sources, such as when navigating with the helpof GPS voice instructions along with a visual map, a decision-maker is faced with a difficult cue integration problem. Thetwo sources, in this case visual and spoken, have potentiallyvery different interpretations or presumed reliability. Whenmaking decisions in real time, how do we combine cues com-ing from visual and linguistic evidence sources? In a sequenceof three studies we asked participants to navigate through aset of virtual mazes using a head-mounted virtual reality dis-play. Each maze consisted of a series of T intersections, ateach of which the subject was presented with a visual cue and aspoken cue, each separately indicating which direction to con-tinue through the maze. However the two cues did not alwaysagree, forcing the subject to make a decision about which cueto “trust.” Each type of cue had a certain level of reliability(probability of providing correct guidance), independent fromthe other cue. Subjects learned over the course of trials howmuch to follow each cue, but we found that they generallytrusted spoken cues more than visual ones, notwithstandingthe objectively matched reliability levels. Finally, we showhow subjects’ tendency to favor the spoken cue can be mod-eled as a Bayesian prior favoring trusting such sources morethan visual ones.

We investigate the processing of inflected verbs in Bengali. The word forms involve an interaction of orthography andphonology: the 1st Person singular is formed from the 3rd Person by adding the suffix /-i/. For stem vowels [, e, , o]this causes the stem vowel to be raised. For [e, o] this is reflected orthographically, but not for [,]. We examine thisin a cross-modal priming study and an eye tracking task where an auditory first-syllable fragment is matched to eitherthe 1st or 3rd Person visual form. We show that orthography plays an important role, with mismatching forms beingless effective as primes, and fragment completion being easier for patterns with different orthography. For words withno orthographic difference, manual responses to fragment completion were at chance, but eye tracking revealed distinctmatch vs. mismatch processing. We discuss implications for roles of orthography and phonology in lexical access.

The cerebellum is classically described in terms of its role inmotor control. Recent evidence suggests that the cerebellumsupports a wide variety of functions, including timing-relatedcognitive tasks and perceptual prediction. Correspondingly,deciphering cerebellar function may be important to advanceour understanding of cognitive processes. In this paper, webuild a model of eyeblink conditioning, an extensively studiedlow-level function of the cerebellum. Building such a modelis of particular interest, since, as of now, it remains unclearhow exactly the cerebellum manages to learn and reproducethe precise timings observed in eyeblink conditioning that arepotentially exploited by cognitive processes as well. We em-ploy recent advances in large-scale neural network modelingto build a biologically plausible spiking neural network basedon the cerebellar microcircuitry. We compare our simulationresults to neurophysiological data and demonstrate how therecurrent Granule-Golgi subnetwork could generate the dynam-ics representations required for triggering motor trajectoriesin the Purkinje cell layer. Our model is capable of reproduc-ing key properties of eyeblink conditioning, while generatingneurophysiological data that could be experimentally verified.

Cardinal directions refer to the four main points of direction in geographical space: north, south, east, and west. Efficientnavigation requires some basic knowledge about cardinal directions. We evaluated developmental changes in cardinaldirection knowledge in real space. Tested in an unfamiliar indoor environment with a window view, 94 children aged 6-12years old were asked to point to North and then point to East. We proposed 7 developmental stages based on knowingthe horizontal plane of cardinal directions, the inter-relationships between them, and how to identify north using referenceframes. Our classification scheme classified all participants and was sensitive to age differences. Our results suggested thatidentifying north was more difficult than knowing the inter-relationships. Many children were not able to use an allocentricreference frame effectively. Overall, our study demonstrates the utility of our classification scheme and the importance ofevaluating cardinal direction knowledge development in children.

A key characteristic of color perception is that it both categorical and continuous. This is reflected in graded color ad-jective use. This red fruit is redder than the other red fruit sounds more natural than this red fruit is redder than the bluefruit (Kennedy & McNally, 2010). We examined the contribution of first-person sensory experience to color gradabilityunderstanding by working with congenitally blind adults. Blind (n=20) and sighted (n=15) adults rated the naturalness ofstatements describing two objects of the same color (two red mugs), dissimilar colors (red mug, blue mug) or similar col-ors (red mug, purple mug). Both groups judged redder as most natural for two red objects, least for objects with differentcolors (red/blue) and intermediate for objects with similar colors (red/purple). Color similarity had a larger effect for thesighted group. Understanding color gradability does not require first-person perception.

Behavioral data, though has been an influential index oncognitive processes, is under scrutiny for having poorreliability as a result of noise or lacking replications ofreliable effects. Here, we argue that cognitive modeling canbe used to enhance the test-retest reliability of the behavioralmeasures by recovering individual-level parameters frombehavioral data. We tested this empirically with theProbabilistic Stimulus Selection (PSS) task, which is used tomeasure a participant’s sensitivity to positive or negativereinforcement. An analysis of 400,000 simulations from anAdaptive Control of Thought - Rational (ACT-R) model ofthis task showed that the poor reliability of the task is due tothe instability of the end-estimates: because of the way thetask works, the same participants might sometimes end uphaving apparently opposite scores. To recover the underlyinginterpretable parameters and enhance reliability, we used aBayesian Maximum A Posteriori (MAP) procedure. We wereable to obtain reliable parameters across sessions (IntraclassCorrelation Coefficient ~ 0.5), and showed that this approachcan further be used to provide superior measures in terms ofreliability, and bring greater insights into individualdifferences.

Many students fail to develop adequate understanding of mathematical equivalence in early grades, which impacts lateralgebra learning. Work from McNeil and colleagues proposes that this failure is partly due to the format of traditionalinstruction and practice with highly similar problems, which encourages students to develop ineffective mental models ofproblem types (McNeil, 2014, McNeil & Alibali, 2005). In the current study, we explore students learning trajectoriesin two matched equivalence interventions. We show that, relative to an active control, the principle-based treatmentintervention gives rise to a greater number of successful learners, a designation that, in turn, leads to improved performanceon distal transfer assessments. We further demonstrate a predictive relationship between students engagement with theintervention, via workbook completion, and likelihood of becoming a successful learner. Our findings have implicationsfor early detection of learning and subsequent scaffolding for low-performing students.

Toddlers, like older children and adults, can learn cause-effect relationships between a single action and its outcome.However, causality in the real-world is more complex. We investigate whether toddlers can learn, from observing an adultsdemonstration, that a sequence of two actions is causally necessary for producing an effect. In Experiment 1, toddlers andpreschoolers (N=142; ongoing) saw evidence that a 2-action sequence was necessary to make a puzzle-box dispense asticker, before trying to get stickers themselves. Preliminary results indicate that older children produce more sequencesthan younger children. Experiment 2 (N=42; ongoing) is examining whether 1- and 2-year-olds behave differently from inExperiment 1 when the demonstration provides evidence that a sequence of actions is not necessary (specifically, that thesecond action alone is causally effective). Although preliminary, our findings suggest that the ability to accurately infercausal structure from action sequence demonstrations may develop over early childhood.

Research on active category learning—i.e., where the learnermanipulates continuous feature dimensions of novel objects andreceives labels for their self-generated exemplars—has routinelyshown that people prefer to sample from regions of the space withhigh class uncertainty (near category boundaries). Prevailingaccounts suggest that this strategy facilitates an understanding of thesubtle distinctions between categories. However, prior work hasfocused on situations where category boundaries are rigid. Inactuality, the boundaries between natural categories are often fuzzyor unclear. Here, we ask: do learners pursue uncertainty samplingwhen labels at the boundary are themselves uncertain? To answerthis question, we introduce a fuzzy buffer around a target categorywhere conflicting labels are returned from two ‘teachers,’ then weevaluate how sampling and representation are affected. We find that,relative to the rigid boundary case, learners avoid uncertainty,opting to sample densely from highly certain regions of the targetcategory as opposed to its boundary. Subsequent generalization testsreveal that the sampling strategies encouraged by the fuzzyboundary negatively affected participants' grasp of categorystructure, even outside the fuzzy buffer zone.

A scientist’s academic pursuit can follow a winding path.Starting with one topic of research in earlier career, one maylater pursue topics that relate remotely to the initial point.Philosophers and cognitive scientists have proposed theoriesabout how science has developed, but their emphasis is typi-cally not on explaining the processes of innovation in individ-ual scientists. We examine regularity in the emerging order of ascientist’s publications over time. Our basic premise is that sci-entific papers should emerge in non-arbitrary ways that tend tofollow a process of chaining, whereby novel papers are linkedto existing papers with closely related ideas. We evaluate thisproposal with a set of probabilistic models on the historicalpublications from 70 Turing Award winners. We show that anexemplar model of chaining best explains the data among thealternative models, mirroring recent findings on chaining in thegrowth of linguistic meaning.

Virtual reality environments provide valuable opportunities for cognitive scientists to investigate complex cognitive func-tions in ecologically valid environments. For example, it is unclear if visual representation of the users body is requiredto evoke optimal performance. This study examined the effects of hand representation in a virtual flight simulation usingbehavioural and biometric data. Event-Related Potentials, Event-Related Spectral Perturbations, and mental workload re-sponses were measured using wireless electroencephalography across the hand presence conditions. Workload indices andneural activity in the parietal region was not significantly affected by the presence of hands, yet lower alpha levels werefound across all cortical regions. Findings are relevant to cognitive scientists as they show that the virtual representationof hands is important as it increases task engagement, while not taxing mental workload or spatial processes in the brain.

In order to understand social relationships, humans must recognize cues of affiliation. When infants see interactionsbetween abstract, animated characters, they use imitation, helping, comforting, and exerted effort to predict who willapproach whom. Moreover, infants attend to and reach for characters who imitate other characters and those who helpothers. The present research builds on these findings and asks whether infants reach for human-animated puppets withdistinct and variable human voices who imitate, are imitated by, comfort, are comforted by, or move synchronously with aperson. At 12 months, infants reached more often for puppets who imitate a humans sound, and also for those who werenot targets of imitation. In contrast, infants did not reach more for puppets who comforted or synchronized their motionswith a human actor. By 12 months, therefore, infants show differentiated responses to different acts of social engagementby those whose social interactions they observe as third parties.

Demographic perceptionthe perception of social quantities of geopolitical scale and social significancehas been extensivelystudied in cognitive and political science (Citrin & Sides, 2008; Gilens, 2001; Herda, 2013). Regular patterns of over-and under-estimation emerge which have historically been attributed to social factors such as fear of specific minorities(Gallagher, 2003; Wong, 2007). Other work has suggested that these patterns result from the psychophysics of the percep-tion of proportions (Landy, Guay & Marghetis, 2018). A Bayesian formulation suggests that biases in the estimation ofboth social proportions and simple visual properties result from a common source: hedging uncertain information towarda prior. Similar to work done by Zhang and Maloney (2012), we present a novel lab paradigm and two experiments thatfocus specifically on manipulating uncertainty in a simple (dot estimation) task, verifying the core assumptions of theBayesian approach.

Sustained attention is a dynamic process with rich temporalstructure. Eye-tracking provides a tool for capturing rich tem-poral data relevant to sustained attention, but extracting rele-vant insights from this rich data is nontrivial. This paper stud-ies eye-tracking data collected from children, aged 3-5, per-forming the TrackIt task, a visual object tracking paradigm de-signed for studying sustained attention development in youngchildren. Building on a hidden Markov model paradigm re-cently proposed for analyzing eye-tracking data with TrackIt,we explore characterizations of participant behavior, such ascontinuously maintaining attention on an object and transition-ing attention between objects, that provide richer insights thantask performance alone. In particular, our results suggest thatimprovement in TrackIt performance that accompanies devel-opment in this age range may stem more from improved abilityto return to task after distractions, rather than from improve-ments in ability to continuously maintain attention on the task.

Errors are usually viewed as detrimental to learning. Yet, recent proposals suggest that errors may create desirable difficul-ties and thereby improve learning. We evaluated these proposals in the context of cross-situational word learning. Duringeach learning trial, adults saw two images and heard two words. In the Error1 condition, the first word was unexpectedbased on prior experience and the second was expected. The referent of the unexpected word could only be establishedafter hearing the expected word. In the Error2 condition, the expected word came first, which made it easier to learn themapping of the subsequent unexpected word. There was no difference between the conditions; however, expected wordswere only learned significantly better than unexpected words in the Error2 condition. This suggests that the structure ofthe learning environment modulates the impact of errors.

The ability to sustain attention on a target in the presence of distractors is critical for learning and development. Recentwork has suggested that labeling a target object facilitates childrens performance in tasks requiring attentional selection,with a proposed mechanism relying on the enhancement of the target representation in working memory. In this pre-registered study, we tested this hypothesis by examining the effect of label familiarity on sustained attention. If labelsinfluence how strongly targets are represented in working memory, then more familiar labels should show a larger benefitrelative to less familiar labels. We discuss the results in the context of theories of language and cognition, and theircontribution to understanding the mechanisms supporting the development of selective sustained attention.

Jurors operate as legal fact-finders, incorporating multiple pieces of evidence into their decisions. Prior work suggestsjurors may not be able to distinguish flaws in scientific evidence (Schweitzer & Saks, 2012) or properly assess the relia-bility of evidence (Thompson, 1989; Kaasa et al., 2007). However, it remains unclear how much probative value is givento individual evidence types. What prior weights do jurors place on different types of evidence and how do these predicttheir decisions? We built a Bayesian model of how people weigh individual pieces of evidence and used this to predictguilt ratings. Consistent with previous work, we found people have trouble distinguishing differences in quality amongevidence, and assign similar probative value to even flimsy types of evidence. The model also revealed individual weight-ing of evidence can exert large, and sometimes problematic effects on decisions to convict. We discuss the implications ofour findings for legal decision-making.

Speakers often violate conversational expectations by offering less information than listeners need (Grice, 1975). Althoughchildren appear sensitive to such violations as comprehenders (Gweon & Asaba, 2018; Katsos & Bishop, 2011), it isunclear how they would respond to them in a reciprocal conversational setting. Here, we ask whether children tailor theinformativeness of their speech based on the informativeness of an interlocutor in a prior interaction. In an informativenessrating task, 4- and 5-year-old children evaluated the utterances of an informative and an under-informative interlocutor.Then, in a referential communication task, roles were reversed, and children produced referential descriptions for either theinformative or the under-informative interlocutor. Results showed that although children were sensitive to conversationalviolations, they did not tailor their utterances to their interlocutors informativeness. Although preliminary, these findingssuggest that cooperative expectations in linguistic exchanges might differ from those underlying broader (non-linguistic)social action.

Intensive longitudinal data was collected through theconcurrent use of a passive experience sampling (ES)smartphone application collecting objective measures ofexperience, and an ecological momentary assessment (EMA)app collecting self-reported affect. After a week-longretention interval, participants completed a memory testgenerated from paired ES and EMA data. Participants wereasked to select the GPS location at the time of a paired targetevent from four alternatives. Correct retrieval was notpredicted by self-reports grouped by negative valence/higharousal or negative valence/low arousal. Positivevalence/high arousal reported at encoding predicted greaterprobability of incorrect responses. Conversely, positivevalence/low arousal predicted greater probability of correctidentification of target. At retrieval, choice was predicted bydissimilarities in discrete emotions between target anddistractors, suggesting the use of affect as a contextualmechanism.

Cumulative culture depends on the fidelity of learning be-tween successive generations, and the robustness with whichthe lessons of one generation apply to the problems of the next.How do humans accomplish these twin goals? We formalizesocial learning as a kind of program induction, and provide anexperimental test of a key prediction. To do this, we exploit akey fact: When humans learn from others, in addition to ob-serving inputs and outputs we often observe the process thatled to that output. For instance, when preparing a meal, wedon’t just observe a pile of vegetables and then a ratatouille.Instead, we observe a causal process that transforms those in-gredients into a finished food. Here, we use probabilistic pro-grams to represent causal processes and show that the observa-tion of an execution trace speeds up program induction, evenwhen learning from only a single example. This model pre-dicts that the inferences and behavior of people will be struc-tured by these execution traces. In two behavioral experiments,we show that human judgments and behavior are affected bythe execution trace in the systematic ways predicted by our for-mal model. These findings shed light on the mechanisms thatunderlie high fidelity social learning in humans, and unify therole of emulation and imitation in social learning.

There is evidence that the process of executing a plannedutterance involves the use of both preceding-context and top-down cues. Utterance-initial words are cued only by the top-down plan. In contrast, non-initial words are cued both bytop-down cues and preceding-context cues. Co-existence ofboth cue types raises the question of how they interact duringlearning. We argue that this interaction is competitive: itemsthat tend to be preceded by predictive preceding-context cuesare harder to activate from the plan without this predictivecontext. A novel computational model of this competition isdeveloped. The model is tested on a corpus of repetitiondisfluencies and shown to account for the influences onpatterns of restarts during production. In particular, this modelpredicts a novel Initiation Effect: following an interruption,speakers re-initiate production from words that tend to occurin utterance-initial position, even when they are not initial inthe interrupted utterance.

Misinformation promoting scientific misconceptions can spread rapidly in ways it once couldn’t, and discussions of thistrend now appear to shape nearly all discourse about polarizing topics (e.g., politics, science denial). What effects mightthis recent trend have on peoples intuitive theories of how others learn and assimilate new evidence? Furthermore, howdo these theories shape engagement in discussion of polarizing issues? To shed light on these questions, we conducteda series of exploratory studies (Experiments = 4; N = 1176) which demonstrate two key results. First, people do notthink that misinformation is more likely to influence people’s beliefs than accurate statistical information, contrary to ourpredictions. Second, and importantly, we found that the more likely someone is to say information (whether accurateor inaccurate) can change other peoples beliefs, the more likely they are to debate important social issues in an effort tocorrect their misconceptions.

Humans frequently overestimate the likelihood of desirableevents while underestimating the likelihood of undesirableones: a phenomenon known as unrealistic optimism. Previ-ously, it was suggested that unrealistic optimism arises fromasymmetric belief updating, with a relatively reduced codingof undesirable information. Prior studies have shown that areinforcement learning (RL) model with asymmetric learningrates (greater for a positive prediction error than a negativeprediction error) could account for unrealistic optimism in abandit task, in particular the tendency of human subjects topersistently choosing a single option when there are multi-ple equally good options. Here, we propose an alternativeexplanation of such persistent behavior, by modeling humanbehavior using a Bayesian hidden Markov model, the Dy-namic Belief Model (DBM). We find that DBM captures hu-man choice behavior better than the previously proposed asym-metric RL model. Whereas asymmetric RL attains a measureof optimism by giving better-than-expected outcomes higherlearning weights compared to worse-than-expected outcomes,DBM does so by progressively devaluing the unchosen op-tions, thus placing a greater emphasis on choice history inde-pendent of reward outcome (e.g. an oft-chosen option mightcontinue to be preferred even if it has not been particularly re-warding), which has broadly been shown to underlie sequentialeffects in a variety of behavioral settings. Moreover, previouswork showed that the devaluation of unchosen options in DBMhelps to compensate for a default assumption of environmentalnon-stationarity, thus allowing the decision-maker to both bemore adaptive in changing environments and still obtain near-optimal performance in stationary environments. Thus, thecurrent work suggests both a novel rationale and mechanismfor persistent behavior in bandit tasks.

How are attentional styles and spatial search strategies related? Analytical attention is directed towards focal elements,while holistic attention is distributed over the whole field. These styles bear similarities with exploitative and exploratoryspatial search strategies, where the agent either spends more time in local resource patches or covers more of the fieldand spends less time in individual patches. Moreover, both mechanisms are affected by the statistics of the environment:diffuse resources lead to exploratory search while visual crowdedness evokes holistic attention. We hypothesize that searchstrategies and attentional styles are guided by related mechanisms. To test this, we prime people with a diffuse-resourcesforaging task (exploration) or a clumpy-resources foraging task (exploitation). Priming is followed by field-dependencytasks to measure subjects attentional styles. We predict that diffuse resources create similar effects to visual crowdedness,inducing holistic attention in subjects, as well as exploration.

Contrary to decades of previous research, one recent study suggests that preschoolers can rapidly learn and transfer avariety of relational concepts (e.g., bigger than; smaller than; opposite of) when those relations are operationalized asthe beginning and ending states of causal transformations performed by agents (Goddu, Lombrozo, & Gopnik, in press).Without causal framing, children reverted from relational reasoning to object matching, consistent with findings from manyprevious (non-causal) paradigms. Here, we investigate whether three-year-olds (Experiment 1) and 24- to 30-month-oldtoddlers (Experiment 2) are able to learn and apply relational concepts in a behavioral task where they themselves mustintervene to solve a problem using relational reasoning. Results indicate that children as young as two years of age areable to rapidly learn and generalize relational concepts and use them productively to solve new problems.

This paper puts forward a Gaussian Mixture Model (GMM) foreye gaze behavior under workload and applies it to the analy-sis of gaze distributions in an automotive context. Specifically,it extends earlier work on Information Constrained Control(ICC) (Hecht, Bar-Hillel, Telpaz, Tsimhoni, & Tishby, 2019)(Hecht, Telpaz, Kamhi, Bar-Hillel, & Tisbhy, 2019) (Hecht etal., 2015) (Hecht, Telpaz, Kamhi, Bar-Hillel, & Tishby, 2018)by generating an ICC GMM derivative. We suggest a mea-sure for workload estimation based on the Kullback Leiblerdivergence (Dkl ) between tested eye gaze distributions and areference workload-free distribution. This derivative assumesdiagonal Gaussians that are distant from each other. Underthese assumptions, we achieve an analytical measure that hassignificantly fewer parameters than discrete grid-like distribu-tions (Hecht, Bar-Hillel, et al., 2019). Testing our measureon eye gazing data collected during real world driving experi-ments in a highway environment confirms the effectiveness ofthis approach.

This research aims to understand when, why, and how people monitor resources such as money, time, and calories. Over-all, we find that people monitor money more closely than time or calories, but this varies by time horizon. While time andcalories were monitored most closely over a day, monitoring of money peaked at a month. Examining several possiblemediators of engagement with resource monitoring, we find the factors that impact ones engagement with resource moni-toring varies both by resource and by time horizon. One mediating factor that seems particularly important is the numberof budget categories people create. We find the degree to which people engage in resource monitoring is positively relatedto the number of categories they form. This research has implications for any behavior that involves tracking resources,such as financial decision-making, dieting, time management, and goal pursuit more generally.

Many convolutional neural network (CNN)-based approaches are excellent functional models of visual attention, but lackcognitive and biological interpretations. In this work, I offer novel, cross-disciplinary justification for the Deep Gaze 1model, which calculates salience as a weighted average of feature maps from a pre-trained CNN. In the cognitive realm,experiments demonstrate that visual attention depends on multiple levels of real-world features (edges, text, faces). Thisis well-modeled using features from a naturalistically-trained CNN. Furthermore, neuroscience research strongly suggeststhat visual attention is computed in the superior colliculus, using information from multiple levels of the ventral visualstream; all information flow in Deep Gaze follows analogous pathways. To encourage broader adoption of this model,whose source code remains unpublished, I offer a readable implementation with minor changes for biological plausibility.It is validated on the MIT1003 dataset using features from MobileNetV2, with results comparable to the original DeepGaze.

A key challenge in language acquisition is learning morpho-logical transforms relating word roots to derived forms. Tra-ditional unsupervised algorithms find morphological patternsin sequences of phonemes, but struggle to distinguish validsegmentations from spurious ones because they ignore mean-ing. For example, a system that correctly discovers ”add /z/”as a valid morphological transform (song-songs, year-years)might incorrectly infer that ”add /ah.t/” is also valid (mark-market, spear-spirit). We propose that learners could avoidthese errors with a simple semantic assumption: morpholog-ical transforms approximately preserve meaning. We extendan algorithm from Chan and Yang (2008) by integrating prox-imity in vector-space word embeddings as a criterion for validtransforms. On a corpus of child-directed speech, we achieveboth higher accuracy and broader coverage than the purelyphonemic approach, even in more developmentally plausiblelearning paradigms. Finally, we consider a deeper semanticassumption that could guide the acquisition of more abstract,human-like morphological understanding.

Human reading behavior is tuned to the statistics of natural lan-guage: the time it takes human subjects to read a word can bepredicted from estimates of the word’s probability in context.However, it remains an open question what computational ar-chitecture best characterizes the expectations deployed in realtime by humans that determine the behavioral signatures ofreading. Here we test over two dozen models, independentlymanipulating computational architecture and training datasetsize, on how well their next-word expectations predict humanreading time behavior on naturalistic text corpora. Consistentwith previous work, we find that across model architecturesand training dataset sizes the relationship between word log-probability and reading time is (near-)linear. We next evalu-ate how features of these models determine their psychometricpredictive power, or ability to predict human reading behav-ior. In general, the better a model’s next-word expectations(as measured by the traditional language modeling perplexityobjective), the better its psychometric predictive power. How-ever, we find nontrivial differences in psychometric predictivepower across model architectures. For any given perplexity,deep Transformer models and n-gram models generally showsuperior psychometric predictive power over LSTM or struc-turally supervised neural models, especially for eye movementdata. Finally, we compare models’ psychometric predictivepower to the depth of their syntactic knowledge, as measuredby a battery of syntactic generalization tests developed usingmethods from controlled psycholinguistic experiments. Onceperplexity is controlled for, we find no significant relationshipbetween syntactic knowledge and predictive power. These re-sults suggest that, at least for the present state of natural lan-guage technology, different approaches may be required to bestmodel human real-time language comprehension behavior innaturalistic reading versus behavior for controlled linguisticmaterials designed for targeted probing of syntactic knowl-edge.

Research has suggested that social impressions of faces madeby Western and Eastern people have different underlying di-mensionalities. However, the individual level consistency, thegroup-level agreement of rater groups, and the interactionsbetween face ethnicity, rater ethnicity, and social impressiontraits remain largely unknown. In this paper, we perform alarge-scale data-driven cross-cultural study of facial impres-sions, and illustrate the idiosyncrasies and similarities behindCaucasian and Asian participants in their social impressions offaces from both ethnicity groups. Our study illustrates multi-ple interesting findings: (1) Asians rate faces lower on mostpositive traits, compared with Caucasian raters, and they havemore diverse opinions than Caucasians. (2) Caucasian faces re-ceive higher average ratings on social impression traits relatedto warmth due to the preponderance of smiles in Caucasianimages, but similar mean scores on traits related to capability,compared to Asian faces. (3) Caucasians and Asians disagreemost on capability related traits, especially on “responsible”and “successful.” Opinions on these two traits diverge moreon Asian than on Caucasian faces. Our findings provide newinsights on the nuances of cross-cultural differences in socialimpressions of faces.

Prior research has distinguished between acquiring new/related knowledge (information foraging) and restructuring col-lected data (sensemaking), demonstrating that both substantially contribute to the comprehension of unknown information.These behaviors are critical cognitive abilities that can lead to scientific success and to innovation. Yet, little is knownabout whether there are individual differences in these behaviors. We provide a paradigm to study how these cognitiveabilities are utilized as participants attempt to understand the causal structure of a fictitious islands ecosystem (e.g., Whatis making the animals sick?). Some causal structures are directly posed by the environment, and can be discovered by in-formation foraging, whereas others can only be derived by sensemaking by merging or splitting the causes and/or effectsof already acquired information. We expect to see individual differences in information foraging and in sensemaking asreflected by the type of structures reported and time spent collecting or assessing the data.

Cognitive science is often concerned with questions about ourrepresentations of concepts and the underlying psychologicalspaces in which these concepts are embedded. One methodto reveal concepts and conceptual spaces experimentally isMarkov chain Monte Carlo with people (MCMCP), whereparticipants produce samples from their implicit categories.While MCMCP has allowed for the experimental study of psy-chological representations of complex categories, experimentsare typically long and repetitive. Here, we contrasted the clas-sical MCMCP design with a linked variant, in which each par-ticipant completed just a short run of MCMCP trials, whichwere then combined to produce a single sample set. We foundthat linking produced results that were nearly indistinguishablefrom classical MCMCP, and often converged to the desired dis-tribution faster. Our results support linking as an approach forperforming MCMCP experiments within broader populations,such as in developmental settings where large numbers of trialsper participant are impractical.

Smith (2015) describes an explosion of interest in Benford’s law, that for data from many domains the first digits have a log distribution. Few studies have similarly asked whether the numbers people generate fit to Benford’s law, but recent data show a reasonable fit. This paper argues that testing for fit to Benford’s law is the wrong question for behavioural data, instead we should think in terms of a “Benford bias” in which the first-digit distribution is distorted towards Benford’s law. We propose calculating the effect size of this bias by testing a linear contrast weighted by Benford’s law. Analyses of existing data sets yielded effect sizes of 0.43-0.52. Applying this approach to a new task extended the scope of Benford bias to predicting outputs of a linear system and found an effect size of .40. Benford bias may be a ubiquitous influence on judgments and decisions based on numbers.

To stay focused on their chosen tasks, people have to inhibit distractions. The underlying attention control skills canimprove through reinforcement learning, which can be accelerated by giving feedback. We applied the theory of metacog-nitive reinforcement learning to develop a training app that gives people optimal feedback on their attention control whilethey are working or studying. In an eight-day field experiment with 99 participants, we investigated the effect of this train-ing on peoples productivity, sustained attention, and self-control. Compared to a control condition without feedback, wefound that participants receiving optimal feedback learned to focus increasingly better (f = .08, p ¡ .01) and achieved higherproductivity scores (f = .19, p ¡ .01) during the training. In addition, they evaluated their productivity more accurately (r =.12, p ¡.01). However, due to asymmetric attrition problems, these findings need to be taken with a grain of salt.

We evaluate whether BERT, a widely used neural network forsentence processing, acquires an inductive bias towards form-ing structural generalizations through pretraining on raw data.We conduct four experiments testing its preference for struc-tural vs. linear generalizations in different structure-dependentphenomena. We find that BERT makes a structural general-ization in 3 out of 4 empirical domains—subject-auxiliary in-version, reflexive binding, and verb tense detection in embed-ded clauses—but makes a linear generalization when tested onNPI licensing. We argue that these results are the strongest ev-idence so far from artificial learners supporting the propositionthat a structural bias can be acquired from raw data. If this con-clusion is correct, it is tentative evidence that some linguisticuniversals can be acquired by learners without innate biases.However, the precise implications for human language acqui-sition are unclear, as humans learn language from significantlyless data than BERT.

We propose an extension to probabilistic pragmatic models to include a dimension that allows for the modeling of argu-mentative language use. Within our extended Rational Speech Act model, argumentative strength stands for a statisticalmeasure of observational evidence which impacts a speakers utterance choice. More concretely, our model recasts speakerutility in terms of a weight parameter which varies between being purely informative and purely argumentative. We fitthe extended RSA model to empirical data from a novel production experiment. Our initial results suggest that there isroom for argumentativity on top of informativity in formalizations of pragmatic language reasoning. Crucially, we see thatthe relationship between the two is not straightforward, as the model fails to capture instances of human behavior whichare more manipulative than expected by the suggested informativity-argumentativity trade-off. All in all, our explorationprovides us with interesting insights about this relationship.

Which information is worth considering depends on how much effort it would take to acquire and process it. Fromthis perspective peoples tendency to neglect considering the long-term consequences of their actions (present bias) mightreflect that looking further into the future becomes increasingly more effortful. In this work, we introduce and validatethe use of Bayesian Inverse Reinforcement Learning (BIRL) for measuring individual differences in the subjective costsof planning. We extend the resource-rational model of human planning introduced by Callaway, Lieder, et al. (2018) byparameterizing the cost of planning. Using BIRL, we show that increased subjective cost for considering future outcomesmay be associated with both the present bias and acting without planning. Our results highlight testing the causal effectsof the cost of planning on both present bias and mental effort avoidance as a promising direction for future work.

Dispositions for prosociality undergo major changes during adolescence, a period of increased sensitivity to peer influenceand incipient internalization of societal norms. However, the proximate mechanisms favoring the development of prosocialpreferences are poorly understood. Here, we show that high-status peers affect adolescents prosocial decision making.Participants repeatedly chose to either donate money to a charity or keep it for themselves and could revise their decisionupon observing the (opposite) decisions of either a high-status or low-status peer from their classroom. Participantstended to conform to peer behavior, reversing their initial preference. Importantly, this pattern was especially strong whenobserving high-status peers, suggesting that norm signalling from high-status peers can be instrumental for the diffusionof prosocial behavior. Using a novel incentivized paradigm in a naturalistic setting, we provide experimental evidencefor the role of high-status peers in the development of prosocial preferences and outline a potential path for interventionsaimed at spreading prosocial norms.

When communicating, people adapt their linguistic representations to those of their interlocutors. Previous studies haveshown that adaptation also works at the semantic level, with listeners aligning their interpretations of vague expressionssuch as quantifiers to those by a certain speaker. While adaptation has been found to arise by passive exposure to the inter-locutors linguistic representations, we hypothesize that actively seeking information could boost this effect. In particular,asking clarification questions can be helpful to reduce the uncertainty about someone elses interpretation. We focus on thegradable adjectives big and small and show that, in line with previous findings, speakers can align their representations tothose by their interlocutors. Moreover, this effect is boosted when people are given the possibility to ask questions. Thoughparticipants can generally ask for useful information, we observe that this ability improves as the interaction progresses.

Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently andthat this exploration allows them to learn. While much work has gone into developing methods for exploration in machinelearning, artificial agents have not yet reached the standard set by their human counterparts. In this work we propose usingDeepMind Lab as a platform to directly compare child and agent behaviors and to develop new exploration techniques.We tested 60 children aged 4-6 examining two conditions that emulate how current reinforcement learning algorithmslearn using dense and sparse rewards and the children are then asked to find a goal in various mazes. These tasks providedata that can easily be compared to algorithms and we evaluate turn-by-turn moves the children do to what the Intrinsic-Curiosity-Module and Depth-First-Search algorithm do in the same exact maze. We show specifically where and whenchildren differ from the algorithms.

The capacity for Theory of Mind (ToM) allows us to repre-sent others’ understanding of the world independently fromour own and then explain and predict their actions in terms oftheir understanding. Researchers have often focused on tryingto find evidence for an implicit theory of mind system: one thatemerges early in human ontogeny and operates mandatoriallyin adults. In this paper, we ask how the recent methodologi-cal push towards replication can be used as a tool that bearson a key theoretical distinction in implicit Theory of Mind,namely the distinction between factive and non-factive ToMrepresentation. Unlike other meta-analyses, our primary inter-est is not the overall replicability of theory of mind findings.Instead, we ask whether the replicability of implicit theory ofmind tasks depends in part on whether they measured factiveor non-factive ToM. We find that, to the extent that there isreplicable and robust evidence for implicit ToM, that evidencelargely comes from tests that investigated factive ToM repre-sentations. This analysis is a proof of concept of the broaderpotential for using replication attempts to ask theoretically mo-tivated questions.

The assessment of how a deceptive message is produced in dif-ferent languages has received little attention, with the majorityof studies focused on the English language. Moreover, thereis no agreement about the stability of linguistic clues of deceitacross different languages. In this paper, we address this issueby analysing both theory-driven linguistic markers of decep-tion (cognitive load hypothesis) and standard text categorisa-tion features. After compiling a multilingual corpus of bothhonest and deceitful first-person opinions regarding five differ-ent topics, we assessed the cross-language applicability of fourdifferent features sets in within-topic, cross-topic and cross-language binary classification experiments. Results showedpromising classification performances in all the three experi-ments with few exceptions. Interestingly, linguistic markersof deceit linked to the cognitive load hypothesis exhibited thesame trend in the two languages under investigation and thecross-language evaluation highlighted their usefulness in spot-ting deceit between different languages.

Language acquisition researchers have demonstrated that human infants tend to learn some sound classes before others.However, similar biases acting on classes of sounds have not yet been demonstrated in a birdsong model. Here, I detail thelearning strategies of four zebra finches at both the level of the song and the level of the syllable. Although some syllables,namely introductory notes and transient chirps, appear to emerge along regular timeframes, the learning strategy chosenby the bird also has a strong influence on each syllables ontogeny. Syllables imitated earlier in a songs development tendto be imitated more accurately than syllables derived later in the learning process.

When humans revise their assumptions based on evidence, theyprocess information on the (un)certainties of the situation. Thisprocess can be modeled by a (mathematically optimal) Bayes-ian reasoning strategy. Humans typically deviate from thisnorm and apply heuristic strategies, often by only partially pro-cessing the available information (e.g., neglecting base rates).From a perspective of ecological rationality, such heuristicspossibly constitute viable cognitive strategies in certain situa-tions. We investigate the adequacy of a cognitively plausibleheuristic strategy, which amounts to approximately averagingthe probability information on prior hypotheses and evidence.We compare this strategy to optimal Bayesian reasoning and toinformation-neglecting strategies by exploring the situationalparameter space (number of hypotheses, prior and likelihoodvalues). Finally, we frame this in the context of teachers’ diag-nostic judgments on students’ potential misconceptions (pri-ors) based on students’ solutions (evidence) and interpret theresulting accuracy of decisions within the ecology of informalstudent assessment.

Frequency plays a central role in human cognition, and in lan-guage processing in particular. There is growing evidence thatacceptability judgements are shaped by the statistics of theinput. In this paper, we focus on a type of constraint opera-tive in long-distance dependencies (e.g. wh-questions, relativeclauses, topicalizations, etc.) which has been claimed to re-sult from verb subcategorization frequency effects. We takea closer look at this hypothesis, and conclude that it does notaccount for the sentence acceptability contrasts. Rather, theevidence we find suggests that the acceptability of these depen-dencies hinges on clause-level semantic-pragmatic factors.

Navigating the social world relies upon the human capacity for mentalizing, or attributing intentions to social agents.Unfortunately, currently available commercial robots still lack such awareness of human intentionality. Building uponrecently-proposed Bayesian models of Theory of Mind (ToM), we propose a ToM model that can handle intention inferencein dynamic, fast-changing environments like a hospital, where staff have to attend to objectives and emergencies as theyarise. Our model infers and maintains a distribution over possible intentions, and uses the posterior predictives to forecastfuture trajectories, which is essential for robot motion planning. We show that our model performs excellently at inferringthe intentions and trajectories of human players controlling a nurse agent in a simulated environment. This work lays thefoundation for robots that can co-work with humans in dynamic, social environments with high-stake goals.

We present a novel framework for building pragmatic artificialagents with explicit and trainable semantic representations, us-ing the Rational Speech Act model. We train our agents onsupervised and unsupervised communication games and com-pare their behavior to literal agents lacking pragmatic abilities.For both types of games pragmatic but not literal agents evolvea mutual exclusivity bias. This provides a computational prag-matic account of mutual exclusivity and points out a possi-ble direction for solving the mutual exclusivity bias challengeposed by Gandhi and Lake (2019). We find that pragmaticreasoning can cause the bias either by promoting lexical con-straints during learning, or by affecting online inference. In ad-dition we show that pragmatic abilities lead to faster learningand that this advantage is even stronger when meanings to becommunicated follow a more natural distribution as describedby Zipf’s law.

Propositional accounts assume sentences are encoded in terms of a set of arguments bound to role-fillers in a predicate,but they never specify how the role representations form in the first place. Dennis (2005) shows an alternative way tocapture role-information based on simple associations derived directly from experience in the Syntagmatic-Paradigmatic(SP) model. We argue that the evidence for the propositional view is not well-founded and explore the possibility for apure associative encoding of proposition-like information. We differentially manipulate overlap in target and distractorsentences, embedded in narratives, and directly place the propositional account against the SP view. Our first experimentprovides some evidence for an SP account, however the second experiment supports the propositional view. Our finalexperiment provides results that are difficult to explain with either account. Overall, our results support the propositionalview and show mixed evidence for the SP account.

Teamwork in Team Space Fortress, a real-time cooperative task, was studied by analyzing the performance of participantspaired with different partners. To defeat the fortress, a player taking the role of bait approaches within the fortress rangeof fire causing the fortress to lower its shield to fire, thereby becoming vulnerable to attack by a partner playing therole of shooter. A novel design exchanging partners within four person groups allowed the identification of adaptationsand isolation of individual contributions to team performance. Team performance was determined by factors at bothindividual and team levels. Using subjective similarity rankings collected on Amazon Mechanical Turk, we constructedhigh-dimensional embeddings of similarity between team trajectories. Results showed that team members who adaptedmost, led to improved team performance. In re-pairings of partners better individual performance did not necessarily leadto better team performance again supporting the need for adaptivity in human machine teaming.

Memory is reliably enhanced for information read aloud compared with information read silentlythe production effect.Three preregistered experiments examined whether the production effect arises from a time-consuming recollective processoperating at test that benefits items that were produced at study. To accomplish this, participants were required to respondwithin a short deadline under the assumption that a time-consuming recollective process would be less able to operatewhen less time is available. If so, the production effect under speeded responding instructions should be reduced relativeto a standard nonspeeded condition. Results generally supported this prediction. However, even under speeded respondinginstructions, there was a robust production effect, potentially suggesting that other, more rapid, processes also contributeto the production effect.

This paper uses the signal detection theory (SDT) to investigate the contribution of visual information to two monitoring-dependent functions, metacognitive awareness of errors and error corrections. Data from two experiments show that complete removal of visual outcome results in a mild decrease in error awareness and a much more significant decrease in correction rates. Partially restoring visual information by including positional information (as in masked password typing) causes a modest but statistically significant improvement in correction performance. Interestingly, participants treat the change to the quality of information differently across the tasks, with more conservative behavior (avoiding false alarms) in the correction task. These findings show the SDT’s ability to quantify, in a graded manner, the contribution of specific types of information to monitoring in complex tasks, while also providing additional information about how participants handle the change to the quality of information in a task-dependent manner.

The ability of humans to quickly identify general conceptsfrom a handful of images has proven difficult to emulate withrobots. Recently, a computer architecture was developed thatallows robots to mimic some aspects of this human ability bymodeling concepts as cognitive programs using an instructionset of primitive cognitive functions. This allowed a robot toemulate human imagination by simulating candidate programsin a world model before generalizing to the physical world.However, this model used a naive search algorithm that re-quired 30 minutes to discover a single concept, and becameintractable for programs with more than 20 instructions. Tocircumvents this bottleneck, we present an algorithm that emu-lates the human cognitive heuristics of object factorization andsub-goaling, allowing human-level inference speed, improvingaccuracy, and making the output more explainable.

The Hot Stove Effect is a negativity bias resulting from theadaptive character of learning. The mechanism is that learn-ing algorithms that pursue alternatives with positive estimatedvalues, but avoid alternatives with negative estimated values,will correct errors of overestimation but fail to correct errorsof underestimation. Here we generalize the theory behind theHot Stove Effect to settings in which negative estimates do notnecessarily lead to avoidance but to a smaller sample size (i.e,a learner selects fewer of alternative B if B is believed to be in-ferior but does not entirely avoid B). We demonstrate formallythat the negativity bias remains in this set-up. We also showthat there is a negativity bias for Bayesian learners in the sensethat most such learners underestimate the expected value of analternative.

Imitation plays a key role in learning cultural knowledge. Young children imitate human models as well as humanoidrobots, even when their actions are clearly non-functional to achieve a given goal. This so-called overimitation is possiblymotivated by the desire to socially affiliate. This study clarifies the impact of social cues (greeting, eyes, friendly voice)and smooth, dynamic body motion of humanoid robots on rates of overimitation. In one condition, we remove all socialcues. In another condition, we change the dynamics of robot movement to be less biological. Overimitation rates will becompared across all three conditions (social & biological, non-social & biological, non-social & non-biological) to learnmore about important model characteristics that support cultural learning. Children aged 5-6 participated in this study. Wediscuss results and implications for using humanoid robots in interactive settings with children.

A popular dual systems theory of category learning argues thatthe structure of categories in perceptual space determines themechanisms that drive learning. However, less attention hasbeen paid to the nature of the perceptual dimensions definingthe categories. Researchers typically assume that there is adirect, linear relationship between experimenter-definedphysical input dimensions and learners’ psychologicaldimensions, but this assumption is not always warranted.Through a set of simulations, we demonstrate that, based on thenature of prior experience, the psychological representations ofexperimenter-defined dimensions can place drastic constraintson category learning. We compare the model’s behavior toseveral human studies and make conclusions regarding thenature of the psychological representations of the dimensionsin those studies. These simulations support the conclusion thatthe nature of psychological representations is a critical aspectto understanding the mechanisms that drive category learning.

We present a general framework for building formal models of naturalistic memory-based decision making. Our frame-work implements established theories of memory search and decision making within a single integrated cognitive system,and uses computational language models to quantify the thoughts over which memory and decision processes operate. Itcan thus describe both the content of the information that is sampled from memory, as well as the processes involved inretrieving and evaluating this information in order to make a decision. The models within our framework can be fit torecall and choice data, and can quantitatively predict choice probability, length of deliberation, retrieved thoughts, andthe effects of decision context. We showcase the power and generality of our framework by applying it to study riskperception, consumer behavior, financial decision making, ethical decision making, legal decision making, food choice,and judgments about well-being, society, and culture.

A central canon in theory of mind research is that between theages of three and four a drastic performance difference inchildren’s understanding occurs. However, the reason for the‘three to four shift’ has yet to be settled. One account, Theoryof Mind Mechanism (ToMM) theory (Leslie, 1994), posits thatchange in inhibitory power can account for this difference. Thisis supported by a recent computational implementation of thetheory, showing that differences in inhibitory power canaccount for age differences at an aggregate level (Wang,Hemmer, & Leslie, 2019). However, as Baker et al. (2016)point out, established findings are entirely based on group-aggregated findings, yet computational and developmentalprocesses do not take place in the ‘aggregated mind’. Whatremains largely unexplored is what happens at the level of theindividual child. Here we combine the computationalimplementation of ToMM with data from Baker et al., 2016,who assessed longitudinal developmental change in Theory ofMind performance by repeated testing of individual child overthe three-to-four shift period on standard ‘Sally and Anne’ falsebelief tasks, to obtain a cumulative record for each child.Specifically, we found that children’s age was not directlyinformative of developmental change in theory of mindreasoning. Instead, the main contributor to theory of mindperformance at the individual learner level is inhibitory power.

Shared book reading positively effects language development, yet the causal pathways of this relationship are not un-derstood. Evidence shows that the book complexity modulates caregiver talk, but the link between the book linguisticcomplexity and child syntactic development remains unclear (Noble, et al. 2017). This project describes the speech gen-erated during book reading to see how it differs from typical child-directed speech and whether the picture-book sentencecomplexity is present in the speech that children hear. 10 families with children aged 30-37 months (MBCDI raw vocab-ulary 350-675 out of 680 total) recorded 6-12 picture-book reading sessions in their homes. The books were controlledfor word length (short: 125 words vs long: 1472 words) and syntactic complexity according to the 8 categories analyzedin Montag (2019): complex (17 tokens) vs. simple (0-7 tokens). The caregiver and child speech syntactic complexitymodulation as a function of picture-book syntactic complexity will be discussed.

In this study we show that a particular form of neuro-stimulation can affect face recognition skills impairing participantsperformance on a face recognition task without affecting the composite face effect. Using a Face-Matching task (n=48)traditionally used to study the composite face effect (better recognition of the top half of an upright face when in compositewith a congruent vs and incongruent bottom half) we confirm that anodal transcranial Direct Current Stimulation (tDCS;double-blind and between subjects study) delivered over the left DLPFC at Fp3 (10 mins at 1.5mA) affects overall facerecognition performance for upright faces. But no effect of the tDCS was found on the composite face effect itself. Weinterpret our results in the light of previous literature on the tDCS effects on perceptual learning and face recognition,suggesting that different mechanisms are involved in the face inversion effect and the composite face effect.

The increase of misinformation in the public sphere over thepast decade represents an urgent societal issue, given thechallenge of distinguishing veridical facts from false ormisleading information. The present experiment’s resultsindicate that people are reliant on numerical information in theirdetermination of whether a statistic related to global warming isrepresentative or misleading. Of particularly practicalsignificance, the results also demonstrate that showingparticipants a mixed set of revealing and misleading globalwarming statistics leads to an increase in global warmingacceptance, rather than sowing confusion (or some sense that alldata are equally dubious or compelling). Replicating priorresults, nationalism and global warming acceptance are in anegative relationship. We also describe the background, design,and assessment of a curriculum intended to help the generalpublic better distinguish between representative and misleadingstatistics about anthropogenic climate change. The findingshighlight numerically-driven inferencing as a useful paradigmfor the assessment of information relating to global warming andenvironmental risk.

Do we need to reflect on our own perceptual experiences to understand what another person is seeing/hearing? Sighted(n=18) and congenitally blind (n=18) participants listened to scenarios describing sighted or blind observers looking at orhearing another person (target). Participants rated the likelihood that observers would know features of the target (e.g., age,gender, eye/hair color). We manipulated distance of observer from the target (nearby versus far) and duration of perceptualexperience (extended versus brief). Blind and sighted groups agreed on features easiest to discern (e.g. hair easier than eyecolor), although blind participants judgments about vision were more variable. Both groups judged nearby and extendedperception more likely to result in knowing. For seeing experiences, blind participants judgments were more influenced byduration, whereas sighted participants by distance. Linguistic communication is sufficient for discovering basic variablesgoverning perception (i.e., distance, duration), but first-person experience calibrates weighting of the variables.

Numerous experiments have suggested that extreme outcomesare disproportionately influential when we make decisions in-volving risk, but there is less consensus on what it actuallymeans to be extreme. Existing accounts broadly fall into twocategories: those that suggest that the best and worst outcomesare uniquely influential and those that suggest that outcomesbecome more influential with increasing deviation from thecentre of the distribution. We conducted two experiments thataimed to tease apart these explanations. Although there wassome evidence that the distance from the centre influencesmemory, neither account was able to fully explain the choicesmade by participants. This finding has implications for the vi-ability of these explanations as well as for the generalisabilityof the effect and the interpretation of the method used to assessmemory.

We are often confronted with new causal information aboutthe world, such as what causes a disease. What we think weknow may influence if and how we choose to use this new in-formation. Yet as prior work has shown, we are not alwayssuccessful at evaluating our own knowledge. We explored howhelping people better understand what they know about a do-main can influence their ability to use new causal informationin a decision-making context. Participants self-assessed theirknowledge (Experiment 1) or completed an objective assess-ment of their knowledge (Experiment 2) of diabetes, beforemaking diabetes-related decisions, either with or without newcausal information. Without a knowledge assessment, partic-ipants were less accurate with new causal information com-pared to without such information, replicating previous work.However, reassessing their knowledge increased participants’decision-making accuracy with causal information. We dis-cuss why helping people realize the limits of their causal un-derstanding may make them better supplement it with new in-formation.

Physics education and psychology research have found novices struggle to accurately predict the trajectory of objects,and perception research has found people cannot perceptually differentiate between plausible and implausible collisionoutcomes. Prior research focused on single force interactions, we explored predictions and perception of both one andtwo force interactions. Participants (N = 111) drew predicted paths of balls acted upon by a single force, two forcesacting simultaneously, and two forces acting sequentially. Paths were categorized into: correct, curved, single forcedominant, inaccurate angle, first force dominant, and recent force dominant. Participants also made perceptual naturalnessand animacy ratings for animations portraying accurate solutions and high frequency alternate conceptions. Preliminaryresults suggest participants were accurate for forces aligned on one dimension, and less accurate for forces not aligned onone dimensionparticipants anticipated curved paths, paths taking an inaccurate angle, and paths aligned with only one ofthe forces.

There is conflicting evidence regarding the effect of acute physical exercise on peoples ability to memorise declarativeinformation. Some studies have found that exercising before learning improves memorisation, while others have foundan adverse effect. We measured memorisation in 70 recruits for the Special Forces unit of the Dutch army during theirfirst week of training. Recruits used a computer-guided learning system to study the names of locations on a map directlybefore and directly after a high-intensity speed march. In the learning session following the speed march, responses werefaster but less accurate than before, particularly at the start of the session. We fitted a computational cognitive model ofhuman memory to the responses made in each learning session to obtain a continuous index of memorisation. This indexshowed a small improvement after the speed march, suggesting that memory representations formed after high-intensityphysical exercise were slightly more stable.

Population size has been proposed to promote cumulative culture in humans. Experimental evidence from adult humanssuggests that this may be due to the potential for combining beneficial information from multiple models. However, it ispossible that such combinatory social learning requires cognitive capacities restricted to adult humans. In our task, childrenaged 5-10 years watched two models consecutively search a 3x3 grid for rewards. Models revealed different correct andincorrect reward locations. This information could be used by the child to maximise their own score on the same task. Wewere interested in childrens ability to select rewarded locations, and avoid unrewarded ones, revealed by both models. Wealso manipulated the spatial and temporal displacement of the information available. Childrens performance on the taskimproved with age. Most children could outperform the mean score of the two models, but outperforming the combinedscore occurred in only limited circumstances.

Computational cognitive modeling offers a principled inter-pretation of the functional demands of cognitive systems andaffords quantitative fits to behavioral/brain data. Typically,cognitive modelers are interested in the fit of a model withparameters estimated using maximum likelihood or Bayesianmethods. However, the set of models with known likeli-hoods is dramatically smaller than the set of plausible gen-erative models. For all but some standard models (e.g., thedrift-diffusion model), lack of closed-form likelihoods typi-cally prevents using traditional Bayesian inference methods.Employing likelihood-free methods is a workaround in prac-tice. However, the computational complexity of these methodsis a bottleneck, since it requires many simulations for each pro-posed parameter set in posterior sampling schemes. Here, wepropose a method that learns an approximate likelihood overthe parameter space of interest by encapsulation into a convo-lutional neural network, affording fast parallel posterior sam-pling downstream after a one-off simulation cost is incurredfor training.

The action order of most everyday activities is only weaklyconstrained: When setting the table, for example, the orderin which the items are placed on the table does not matter ifall required items are on the table eventually. Little is knownabout how humans deal with weakly constrained sequences.Consistent with research on local optimality of human behav-ior and the “law of less work”, we propose that the order ofweakly constrained sequences is not chosen arbitrarily but dueto preferences, with the overall goal to minimize cognitive andphysical effort. We implement and validate a stepwise-optimalmodel for table setting, revealing ordering preferences basedon distance, functional relations between items, and reachabil-ity. The model’s success has implications concerning actionorganization in weakly constrained sequences as well as con-trol of action sequences and provides further evidence on thequestion of global vs. local optimality of human cognition.

Communication is highly overloaded. Despite this, even young children are good at leveraging context to understandambiguous signals. We propose a computational shared agency account of signaling that we call the Imagined We (IW)framework. We leverage Bayesian Theory of Mind to provide mechanisms for rational action planning and inverse actioninterpretation. In order to expand this framework for communication, we first treat signals as rational actions. We thenincorporate our rich understanding of intuitive utilities to constrain the scope of affordable actions. Finally, we treatcommunication as a cooperative act, subject to constraints of maximizing a shared utility. We implement this modelin two completely different behavioral psychology works to demonstrate the generality of the IW under different typesof uncertainty in cooperative communication. Additionally, we demonstrate that the IW outperforms multiple baselinemodels in a novel task across a series of simulation conditions.

How are exceptions to a generalization learned? 20 participants were exposed to a mini-artificial language in whicheach word (prefix + wordstem) was associated with a unique image. One of two prefixes generalized probabilistically:it appeared with 40 stems associated with faces and 8 exceptions, which were associated with scenes. The other prefixoccurred with 8 faces and 8 scenes. The prefixes and the image categories (faces vs. scenes) were counterbalanced acrossparticipants. Participants performed a 2 alternative-forced choice task on all items, with feedback, over 6 repeating blocks.Results show that image-word pairs that included the generalizable prefix were learned better than those which appearedwith the other prefix, despite having 48 items in the first class and 16 in the other (d = 1.28, d = 0.80, p = 0.023). Weinvestigate the neural representation of these words and how they change over the course of learning.

Statistical learning is a powerful mechanism that allows us torapidly extract structure from the environment. However,nuances of what structure is extracted—for example, whetherreliable groups are stored without knowledge of theirconstituent item order—are not well understood, leaving uswith open questions about how this mechanism supportsbehaviour. Here, we extend prior work on the representation ofstatistical structure by asking what specific aspects of structurematter for memory judgments. We consider three candidatesfor memory representation: transitional probability, order-independent group information, and position tags. Participantswatched a stream of shape triplets and then completed arecognition memory test designed to isolate contributions oftransitional probability, group, and position. We demonstratethat although memory for transitions alone would be sufficientfor knowledge of triplets, participants showed evidence ofrepresenting both transitional probability and group. Our datahighlight statistical learning as a mechanism enablinggeneralization across experiences.

A number of questionnaire based psychometric testing frameworks are globally for example OCEAN (Five factor) indi-cator, MBTI (Myers Brigg Type Indicator) etc. However, questionnaire based psychometric tests have some known short-comings. This work explores whether these shortcomings can be mitigated through computer-based gaming platforms forevaluating psychometric parameters. A computer based psychometric game framework called Antarjami has been devel-oped for evaluating OCEAN (Five factor) indicators . It investigates the feasibility of extracting psychometric parametersthrough computer-based games, utilizing underlying improvements in the area of modern artificial intelligence. The can-didates for the test are subjected to a number scenarios as part of the computer based game and their reactions/responsesare used to evaluate their psychometric parameters. As part of the study, the parameters obtained from the game werecompared with those evaluated using paper based tests and scores given by a panel of psychologists. The achieved resultswere very promising.

Humans comprehend natural language sentences in real time, processing the elements of each sentence incrementally withimmediate interpretation, while working within the limitations of general cognitive abilities. While much research hasbeen devoted to human sentence comprehension, a detailed computational theory of how this is done has been lacking.In this work we explore some fundamental principles of human sentence comprehension, propose a novel computationaltheory of knowledge representation and incremental processing to comprehend sentences using general cognitive abilities,and discuss results of an implementation of this theory in a robotic agent. We then explore the theorys implications forfuture work in various areas of cognitive science.

Decision making (DM) requires the coordination of anatom-ically and functionally distinct cortical and subcortical areas.While previous computational models have studied these sub-systems in isolation, few models explore how DM holisticallyarises from their interaction. We propose a spiking neuronmodel that unifies various components of DM, then show thatthe model performs an inferential decision task in a human-likemanner. The model (a) includes populations corresponding todorsolateral prefrontal cortex, orbitofrontal cortex, right inferiorfrontal cortex, pre-supplementary motor area, and basal ganglia;(b) is constructed using 8000 leaky-integrate-and-fire neuronswith 7 million connections; and (c) realizes dedicated cognitiveoperations such as weighted valuation of inputs, accumulationof evidence for multiple choice alternatives, competition be-tween potential actions, dynamic thresholding of behavior, andurgency-mediated modulation. We show that the model repro-duces reaction time distributions and speed-accuracy tradeoffsfrom humans performing the task. These results provide be-havioral validation for tasks that involve slow dynamics andperceptual uncertainty; we conclude by discussing how addi-tional tasks, constraints, and metrics may be incorporated intothis initial framework.

We report on findings from a pupillometry study that investigated auditory language comprehension in adults. Specifically,we assessed the participants Big Five traits and correlated them with changes in pupil size in response to socio-culturalclashes violating common gender stereotypes, such as I always buy my bras at Hudsons Bay spoken by a male speaker.Morpho-syntactic errors, such as She usually drive (as opposed to drives) her car slowly, and semantic anomalies, such asPeople often read heads (as opposed to books), were included as controls.Results obtained from 88 native speakers of North American English suggest that the processing of different kinds oflinguistic clashes is correlated with different Big Five traits. The results expand on findings in Hubert and Jrvikivi (2019),and add support to theories of linguistic comprehension in which extra-linguistic variables are considered early in theprocess (see e.g. Van Berkum et al., 2008, 2009).

Understanding the computational mechanisms enabling visuospatial reasoning is important for studying human intelli-gence as well as for exploring the possibility of introducing human-like reasoning into artificial intelligence systems. Inour work, we investigate how a collection of primitive image processing operations can be combined into different co-herent strategies for solving a range of visuospatial reasoning tasks. We evaluate our approach on 20 subtests from theLeiter International Performance Scale-Revised (Leiter-R). Through our computational experiments, we show that withonly four primitive operations similarity, containment, rotation, and scaling we can form strategies that solve, to differentdegrees of success, at least portions of 17 of the 20 subtests. These results lay foundations for our future work to study howintelligent agents can learn and generalize strategies from simple task definitions in order to perform complex visuospatialreasoning tasks.

Studying the mechanisms and trajectories of child development continues to be of critical importance, especially in thecontext of fast-changing digital environments, ubiquitous screens, and ever-increasing permeations of technology intoour lives. In this work, we study historical changes in the visual complexity of information presented in different mediacategories, including an eighty-year history of Disney movies and a forty-six-year history of NBC news programs andtelevision commercials. Our analyses include metrics of static visual variance in single frames as well as several metricsof visual change over time. By performing similar analyses on a dataset of egocentric videos, we compare trends in digitalmedia with data that more closely resemble real-life visual experiences. Understanding the visual characteristics of themedia we consume is an important step towards further investigating the effects that these characteristics might have onour perception, attention, and learning, especially in young children.

Vowels in words have been associated with specific meanings in sound symbolism (Hamano, 1998; Newman, 1933;Sapir, 1929). The purpose of this study was to examine whether each vowel individually involves physical and emotionalmeanings. Six-hundred and thirteen participants (482 females; M 16.97) rated 5-point semantic differential scales (size,distance, thickness, extent, weight, height, depth, preference, arousal, and familiarity) to presented Japanese vowels (a, i, u,e, and o). Results showed that the size, extent, and thickness of a, u, and o were significantly higher than i and e, whereasthe preference and familiarity of a was higher than the others. These results were consistent with previous findings towhich vowels in sound-symbolic words were associated with physical (i.e., size, extent, and thickness) and emotional (i.e.,preference) evaluations. Our findings suggest that each vowel itself could individually contribute to specifically physicaland emotional evaluations.

Previous research has shown that category expectations can improve recall, by reducing absolute average error (e.g. Hut-tenlocher, et.al., 1991; Hemmer & Steyvers, 2009), particularly when expectations are consistent with studied information.However, studied information that is expectation-inconsistent may also boost memory (e.g. Sakamoto & Love, 2004).Using a cued-recall task, we manipulated the degree to which studied object-color pairs aligned with peoples (N=29)expectations to explore the role of expectations in delayed recall. Our preliminary results show greater recall accuracyfor expectation-consistent items (e.g. yellow bananas) compared to expectation-inconsistent (purple bananas), and no-expectation items (yellow toothbrushes). However, there was no difference in accuracy between expectation-inconsistentand no-expectation items, nor was there a difference between weak and strong expectation-inconsistent items (orangish-yellow and purple bananas, respectively). This preliminary work shows that in delayed recall, the benefit of categoryexpectations might not extend to instances when studied information is misaligned with those expectations.

Changes in students learning approaches/attitudes when transitioning from high schools to universities is an importanttopic in Japanese higher education researches. In previous Japanese research, case studies have discussed students learningexperiences and attitudes in high schools and universities. However, most of them only discussed the difference and thesimilarity between high school and university and did not suggest the ways of connecting two different or similar learningsystems. The present study conducted surveys using two questionnaires that examined first-year undergraduate studentslearning experience in high school, learning attitude at the start of the semester, and learning experience and attitude at theend of the semester. The analysis of the startend of the semester suggests there were two different learning attitudes: onethat is continued from high school and difficult to be affected by the learning experience in university and the other that ischangeable through active and communicative learning experiences in university.

We investigated whether capuchin monkeys could use information about rewarded and unrewarded stimuli such that chain-ing of their response patterns would in principle generate increasingly successful performances, indicative of potential forcumulative culture. Two populations of tufted capuchin monkeys were tested using a touchscreen stimulus-selection taskrequiring subjects to learn the strategy of repeating rewarded, and avoiding unrewarded selections following demonstra-tions of varying success. Although capuchins outperformed demonstrations of chance-level performance (simulating per-formance of a nave individual), they did not consistently outperform demonstrations of above-chance-level success. Thissuggests that, in a social transmission scenario, the accumulation of beneficial information over successive transmissionevents would be relatively limited. Despite mastering the task contingencies, the capuchins did not use the informationoptimally, limiting the potential for cumulative culture. Our data may provide insights into factors constraining cumulativeculture in the natural behaviour of non-humans.

Past research indicates that humans can infer hidden causesfrom covariational evidence, and readily use temporal informa-tion to infer relationships among events. Here we explore a set-ting in which people can attribute events to a common hiddencause or causal relationships among observed events, includingcausal cycles, purely on the basis of timing information. Wepresent data from three behavioral experiments and extend pre-viously proposed Bayesian models that makes use of order anddelay information for causal structure learning. Our findingssupport the idea that people rely on the delays between eventsrather than order information alone. Meanwhile, deviationsfrom our model predictions suggest that people have an induc-tive bias against common hidden causes and rely on heuristicsto distinguish between causal structures, such as event over-laps, at least with the cover story considered in these experi-ments. Further, our data suggest that people have particularlyflexible representations of cyclic relationships.

Do letter-shapes predict in any way the canonical sounds they represent? Does the letter a in any sense visually predictits canonical pronunciation //? We extended existing quantitative approaches to measuring systematicity between phonol-ogy and semantics. We quantified all pairwise visual distances between letters, using Hausdorff distance. We took thecorresponding canonical pronunciations of the letters and quantified all pairwise distances between their feature-level rep-resentations, using edit distance and Euclidean distance. We defined letter-sound systematicity as a correlation betweenthese two lists of distances. We confirmed Korean as the gold standard for letter-sound systematicity; it was designed in the15C to have exactly this characteristic. We found small but significant correlations in Arabic, Cyrillic, English, Finnish,Greek and Hebrew orthographies, with Courier New giving the most consistent correlations. Pitmans English shorthandand the Shavian shorthand alphabet also showed robust systematicity, and baseline fictitious orthographies showed nosystematicity, validating our approach.

In applied settings, computational models of memory haveproven useful in making principled performance predictions.Specifically, historical data are used to derive modelparameters in order to enable out-of-sample predictions.Parameters are typically fit to meaningful subsets of data.However, labels that demarcate what constitutes a“meaningful” subset are not always available. Here, we utilizea data-driven method to cluster past performance into subsetspossessing statistical similarities. We contrast predictions fromcluster-specific model parameters with predictions based onsubsets that are artifacts of the experimental design. We showthat cluster-based predictions are at least as accurate as thechosen baselines and highlight additional advantages of thedata-driven approach.

Asking questions about unknown things involves recognizing knowledge gaps, identifying information sources, and for-mulating appropriate questions. This active involvement propels development by individualizing the learning environment.To characterize active engagement in word learning, we investigated whether preschoolers ask questions about novel vo-cabulary and evaluate definition quality. In Study 1, preschoolers were asked to perform actions following instructionswith novel (transpose) or familiar (switch) verbs. They asked more questions about novel (M = 3.31 out of 9, SD = 3.34)than familiar verbs (M = .17, SD = .44), t(35) = -5.68, p ¡ .001. In Study 2, informative or uninformative definitions wereprovided. Preliminary data suggest that preschoolers only asked questions when faced with uninformative definitions (M= .65 out of 3). When faced with novel words, preschoolers not only elicit questions, but also determine whether theirinformation needs have been met.

This paper reports initial results from a usability study con-ducted in the formative and user-centered design phase of alarger project to translate an existing, science-focused edu-cational technology for neurotypical middle school studentsinto a new, social-reasoning-focused educational technologyfor students on the autism spectrum. Participants in our studyincluded both adolescents on the autism spectrum and typi-cally developing adolescents, who were asked to complete theBetty’s Brain educational-technology-based science activity aswell as a social-reasoning movie question-answering activity.Results include qualitative observations of general student en-gagement and challenges as well as quantitative measures ofperformance and eye gaze, including key differences observedacross our two sample groups, with the goal of informingthe design and adaptation of future technology-based inter-ventions. Our findings suggest specific considerations for de-signing educational technologies for adolescents on the autismspectrum, including 1) finding ways to help students followinstructional/tutorial portions of new technologies, especiallywhen lengthy instructions and/or complex interfaces are in-volved; 2) proactively anticipating and finding ways to mit-igate potential student episodes of frustration / dysregulationwhile using the technology; and 3) capitalizing on features ofthe technology found to be engaging/motivating for students.

Tests of cross-situational word learning use a range of stimuli. How does the distinctiveness of a stimulus affect participantsability to learn its label? In two experiments, participants were presented with pairs of unfamiliar images accompanied bytwo pseudoword labels. The images were either two visually similar robots or two visually dissimilar novel objects. Bydesign, the mapping of label to image was purposefully unclear, and we further manipulated which images were displayedwith one another across trials (i.e., their presentation context). In one condition, pairs of images were randomly determined,while in the other, sets of images consistently appeared with one another throughout training. At test, participants weregiven one label and instructed to match it to one of four possible images. Participants who had been exposed to the visuallydissimilar objects outperformed those who had been exposed to the visually similar robots, regardless of presentationcontext.

Previous research by Kneer and Bourgeois-Gironde (2017) suggests that legal experts are susceptible to the “severity effect” – they ascribe a higher level of intentionality for actions if they lead to very bad side-effects than when they have somewhat bad side-effects. These results are potentially problematic for the legal system because ascriptions of intentionality in the law explicitly depend on the evaluation of mental states of the agent (mens rea), not on the badness of the outcomes she caused. In this paper, we provide and test an alternative explanation of the “severity effect” that has no troubling implications for the law. We suggest that it may be a subtype of a more general “side-effect effect” (Knobe, 2003), which is compatible with certain legal criteria of ascribing intentionality.

Distributional Semantics Models (DSMs) are a primary method for distilling semantic information from corpora. However,a key question remains: What types of semantic relations do DSMs detect? Prior work has addressed this question using alimited set of ratings that typically are either amorphous (association norms) or restricted to semantic similarity (SimLex,SimVerb). We tested four DSMs (SkipGram, CBOW, GloVe, PPMI) using multiple hyperparameters on a theoretically-motivated, rich set of relations involving words from multiple syntactic classes spanning the abstract-concrete continuum(21 sets of ratings). Results show wide variation in the DSMs’ ability to account for the ratings, and that hyperparametertuning buys comparatively little for improving correlations. For CBOW and SkipGram, we included word and contextembeddings. For SkipGram, there was a marked improvement in simulating the human data by averaging them. Ourresults yield important insights into the types of semantic relations that are captured by DSMs.

Semantic space models based on distributional information and semantic network (graphical) models are two of the mostpopular models of semantic representation. Both types of models succeed at modeling or explaining various tasks. Bothtypes of models also have limitations. Spatial models have difficulties representing indirect semantic relations, whilegraphical models have lacked a theoretical account for the construction of their semantic network. In this article, wedevelop the Distributional Graph Model. The new model resembles semantic space models in the way that it is a repre-sentation of semantic memory obtained from statistical learning on a linguistic corpus. But like other graphical models,it is able to capture indirect semantic relatedness as well. Using an artificial language specifically designed to test differ-ent types of syntagmatic and paradigmatic relationships, we show that the Distributional Graph Model demonstrates thebenefits of both graphical and spatial distributional models.

BERT is a language processing model trained for word prediction in context, which has shown impressive performancein natural language processing tasks. However, the principles underlying BERT’s use of linguistic cues present in contextare yet to be fully understood. In this work, we develop tests informed by the semantic priming paradigm to investigateBERTs handling of lexical relations to complete a cloze task (Taylor, 1953). We define priming to be an increase in BERTsexpectation for a target word (pilot), in a context (e.g., I want to be a ), when the context is prepended with a relatedword (airplane) as opposed to an unrelated one (table). We explore BERTs priming behavior under various predictiveconstraints placed on the blank, and find that BERT is sensitive to lexical priming effects only under minimal constraintfrom the input context. This pattern was found to be consistent across diverse lexical relations.

Humans form and revise theories about the world throughout the lifespan. While intuitive theories of the physical andbiological world have been explored, the domain of time is understudied. We explored childrens theories about time byasking 4- to 6-year-olds (n = 38) and adults about the reality or possibility of temporal phenomena. We also asked them torate their confidence in their answers. All children agreed that clocks and aging are real. However, judgements about timetravel, getting younger, seeing the future, and the past and future themselves, changed. While 6-year-olds gave adult-likeresponses to most questions, 5-year-olds were less sure. Unlike older children, 4-year-olds said seeing the future and timetravel are possible, but the past is not real. These results suggest that children converge on adultlike theories about timebetween 4 and 6 years of age. Future work will explore factors driving the formation of these theories.

One challenge that children face when learning from others isthat social agents can behave in unpredictable ways. Socialagents may acquire—or fail to acquire—new information thatinfluences how they interact with the learner. Little is knownabout children’s sensitivity to these changes or howeffectively children update their own behavior in response.Participants (N = 129) searched for rewards while receivingsuggestions from a social agent. The suggestions changed inlevel of reliability over time. All children updated howheavily they weighted the cues after the change. However,younger children were more influenced by their initialexperience with the suggestions, indicating that youngerchildren may have more difficulty disengaging from socialinformation in uncertain learning environments.

Conceptual combination is the act of building complex concepts from simpler ones. Although previous research has ex-amined how inferences about compound objects (e.g., fuzzy chair) are produced from their constituent concepts, littleis known about the combinatorial processes that produce inferences about compound social categories (e.g., Irish Musi-cian). Using a computational approach, we investigated the relationship between trait ratings of 25 nationality-occupationcombinations and ratings of their constituent concepts. 25 non-human animal combinations (e.g., circus snake) serve as acomparison. We find that constituent concepts are weighted unequally when combined: head concepts (Musician/Snake)are prioritized over modifier concepts (Irish/Circus) for both combination types. Additionally, ratings of more familiar so-cial combinations diverge increasingly from ratings of their constituent concepts, whereas ratings of more familiar animalcombinations instead converge with ratings of their constituents. This raises the possibility that existing knowledge playsdifferent roles in peoples inferences about human versus animal categories.

Early childhood home environments are well understood to be foundational for cognitive development, yet their relation-ship to specific cognitive skills is challenging to understand empirically in low resourced nations, leading to lack of clarityabout the roles of socialization versus maturation. We examine the contributions of environmental context on culturallyadapted versions of executive functioning (EF; inhibitory control), expressive language, and reasoning tasks (spatial andrelational reasoning) in a representative sample of 1,834 children (24-59 month-olds) in Nicaragua. Multivariate regres-sions revealed children with highly structured homes and enrollment in early education in this context exhibited higherEF, expressive language and reasoning skills, explaining cognitive skills better than socioeconomic status. These resultssuggest these cognitive skills are malleable and impacted by the home context. Language and reasoning skills were alsorelated to more social partners, suggesting language and reasoning are more tied to social interaction than EF.

Existing research has developed a working understanding of borderline personality disorder (BPD) patient traits and behav-ior in everyday life, but the subtleties of their cognitive processes during decision-making remains unclear. To understandhow reliance on previous experiences (priors) versus current sensory information (likelihoods) in the decision-makingprocess may differ for those with BPD in comparison to those within neuro-typical population, we implemented a coin-catching behavioral task with varying levels of prior and likelihood uncertainty. We hypothesized that, in accordance totypical BPD characteristics, BPD patients will rely significantly more on likelihood information even when likelihoodinformation is more unreliable than prior information. Analyzing the results using Bayesian statistics, we found evidencesuggesting that both the BPD patient group and the neuro-typical control group utilized prior and likelihood informa-tion similarly in decision-making. We theorize that BPD characteristics that are prominent in social interactions may notexactly replicate in non-social settings.

Executive function is a cognitive control system contributes uniquely to resilience (Greenberg, 2006; Obradovic, 2016).This study looked into resilience development during its controversial age period in cognitive perspective, aims to explorehow its components (i.e., cognitive flexibility, inhibitory control, and working memory) affect resilience in different agegroups. Data were collected in middle schools and universities (N=197). Participants were asked to join a series of labexperiments and questionnaires in a psychological lab. Results showed resilience as well as executive function in algorith-mic mind level develop from adolescence to emerging adulthood. Cognitive flexibility plays central role in functioningresilience with various cognitive mechanisms for different populations. With the identification of cognitive mechanismsunderlying the relation between cognitive flexibility subsets (i.e., reactive flexibility and spontaneous flexibility) and re-silience, this study contributes a cognitive perspective for better understanding of resilience before challenging eventshappen.

Ostracism is a social phenomenon, shared by most social animals, including humans. Its detection plays a crucial role forthe individual, with possible evolutionary consequences for the species.Considering (1) its relation with communication and therefore language and (2) its social nature, we hypothesised that thecombination of linguistic and community-level social features would have a positive impact on the automatic recognitionof ostracism in human online communities.We modelled a linguistic community through Reddit data and we analysed the performance of simple classification al-gorithms (Nave Bayes and SVM), particularly focusing on the feature selection. Comparing the accuracy scores of thealgorithms fed with a) linguistic features, b) extralinguistic features, and c) linguistic + extralinguistic features, we testedour hypothesis, showing how models based on c) generally outperform.To our knowledge, this is the first attempt to automatise the identification of such a complex phenomenon through NLPtechniques.

Analyzing how human beings resolve syntactic ambiguity haslong been an issue of interest in the field of linguistics. It is, atthe same time, one of the most challenging issues for spokenlanguage understanding (SLU) systems as well. As syntacticambiguity is intertwined with issues regarding prosody and se-mantics, the computational approach toward speech intentionidentification is expected to benefit from the observations ofthe human language processing mechanism. In this regard, weaddress the task with attentive recurrent neural networks thatexploit acoustic and textual features simultaneously and revealhow the modalities interact with each other to derive sentencemeaning. Utilizing a speech corpus recorded on Korean scriptsof syntactically ambiguous utterances, we revealed that co-attention frameworks, namely multi-hop attention and cross-attention, show significantly superior performance in disam-biguating speech intention. With further analysis, we demon-strate that the computational models reflect the internal rela-tionship between auditory and linguistic processes.

Despite the tremendous advances of Artificial Intelligence, a general theory of intelligent systems, connecting the psycho-logical, neuroscientific and computational levels is lacking. Artificial Neural Networks are good starting points to buildthe theory. We propose to analyze generalization of learning in simple but challenging problems. We have previouslyproposed to concentrate on learning sameness, as we have shown that this is difficult for a SRN. Here we present theresults of trying to use a Long-Short Term Memory Network to learn sameness. We show that the LSTM although muchmore efficient to learn partial examples of sameness fails to generalize to a proportion of the examples. This suggests thatLSTM and SRN share a core set of features that make generalization of sameness problematic. By analyzing where thetwo models fail, we arrive at a proposal of what makes sameness hard to learn and generalize in recurrent neural networks.

We present a computational model of sentence production thatemulates variation of the output of lexicalization andgrammatical encoding of the abstract pre-lexical message, interms of complexity and accuracy of the generated sentence aswell as fluency and cognitive costs of the sentence production.The model integrates approaches from routine action selectionmodels built on Dual Systems Theory (Norman & Shallice,1986) with ‘A Blueprint for the Speaker’ developed by Levelt(1989). The paper describes and justifies the modelarchitecture, explores factors affecting language variation inproduction, and applies the model for testing relationshipbetween complexity, accuracy, and fluency (CAF) of languageproduction as debated within Second Language Acquisition(SLA) research. A simulation that generated 78,750 sentencesprovides evidence of the trade-off relationship between CAFparameters as speakers have to sacrifice performance on one ofthe CAF factors in order to improve the remaining two.

Children struggle to conduct controlled tests, even with explicit instruction (Chen & Klahr, 1999). How learners approachmultivariable tasks can be affected by task goals; a scientist uncovers causal regularities whereas an engineer produceseffects (Klahr, et al., 2011). This study investigated whether science vs. engineering goals presented in a narrative picturebook influenced childrens ability to conduct a controlled test.Six-to-8-year-olds (N=72) were first pre-tested on their ability to design a controlled test of a variable predicting how fara ball travels down to a ramp. Children were then read a picture book that contained a science (conduct controlled test) orengineering (create faster ramp) goal. Next, they completed an identical post-test and transfer-test with two new variables.Childrens ability to design a controlled test improved significantly from pre- to post-test (p=.008) and marginally frompre-test to transfer (p=.067) in both conditions, suggesting that children learned from both goals.

Previous work suggests that children’s ability to understand metaphors emerges late in development. Researchers argue that children’s initial failure to understand metaphors is due to an inability to reason about shared relational structures between concepts. However, recent work demonstrates that causal framing facilitates preschoolers’ relational reasoning. Might causal framing also facilitate preschoolers’ metaphor comprehension? In Experiment 1, we presented 128 4- to 5-year-olds with a novel metaphor comprehension task, following a causal warm-up task, control warm-up task, or no warm-up task. In the novel comprehension task, preschoolers rated functional metaphors and nonsense statements as smart or silly, and provided explanations. Preschoolers ranked metaphors as “smarter” than nonsense statements, and a quarter of preschoolers provided functional explanations. There was no effect of warm-up tasks. In Experiment 2, we validated the metaphor comprehension task with adults. Overall, the current work presents a new paradigm that demonstrates preschoolers’ capacity to understand functional metaphors.

Executive control processes allow task-appropriate behaviour across cognitive domains, yet, children have a long devel-opmental period with little or no control. Traditionally, this is viewed as a negative but necessary consequence of the timetaken for prefrontal development and learning control processes. Here we examine a recent model of controlled semanticcognition (https://biorxiv.org/cgi/content/short/860528v1) as a test case to present evidence for an alternative (yet perhaps,complementary) view; that a developmental period without control has a positive functional role in learning. Varying thelength of a developmental period without control, we identify an optimal period (around one third of the learning time)which allows conceptual learning to happen much faster, without loss of conceptual abstraction ability. This speeding ismediated by the way control interacts with representation regions (deeper multimodal ¿ shallower input areas). This hasimplications for our understanding of controlled semantic cognition and the development of control more generally.

Humans can extract co-occurrence regularities from their environment, and use them for learning. This statistical learningability (SL) has been studied extensively. However, almost all SL studies present the regularities to be learned in uniformfrequency distributions (each unit appears equally often). In contrast, real-world learning environments, including thewords children hear and the objects they see, are not uniform, and consequently more predictable than lab-based ones.Recent research shows that word segmentation in children and adults is facilitated after exposure to a Zipfian distribution.Here, we ask if this effect is domain-general by testing children and adults on a visual SL task. Both children and adultsperformed better in the Zipfian distribution compared to the uniform one, overall, and for low-frequency triplets. Theseresults illustrate the impact of distribution predictability on learning across modality and age, and point to the possiblelearnability advantage of skewed distributions in the real-world.

From minimal observable action, humans automatically make fast, intuitive judgments about what other people think,want, and feel (Heider & Simmel, 1944). Even when no agent is visible, children can infer the presence of intentionalagents based on the environmental traces that only agents could leave behind (Saxe et al., 2005; Newman et al., 2010).Here we show that four- to six-year-olds can go beyond inferring the presence of an agent to matching an agents mentalstate with the trace they left behind. Participants (N = 35, M: 5.6 years, range:4.0 6.8 years) saw pairs of dresser drawerswith different numbers and orientations of open drawers, and were asked to match one of the static scenes to an agentsknowledge state (whether the agent wasnt searching at all but was just playing, knew exactly where an object was hidden,knew the approximate location, had no idea where it was hidden, or at first didnt know and then remembered). We comparechildrens performance to a formal model, in which we build upon classical models of Bayesian Theory of Mind that treatmental state inferences as a form of inverse planning; here we extend those models to consider cases where the behavioris not observed but must be inferred from the structure of the environment.

Dogs are recognized for their social reasoning and skillful interactions with humans, but their understanding of causal rela-tionships and the underlying principles (e.g., temporal priority) are under-explored. To address this gap, we adapted a taskused with children to investigate how pet dogs use temporal sequences of events. Dogs (N=22) watched an experimenterperform a sequence of two actions on a puzzle box: i) one action before a treat was dispensed from the box (causal action)and ii) the other action after the treat appeared (non-causal action). Each action was temporally equidistant from the treat.After observing the sequence, dogs interacted with the box. Preliminary results indicate that over the course of five trialsdogs preferred interacting with the causal action and were more likely to investigate it first, compared to the non-causalaction on trial one. Results will be discussed in a comparative context of observational and experiential learning.

Word class conversion refers to the extended use of a wordfrom one grammatical class to another without overt morpho-logical marking. Noun-to-verb conversion, or denominaliza-tion, is one form of word class conversion studied extensivelyin the literature. Previous work has suggested that novel de-nominal verb usages are comprehensible if the listener cancompute the intended meaning based on shared knowledgewith the speaker. However, no existing work has explored thecomputational mechanism under this proposal. We proposea frame-semantic generative model, Noun2Verb, that supportsthe inference and generation of novel denominal verb usagesvia semi-supervised learning. We evaluate this framework ina dataset of denominal verbs drawn from adults and childrenagainst a state-of-the-art model from natural language process-ing. Our results show that Noun2Verb aligns better with humaninterpretation and bridges the gap between machines and hu-mans in lexical innovation.

We investigated whether the presence of exception items can impede effects of category compression (within-category items appearing more similar) in classification learning. We hypothesized that the distinct representations afforded to exceptions may cause the target category to appear less cohesive, thereby reducing the likelihood of compression occurring. Across two experiments, participants engaged in classification learning without exceptions, with an easy exception, or with a difficult exception. Pairwise similarity ratings for all items were collected before and after learning to index compression. Results from Experiment 1 suggest that difficult exceptions can impede compression for the contrast category when situated within its cluster, while results from Experiment 2 suggest that both kinds of exceptions can impair compression of standard items in a target category relative to the No Exception control. We also observed surprising evidence of a novel between-category compression effect that was observed with the category structure developed for these experiments.

Emergent communication in artificial agents has been studiedto understand language evolution, as well as to develop artifi-cial systems that learn to communicate with humans. We showthat agents performing a cooperative navigation task in variousgridworld environments learn an interpretable communicationprotocol that enables them to efficiently, and in many cases,optimally, solve the task. An analysis of the agents’ policiesreveals that emergent signals spatially cluster the state space,with signals referring to specific locations and spatial direc-tions such as left, up, or upper left room. Using populationsof agents, we show that the emergent protocol has basic com-positional structure, thus exhibiting a core property of naturallanguage.

Infant directed speech (IDS) is characterized by exaggerated pitch and vowel lengthening. The current study recorded ev-eryday interactions with fifty 12-month-old infants and their families to examine whether there are significant differencesin the acoustic features of IDS (such as frequency, pause duration, and vowel length) between male and female speak-ers, and whether any differences are related to childrens vocabulary development at 12 months and 15 months. Femalespeakers, compared with male speakers, exhibited significantly longer pauses in phrase final positions, thereby poten-tially signaling syntactic structures more clearly. Controlling for family income and maternal education, female speakersfrequency variation at non-final vowel positions accounted for an additional unique variance for infants productive vocab-ulary at 12 months and receptive vocabulary at 15 months while none of the acoustic features of male speakers related tovocabulary size. These results suggest that female speakers IDS may be more influential in language development.

Which quantificational forces do languages encode lexically?When a language features multiple quantificational scales(e.g. determiner and adverbial quantification), does the pat-tern of lexicalization of quantificational forces we discoverfor one scale correlate with those of other scales? We useEnglish as a first test case for examining these questions,adapting the basic ideas of Lewis (1975) into the hypothesisthat English lexical quantifiers unrelated to cardinal numbersor definite descriptions, determiner and adverbial alike, haveone of six quantificational forces. To begin to test this claimempirically, we elicited speaker interpretations of a range ofquantifiers in a web-based study. Dividing participants into anadverbial condition and a determiner condition, we gave acontext specifying a 100-day period and asked participants tojudge the quantificational force of quantified sentences denot-ing an individual’s daily activities during this period. Wefound evidence of cross-scale correspondences but fewerquantificational forces than expected. These results providepreliminary evidence for parts of our hypothesis but suggest aneed for future research that covers more lexical items, lan-guages, and quantificational scales.

When making causal inferences, prior research shows that peopleare capable of controlling for alternative causes. These studies,however, utilize artificial inter-trial intervals on the order ofseconds; in real-life situations people often experience data overdays and weeks (e.g., learning the effectiveness of two newmedications over multiple weeks). In the current study, participantslearned about two possible causes from data presented in atraditional trial-by-trial paradigm (rapid series of trials) versus amore naturalistic paradigm (one trial per day for multiple weeks viasmartphone). Our results suggest that while people are capable ofdetecting simple cause-effect relations that do not requirecontrolling for another cause when learning over weeks, they havedifficulty learning cause-effect relations that require controlling foralternative causes.

Learning occurs across distributed multimodal experiences. To accumulate knowledge one must integrate related infor-mation across different representational formats i.e. across text and photographs. We extended an established memoryintegration paradigm to test acquisition and integration of knowledge across different representational formats based onart history museum exhibits. Participants received integrable passage pairs in either text-text or text-text+photographformats. Even though the processing demands were higher with photographs, preliminary results indicate no significantdifferences between conditions. Future work will examine potential differences in integration across both context (class-room to museum) and representational formats (text and museum artifacts). We hypothesize that integration across contextand representational format will create higher cognitive demand than integration across representational formats; will thisbe offset by the higher information-value of museum exhibits? This research will provide key insights into multimodallearning and inform best practices for maximizing comprehension in informal learning settings such as museums.

Between prototype and exemplar models of categorization lie adaptive models, which represent categories using a varyingnumber of reference points. They regulate the amount of abstraction they make depending on the category structure. Moti-vated by ecological considerations, we investigate whether adopting such adaptive strategies could improve generalizationin realistic environments. We compare performance of four adaptive models: RMC, SUSTAIN, REX, VAM with that ofprototype and exemplar models on three artificial and three natural category structures. Both the exemplar model withadapted sensitivity parameter and VAM perform well on category structures requiring different amount of abstraction. Ourresults confirm the importance of the link between abstraction and generalization.

Memory requires both individuation of specific episodes aswell as extraction of gist across related experiences. Thisstudy developed a spatial memory paradigm to track changesin item memory (memory for specific locations) and gistmemory (estimate of the center of the locations) across aperiod of a month, and to measure the relation between thesetwo forms of memory. We found that item memories decayedcompared to gist memory after a month, yet there was apositive relationship between the two forms of memory thatpersisted. Moreover, item memories were biased towards gistmemory only after a month. These findings together indicatethat gist memory, initially extracted from item memories,gradually develops into a stable representation that can guideitem memory retrieval over longer durations.

We explore the benefits of augmenting state-of-the-art model-free deep reinforcement learning with simple object representa-tions. Following the Frostbite challenge posited by Lake et al.(2017), we identify object representations as a critical cognitivecapacity lacking from current reinforcement learning agents.We discover that providing the Rainbow model (Hessel et al.,2018) with simple, feature-engineered object representationssubstantially boosts its performance on the Frostbite game fromAtari 2600. We then analyze the relative contributions of therepresentations of different types of objects, identify environ-ment states where these representations are most impactful, andexamine how these representations aid in generalizing to novelsituations.

We explore structural issues with parameter estimation fornon-linear cognitive models: Some parameter values are eas-ier to recover than others, and the recoverability of differentparameters interacts in systematic ways. We propose methodsfor researchers to anticipate and visualize and these issues, andthe systematic ways they differ across experimental designs.Our approach consists of assessing how changes in parame-ter values translate into changes in behavioral predictions, anddevelop measurements of the relative responsiveness of predic-tions to parameter values. We demonstrate application of ourapproach to cumulative prospect theory (CPT), a widely-usedmodel of risky decision-making.

Multiple theories of scientific change have been prominently promulgated since Kuhn. A quasi-discipline “Scientonomy” has even been proposed to formalize these theories. The cybernetics principle known as “The Law of Requisite Variety (LRV)” when combined with cognitive science insights regarding categorization and its ilk can be used to chart one such formalism. LRV holds that control/prediction can only be assured when the internal complexity of a system matches the external complexity it confronts The key indicator of an activity directed at scientific change comes from examinations of the models which scientists deploy in attempting to link pre-existing explanations with new problems to be explained. Normal science is a reductive activity – limiting the variety encountered. Innovative science is the process of expanding such variety, and scientific change is what happens when the innovative crosses the threshold for normal.

To efficiently learn and retain motor skills, we can introducecontextual interference through interleaved practice.Interleaving tasks or stimuli initially hinders performance butleads to superior long-term retention. It is not yet clear ifimplicitly learned information also benefits from interleavingand how interleaved practice changes the representation ofskills. The present study used a serial reaction time task whereparticipants practiced three 8-item sequences that were eitherinterleaved or blocked on Day 1 (training) and Day 2 (testing).An explicit recall test allowed us to post-hoc sort participantsinto two groups of learners: implicit learners recalled less itemsthan did explicit learners. Significant decreasing monotonictrends, indicating successful learning, were observed in bothtraining groups and both groups of learners. We found supportfor the benefit of interleaved practice on retention of implicitsequence learning, indicating that the benefit of interleavedpractice does not depend on explicit memory retrieval. ABayesian Sequential Learning model was adopted to modelhuman performance. Both empirical and computational resultssuggest that explicit knowledge of the sequence wasdetrimental to retention when the sequences were blocked, butnot when they were interleaved, suggesting that contextualinterference may be a protective factor of interference ofexplicit knowledge. Slower learning in the interleavedcondition may result in better retention and reducedinterference of explicit knowledge on performance.

We examined the effect of speaking more than one language (multilingualism) or two dialects of the same language (bi- dialectalism) on executive control (EC) by administering seven EC tasks to 46 multilingual, 72 bi-dialectal and 47 monolingual young adults. We used the EC model of Miyake, Friedman, Emerson, Witzki, Howerter and Wager (2000) according to which EC comprises three components: working memory, task-switching and inhibition. We also tested two theoretical views regarding the locus of the bilingual advantage: first, that bilingualism affects specific EC components and, second, that bilingualism has a more general effect on the whole EC network. Miyake et al.’s (2000) model was a good fit to our EC data. We also found that both multilinguals and bi-dialectals had significantly higher EC scores than monolinguals. Moreover, both the multilingual and the bi-dialectal advantage was found in overall EC ability and could not be attributed to a specific EC component.

Often in language and other areas of cognition, whether twocomponents of an object are identical or not determine whetherit is well formed. We call such constraints identity effects.When developing a system to learn well-formedness from ex-amples, it is easy enough to build in an identify effect. But canidentity effects be learned from the data without explicit guid-ance? We provide a simple framework in which we can rig-orously prove that algorithms satisfying simple criteria cannotmake the correct inference. We then show that a broad classof algorithms including deep neural networks with standardarchitecture and training with backpropagation satisfy our cri-teria, dependent on the encoding of inputs. Finally, we demon-strate our theory with computational experiments in which weexplore the effect of different input encodings on the ability ofalgorithms to generalize to novel inputs.

Rhythmic abilities have been related to language processing skills such as phonological awareness, rise time discriminationand verbal memory. Following this reasoning, they have also been linked to reading acquisition. In particular, in prereaders,tapping to a beat, a task that entails rhythmic processing through auditory-motor synchronization (AMS), has shown todiscriminate children with poor and good phonological skills. However, evidence regarding how the AMS-reading linkdevelops through time, starting before reading instruction, is scarce. In the present study, we followed a large sample of600 children from kindergarten to second grade, through a digital assessment of literacy and literacy-related skills, as wellas rhythmic abilities. We found that AMS in K5 uniquely contributes to future reading performance, above and beyondphonological skills. These findings underscore the role of rhythmic abilities in reading acquisition, and its relation tophonological processing.

Theoretical models of self-regulated learning highlight theimportance and dynamic nature of metacognitive monitoringand regulation. However, traditional research typically has notexamined how different judgments, or the relative timing ofthose judgments, influence each other, especially in complexlearning environments. We compared six statistical modelsof performance of undergraduates (n = 55) learning inMetaTutor-IVH, a multimedia learning environment. Threetypes of prompted metacognitive judgments (ease of learning[EOL] judgments, content evaluations [CEs], and retrospectiveconfidence judgments [RCJs]) were used as individualpredictors, and combined in a uniformly-weighted compositescore and empirically based weighted composite score acrossthe learning session. The uniformly weighted composite scorebetter captured performance than the models using only anEOL judgment or RCJ judgment. However, the empiricallyweighted composite model outperformed all other models.Our results suggest that metacognitive judgments should notbe considered as independent phenomenon but as an intricateand interconnected process.

People have an intuitive sense of probability beginning early in life, where they appreciate that samples should reflectpopulations in their statistical properties (e.g., Denison & Xu, 2019). We examined whether adult participants in twoexperiments (N=132; N=141, respectively) can use this intuitive sense to infer unseen properties that might be affectingthe sampling process. In both experiments, adults saw boxes with different sized balls in varying proportions. They thensaw sampling events, in which small numbers of balls were shaken from a hidden exit on the top of the box, that wereeither probable or improbable, based on box proportions. In general, adults appropriately inferred constraints on the sizeof the hidden exit by integrating information from the sizes of the balls that were sampled and the overall distribution ofballs in the box. Ongoing work examines whether toddlers can make similar inferences.

Affective components are frequently overlooked in computational modelling, despite the notable role of emotions in learn-ing. Towards the goal of measuring affect in learning, we developed a theory-based Bayesian model that predicts surprisebased on a learners prior beliefs and the evidence observed, and then compared the model to a physiological measure com-monly suggested to capture surprise: pupil dilation. Critically, we also investigate whether this correlation is strong whenparticipants predict the events. Comparing our model predictions to the first four test trial responses from 93 participants(mean age: 8.00 years) revealed a significant, positive correlation when making predictions (r(9)=.55, p=0.04), a negativecorrelation when only evaluating (r(9)=-.50, p=0.07), and significant difference between groups (z=2.34, p¡0.01). Nextsteps will allow us to build on this result by developing a modified Bayesian model, that takes physiological surprise as acomponent in predicting the participants learning.

Traditional results using a cued-recall paradigm have allegedlydemonstrated that distant analogs tend to be retrieved less oftenthan disanalogous matches maintaining only surface similarity.Recent results, however, suggest that said advantage may be due tothe inadvertent inclusion of structural similarity in surface matches.In two experiments we had distant analogs compete in LTMwith two types of surface matches lacking any degree ofstructural overlap, but equated with the target in terms ofelement similarities. Distant analogs were less retrieved thatstories maintaining similar first-order relations and objects withthe target, but no overlapping structure. This differencedisappeared when surface similarity involved only similar objects.Results show that the surface superiority effect relies on thetype of surface matches that compete with distant analogs, thussuggesting a more complex picture of the forces that governaccess to similar items in memory.

A non-probabilistic model of speakers competence regarding extraction from a coordinate structure, which was argued tobe sensitive to how the conjuncts are connected to each other in discourse (e.g., Lakoff, 1986; Kehler 2002) is presented.The model makes use of Lakoffs (1986) account of the acceptability of extraction from coordinate structures by adoptingthe Frame Semantics framework. Lakoff argues that acceptability of extraction is affected by the belonging of the conjunctsto certain scenarios (e.g. a natural sequence of events), something that is measurable in this framework. An algorithm thatmeasures the degree of relatedness between two conjuncts by consulting FrameNet (the framework implementation) andquantifying the common frames they belong to is proposed and tested on sentences used in an acceptability judgementsurvey on extraction from coordinate structures (Harris, 2009). The models outcomes interact with the experimentalconditions in predicting human judgements, providing initial support for the proposal.

Functionalist accounts of language suggest that formsare paired with meanings in ways that support efficientcommunication. Previous work on grammatical markingsuggests that word forms have lengths that enable efficientproduction, and previous work on the semantic typologyof the lexicon suggests that word meanings representefficient partitions of semantic space. Here we consider anintegrated information-theoretic framework that captures howcommunicative pressures influence both form and meaning.We take tense systems as a case study, and show how theframework explains both which tense systems are attestedacross languages and the length asymmetries of the forms inthose systems.

Children’s tendency to associate novel words with novel refer-ents has been taken to reflect a bias toward mutual exclusivity.This tendency may be advantageous both as (1) an ad-hoc ref-erent selection heuristic to single out referents lacking a labeland as (2) an organizing principle of lexical acquisition. Thispaper investigates under which circumstances cross-situationalneural models can come to exhibit analogous behavior to chil-dren, focusing on these two possibilities and their interaction.To this end, we evaluate neural networks’ on both symbolicdata and, as a first, on large-scale image data. We find thatconstraints in both learning and selection can foster mutual ex-clusivity, as long as they put words in competition for lexi-cal meaning. For computational models, these findings clarifythe role of available options for better performance in taskswhere mutual exclusivity is advantageous. For cognitive re-search, they highlight latent interactions between word learn-ing, referent selection mechanisms, and the structure of stimuliof varying complexity: symbolic and visual.

How do humans learn models supporting decision making?Reinforcement learning (RL) is a success story both in ar-tificial intelligence and neuroscience. Essential to these RLmodels are state representations. Based on what current statean animal or artificial agent is in, they learn optimal actionsby maximizing future expected reward. But how are humansable to learn and create representations of states? We introducea novel sequence prediction task with hidden structure whereparticipants have to combine learning and memory to find theproper state representation, without the task explicitly indicat-ing such structure. We show that humans are able to find thispattern, while a sensory prediction error version of RL cannot,unless equipped with appropriate state representations. Fur-thermore, in slight variations of the task, making it more diffi-cult for humans, the RL-derived model with simple state rep-resentations sufficiently describes behaviour and suggests thathumans fall back on simple state representations when a moreoptimal task representation cannot be found. We argue thistask allows to investigate previously proposed models of stateand task representations as well as supporting recent resultsindicating that RL describes a more general sensory predictionerror function for dopamine, rather than predictions focussedsolely on reward.

Social relationships such as playmates and friendships are im-portant for children’s development. But relatively little isknown about how such relationships are formed. In two stud-ies, 5- to 6-year-old children chose their playmates in a hypo-thetical scenario that resembled a real-world social situation.The findings suggested that children used both the base-rateinformation about the social group and the adaptive samplingstrategy in playmate choice – they approached or avoided in-dividuals based on the group that the individuals belonged to,as well as their past experiences with the individuals.

In this paper, we consider chance-curation (the task of eas-ing chance-discovery activities for agents) as far as it concernsinformation sharing in online communities, understood as Vir-tual Cognitive Niches. We claim that Virtual Cognitive Nichesare digitally-encoded collaborative distributions of informa-tion and pieces of knowledge into the environment. The par-ticularity of Virtual Cognitive Niches, as socially biased net-works, is that they provide more ways for agents to interactthan to control the quality of the information they share and re-ceive. We contend that this social bias enables chance-curationstrategies that agents cannot foster in real-life communities. Inparticular, the chance curation strategies that we discuss are:redirecting the attention of agents to the virtual domain, foster-ing an only-docility-based relation with truth, and increasingthe social virtues of fallacies.

Recent research at the cross between cognitive and social sci-ences is investigating the cognitive mechanisms behind coop-erative decisions. One debated question is whether cooperativedecisions are made faster than non-cooperative ones. Yet em-pirical evidence is still mixed. In this paper we explore theimplications of individual heterogeneity in social value orien-tation for the effect of response time on cooperation. We con-duct a meta-analysis of available experimental studies (n=8;treatments=16; 5,232 subjects). We report two main results:(i) the relation between response time and cooperation is mod-erated by social value orientation, such that it is positive forindividualist subjects and negative for prosocial subjects; (ii)the relation between response time and cooperation is partlymediated by extremity of choice. These results suggest thathighly prosocial subjects are fast to cooperate, highly individ-ualist subjects are fast to defect, and subjects with weaker pref-erences make slower and less extreme decisions. We explainthese results in terms of decision-conflict theory.

Understanding Some girls have bags is difficult for preschoolers, because they may not properly calculate scalar implica-ture. In this study, we examined whether preschoolers and adults guess scalar information without scale markers (some,all) using negative/positive Japanese sentences in picture selection task. This task consisted of three cards, e.g., SOMEcard illustrated two persons with a bag and four persons without a bag. The results were that the positive expression,Baggu wo motte-iru hito ga imasu (there are persons with a bag), was guessed as meaning of ALL (all persons have a bag)by children, while guessed as SOME by adults. Interestingly, the negative expression, Baggu wo motte-inai hito ga imasu(there are persons without a bag), was guessed as NONE by children, but guessed as SOME by adults. The results suggestchildren may not utilize the combination of existence and agents state information to guess scalar implicature.

Against the typical results from laboratory studies, it has beensuggested that retrieving distant analogs might be easy in real-life, where we tend to encode familiar situations with expert-like schemas. In each of two experiments, we formed twogroups of participants who, as determined by a questionnairepresented during a first session, had reported that they haveexperienced an event corresponding to a schema-governedcategory (Experiment 1) or to a system of schema-governedcategories (Experiment 2). While the episodes reported by oneof the groups belonged to the same domain as the target analogto be presented during the second session, those of the othergroup belonged to a different thematic domain. During atemporally and contextually separated session, the experimenterspresented both groups with a target analog belonging to theschema-governed category for which participants had reporteda base analog. Participants had to retrieve an autobiographicalepisode that they considered analogous to the situationpresented by the experimenter. In line with traditional studies,we found that retrieving distant instances of relationalcategories is much more difficult than retrieving closeinstances.

In a compositional language the meaning of a sentence is a function of the meaning of its parts and the way they arecombined. Recent computational models of the emergence of compositionality have focused on the emergence of wordswhich encode sub-units of meaning in sub-units of form. Decidedly less attention has been paid to the emergence of rulesgoverning the combination of these words. Our work uses LSTM networks in an iterated learning set-up to provide anaccount of how some aspects of compositional structure may emerge through cumulative cultural evolution. We presenta novel metric for assessing the degree of positional structure present in an emergent model and use it to illustrate howcanonical word order may emerge naturally in LSTM models. This supports the notion that some elements of linguisticstructure result more from the dynamics of language transmission and use than domain-specific cognitive biases.

Eitam et al. (2013) reported that immediate feedback to response could motivate the same response in the followingtrials. They suggested action-effects could have a value as information on control over the environment, resulting inthe response facilitation. However, the underlying mechanism of such faciliatory effects, what particular processes action-effects reinforce, remains unclear. Therefore, we investigated whether the response facilitation depends on actions, stimuli,or stimulus-response relationship. Participants were required to select adequate responses in accordance with the stimulias response cue. The action-effects depended on the combination of stimuli and responses; immediate and lagged effectscould be predicted by the stimulus, but shared the same response button. Results showed that the response was executedfaster when driven by stimuli associated with immediate effects than those associated with lagged effects. This indicatesthat immediate action-effects might facilitate response via stimuli-response association, but not via independent processesof actions or stimuli.

Several canonical experimental paradigms (serial reaction task, mxn task, etc.) have been proposed to study the typi-cal behavioural phenomena in a sequential motor key-press task. The repeated execution of visuomotor sequences insuch paradigms lead to overall performance improvement such that the inter-response intervals in between certain sub-sequences decreases as compared to that across other sub-sequences. This efficient and hierarchical cluster organisation iscalled motor chunking. We provide empirical evidence for motor chunking in grid-navigation sequencing tasks. The par-ticipants performed Grid-Sailing Task (GST) [Fermin et. al., 2010] that required navigating a 10x10 grid from start to goalposition while using a particular key-mapping between the 3 cursor movement directions and the 3 keyboard buttons. Thisstudy confirms the emergence of subject-specific, unique temporal patterns related to chunking after substantial practice.

The Stroop test evaluates the ability to inhibit cognitive interference. This interference occurs when the processing of onestimulus characteristic affects the simultaneous processing of another attribute of the same stimulus. Eye movements are anindicator of the individual attention load required for inhibiting cognitive interference. We used an eye tracker to collecteye movements data from more than 60 subjects each performing four different but similar tasks (some with cognitiveinterference and some without). After the extraction of features related to fixations, saccades and gaze trajectory, wetrained different Machine Learning models to recognize tasks performed in the different conditions (i.e. with interference,without interference). The models achieved good classification performances when distinguishing between similar tasksperformed with or without cognitive interference. This suggests the presence of characterizing patterns common amongsubjects, despite of the individual variability of visual behavior. The results open up interesting investigations.

Configurative weighting and adding can be a surprisingly effective approximation of multiplicative functions. In thecontext of joint probability judgment, Nilsson et al. (2009) has shown that, when marginal probabilities are only approxi-mately known, the configurative weighted average (CWA) of two probabilities not only predicts a high level of conjunctionfallacies, as observed in data, but also correlates higher with the true joint probability than if the two probabilities are mul-tiplied. Here we show that [1] the surface representing the optimal Bayesian estimate of a joint probability can be closelyapproximated by two planes, [2] configurative weighting and adding, such as the CWA model, constitutes such a two-planeapproximation, and [3] a bias-variance tradeoff is not sufficient to explain the accuracy of the CWA. More generally, thissuggests that the efficiency of heuristics might be due to suitable weighting operations rather than less-is-more effects.

Research demonstrates that when participants read a sentence about an agent in a certain location and then are showna pictured object, verification time is shorter whenever the pictured object matches the final object state implied by thesentence. Using a sentence-picture verification paradigm, we set out to investigate if the same pattern of results holdstrue when proprioceptive and kinesthetic experiences are considered. In three experiments participants read sentencesthat implied object state-changes as a function of the impact caused by differently weighted items (You drop a bowlingball/balloon on a tomato) followed by a pictured object in either a canonical (e.g., a round tomato) or a non-canonical (e.g.,a squashed tomato) state. The results showed that depictions of non-canonical objects showed the effect, but depictionsof canonical objects did not. Thus, representations of object states compete when non-visual features of the situation areimplied by the sentential context.

For modal approaches to conceptualization, concepts are couched by a corpus of cognitive processes such as perception,language, and action. Motor verbs offer an opportunity to evaluate the automatic onset of a clear spatial and modalcomponent in the mental representations of linguistic items. This study aimed to test the automaticity of these spatialcomponents when processing the meaning of concrete motor verbs. In one eye-tracking experiment, 31 participants viewed144 Spanish rebug sentences (i.e. escurrir ) with four schematic pictures (left, right, up, down) by a visual word paradigm.The study registered more and larger visits on the schematic pictures related to the motor and perceptive experience whendoing the action refereed by the verbs. Mainly, these findings add evidence on the automaticity of the modal componentof mental representations and help to understand how this component is ingrained in language for action meaning.

Working memory is the ability to actively maintain information in conscious awareness, carry out cognitive operations on it, and produce an outcome. Working memory holds a small amount of information in the mind and is used in the execution of cognitive tasks, in contrast to long-term memory, which is extensive. Many important cognitive behaviors, such as reading, reasoning, and problem-solving, require working memory because for each of these activities, some information must be maintained in an accessible state while new information is processed and potentially distracting information is ignored. While the effect of chess training on intelligence and academic performance has been examined, its impact on working memory needs to be studied. This study, funded by the Cognitive Science Research Initiative, Department of Science and Technology, Government of India, analyzed the effect of 2-year chess training on the working memory of children. A pretest–posttest with control group design was used. The randomly selected sample consisted of 88 children in the experimental group and 90 children in the control group for the baseline and first-year assessments. Children of both genders studying in school (grades 3 to 9) comprised the sample. At the second-year assessment, there were 80 children in the experimental group and 77 in the control group. The experimental group underwent weekly chess training for 2 years, while the control group was actively involved in sports and extracurricular activities offered by the school. Working memory was measured by two subtests of Wechsler Intelligence Scale for Children—Fourth Edition (WISC-IV) INDIA. The children were trained using Winning Moves curriculum, audiovisual learning method, hands-on chess training and recording the games using score sheets, and analyzing their mistakes. They were also trained in Opening theory, Checkmating techniques, End-game theory, and Tactical principles. Analysis of covariance revealed that the experimental group had significant gains in working memory compared to the control group. The present study supports a link between chess training and working memory. The transfer of skills acquired in chess training to the improvement of working memory could be attributed to the fact that while playing chess, children evaluate positions, visualize new positions in their mind, evaluate the pros and cons of each move, and choose moves based on the information stored in their mind. If working memory’s capacity could be expanded or made to function more efficiently, it could result in the improvement of executive functions as well as the scholastic performance of the child.

Although substantial work investigates childrens understanding of ordinal and magnitude-based relations, little work hasinvestigated childrens understanding of the vocabulary used for these relations and how relational language knowledgemay be constrained by symbolic number knowledge. In the current study, children were asked which of two numbers wasbigger/smaller than or before/after five. On close trials, the correct answer was 4 or 6 (one away from 5) and on far trials,the correct answer was 3 or 7 (two away from 5). We hypothesized that 4- to 6-year-olds understanding of ordinal relations(before/after) are initially constrained to refer to numbers immediately before/after (i.e., close values), but that this is notthe case for bigger/smaller comparisons. Preliminary results suggest this to be the case, with children performing betteron close trials than far trials for ordinal relations, but not magnitude relations.

Math anxiety is a common correlate of math performance. However, the causal direction of this relation is unclear.Research with young students is limited but critical for determining patterns of development. Students (N = 147) completedmath measures (i.e., number comparison, arithmetic fluency, and math problem solving) and math anxiety assessmentstwice, first in grade 2 (Mage = 7 years:10 months) and then a year later in grade 3. Correlational analysis revealed thatmath anxiety is related only to arithmetic fluency, related to other types of math performance. Cross-lagged analyses wereconducted to evaluate causal relations between math anxiety and arithmetic fluency. These analyses showed that arithmeticfluency in grade 2 predicted change in math anxiety from grade 2 to grade 3, however, math anxiety in grade 2 did notpredict the change in arithmetic fluency from grade 2 to grade 3. These results suggest that math anxiety may be the resultof poor math performance.

Learners ability to effectively monitor and apply cognitive (e.g., reading) and metacognitive (e.g., content evaluations)strategies in game-based learning environments (GBLEs) are influenced by internal factors such as prior knowledge. Thisstudy examined whether there were differences in learners strategy usage over time during learning with Crystal Island,a GBLE for microbiology, between high and low prior knowledge groups. Results indicated that learners with high priorknowledge had greater posttest scores, but spent less time reading. This is further influenced by relative time in gamewhere learners with high prior knowledge have greater reading durations at the start of the game, but smaller durationstowards the end compared to low prior knowledge learners. Learners’ metacognitive strategy usage did not differ betweenprior knowledge groups, but the use of this strategy increased over time. Implications for designing adaptive GBLEs fromlearners cognitive and metacognitive strategy use are discussed.

Learning in the STEM disciplines depends on high-quality STEM books, but choosing a textbook can be difficult inthe absence of objective measures of text quality. Here we compared two natural language processing approaches forevaluating text cohesion. In Coh-Metrix (Graesser et al. 2004), text cohesion is indicated by the mean cosine value of theall possible pairs of sentence vectors, with sentence vectors based on LSA. We introduce a new method for measuring textcoherence based on the deep learning language model RoBERTa (Liu et al., 2019). In this new approach, coherence ismeasured by determining the average predictability of all of the words in the text, with word predictability a function ofeach words linguistic context. Coherence as measured by RoBERTa more closely matched the coherence ratings of humanjudges than did Coh-Metrix. Implications for the assessment and categorization of STEM books are discussed.

Disfluency in speech leads listeners, even two-year-oldchildren, to expect the speaker to refer to novel and discourse-new objects. Previous evidence suggests this link betweendisfluency and discourse novelty is not driven simply bytracking of co-occurrence statistics connecting disfluency withreference to a new object, but also by integrating extra-linguistic information about the speaker’s perspective. Weasked whether children can attribute a speaker’s disfluency todifferent sources – language planning difficulty vs. distractionfrom the conversation. We tested children’s processing ofdisfluency when interacting with an engaged versus adistracted speaker. When the engaged speaker was disfluent,children looked more at a novel and discourse-new image thanat a familiar and just-named image, consistent with the existingliterature. This disfluency effect was attenuated when thespeaker was distracted, suggesting that four-year-old childrencan flexibly attribute a speaker’s disfluency to different sourcesin online interpretation of disfluent speech.

Humans have a remarkable capacity to reason about abstract relational structures, an ability that may support some ofthe most impressive, human-unique cognitive feats. Equality (or identity) reasoning has been a key case study of abstractrelational reasoning. This paper revisits the question of whether equality can be learned in non-symbolic neural networks.We find that simple neural networks are able to learn basic equality with relatively little training data. In a second casestudy, we show that sequential equality problems (learning ABA sequences) can be solved with only positive traininginstances. Finally, we consider a more complex, hierarchical equality problem, and find that this task can be solved witheither avast amount of training data or pre-training on basic equality. Overall, these findings indicate that neural modelsare able to solve equality-based reasoning tasks, suggesting that essential aspects of symbolic reasoning can emerge fromdata-driven,non-symbolic learning processes.

Children learning verbs benefit from seeing multiple events. Study 1 asks whether children can learn verbs when irrelevantevents are present, as is common in everyday contexts. Two- and 3-year-olds saw events in one of three experimentalconditions or one of two control conditions. They successfully extended the verbs only in the experimental conditions.Three-year-olds were more successful than were 2-year-olds, though the younger children could extend verbs. In Study 2,children saw similar events while an eye tracker tracked visual attention to events. Over trials, children looked longer atrelevant than irrelevant events, and maintained their looking to relevant events while increasing their looking to distractorevents. Two-year-olds performed at chance, but 3-year-olds extended the verbs. Together, these results show children canignore irrelevant events and extend new verbs by 3 years. Results reveal mechanisms for learning in everyday contextswhen verbs are heard in varied situations over time.

In deception games, deceivers must find ways to draw in unknowing bystanders, and bystanders must develop strategiesfor detecting falsehoods. What are the strategies that people use in these roles, and can computer systems also detect thesebehaviors? We address this question through text-based games of Mafia, wherein players are assigned to deceptive roles(mafia) or roles incentivizing detecting deception (bystanders). We find that participants adopt sophisticated role-basedstrategies, wherein the mafia, who are outnumbered but know the identities of all players, act carefully to secure the votesof the bystanders by speaking more even as verbose speakers tended to be eliminated. These role-based behaviors weredistinct enough that a computational classifier could distinguish between mafia and bystanders with 70.3% accuracy andoutperform human players. Understanding the systematic features defining honest and deceptive players advances ourability to automatically detect online deceit and grasp group dynamics in real-world collaboration.

Infants’ ability to attend actively and selectively to naturalistic stimuli is critical to early learning. Most studies on infant visual attention use screen-based paradigms wherein infants view stimuli on computer screens. Little is known about how infants observe others’ activities in everyday contexts. Using head- mounted eye-tracking, this study examined how infants distributed attention when observing their parents perform an everyday task – making peanut-butter and jelly sandwiches – in a home-like environment. Infant observers attended to parents’ activities less than adult observers in the same situation. However, when infants were engaged in action observation, their gaze patterns were distributed on task- relevant objects similarly to adult observers, suggesting they actively obtained rich visual input in this free-viewing situation. Moreover, infant-parent dyads coordinated visual attention during the food preparation task in similar ways as observed in other everyday tasks, such as toy play, suggesting sensorimotor processes play a critical role in coordinated attention.

Contemporary deep neural network based classification sys-tems are typically designed to learn information at a singlelevel of granularity from datasets in which all items occur withequal frequency. Humans, on the other hand, acquire informa-tion at several different levels of granularity from experiencesthat contain some items more frequently than others. This al-lows us to learn and differentiate frequent items better fromother items. We investigate the consequence of learning froma natural frequency/multi-level dataset in a deep neural net-work designed to model the human neocortex, complementedin some simulations with a replay buffer, playing the role ofthe human hippocampus. The NC network, when trained onits own, is able to learn more frequent items relatively quicklyand differentiate them better from other items, as human learn-ers do. However, the network’s performance on infrequentand unseen examples pays a price in generalization perfor-mance compared to a standard training regime. The replaybuffer serves to ameliorate these deficiencies, and we intro-duce a computationally and psychologically motivated replayweighting scheme that performs better than two alternatives.

Research in cognitive science and machine learning suggests that learning systems can use small subsets of valuabletraining items in order to quickly learn to achieve good task performance. We hypothesize that people often use smallsubsets of stored exemplars to quickly train new neural networks, called extemporaneous networks, when faced with tasksfor which they do not currently have dedicated networks. We explore this hypothesis using participants’ responses in abehavioral experiment to identify easy versus difficult training items. We find that a network confidence measure indicatesa network trained with a small set of good items provides the best account of participants’ reaction times. Furthermore,computer simulations demonstrate that learning systems can achieve good performance when trained with small sets ofeasy exemplars. Our results indicate that humans may complete tasks using extemporaneously-created networks trainedinternally on small datasets.

Adults tend to make biased inferences when they are givenbase-rates that conflict with individuating information (i.e., apersonality description). More recent work has shown thatchildren rely on individuating information by the age of 6,though 4-year-olds rely more on numerical information,arguably providing the more normative response (Gualtieri &Denison, 2018). In two experiments (N = 80 per experiment),we explored age differences in 4- and 6-year-old children’sability to integrate base-rate and individuating information bymanipulating the strength of the information provided. Four-year-olds’ responses reflected more base-rate use, regardless ofthe strength of the individuating information. Six-year-oldsweighed the information at hand, showing a general preferencefor the individuating information but relying more on the base-rates when the individuating information was less informative.Though younger preschoolers may overuse base-rateinformation, with development there is an increased sensitivitytoward individuating information and weighing information.

Language is rich in statistical regularities that capture meaningful, semantic links between words crucial for languagefluency. Words that can be combined to express meaningful ideas (e.g., drink-soda) reliably directly co-occur together,and words similar in meaning share patterns of co-occurrence (e.g. soda and milk share co-occurrence with drink). Here,we investigate whether children (4-year-olds) and adults can capitalize on these regularities to form new semantic linksbetween new and familiar words. Participants hear sentences in which new words directly co-occur or share co-occurrencewith familiar words. We then assess the formation of corresponding semantic links using an implicit, gaze-based measureand an explicit labeling measure. Results suggest that new semantic links form only from direct co-occurrence in chil-dren, and from both direct and shared co-occurrence in adults. This research is therefore uncovering the development ofmechanisms for building organized word knowledge from mere exposure to language.

Deep neural networks are increasingly being used in cognitivemodeling as a means of deriving representations for complexstimuli such as images. While the predictive power of thesenetworks is high, it is often not clear whether they also offeruseful explanations of the task at hand. Convolutional neuralnetwork representations have been shown to be predictive ofhuman similarity judgments for images after appropriate adap-tation. However, these high-dimensional representations aredifficult to interpret. Here we present a method for reducingthese representations to a low-dimensional space which is stillpredictive of similarity judgments. We show that these low-dimensional representations also provide insightful explana-tions of factors underlying human similarity judgments.

We present a new quantum-markovian model of two-alternative forced choice (2AFC) decision-making. We treatthe decision-making process as an accumulation of evidencebetween two competing alternatives, analogous to the drift dif-fusion model (DDM), in which the stimulus acts as a gener-ative process, emitting bits of information that are treated asquantum particles. The particles are acted on by a landscapedetermined by the agent’s experience with the task or stimu-lus, signal strength, and allocated cognitive control. We de-rive closed form expressions for success rates under both theinterrogation and free response paradigms. Under the free re-sponse paradigm, we show that this model reduces to a Markovprocess with closed form response time (RT) distributions thattake the form of inverse gaussians (IGs) with periodic noisecharacteristic to the task set. In the limit of long RT, the RTdistributions become smooth, recovering true IG distributionsanalogous to the standard DDM.

A key aspect of human reasoning is the ability to recognize abstract patterns in sequential data and then use those patternsto make novel inferences. Capturing this capacity for abstract reasoning is a major challenge for neural network modelsof human cognition. We present a recurrent neural network model of abstract sequential reasoning that is augmented witha form of episodic memory. This memory system enables the network to accomplish a form of variable-binding that haslong been considered an important component of abstract reasoning. We evaluate the model using visually grounded,abstract sequential reasoning and pattern completion tasks, including a task based on relations commonly found in RavensProgressive Matrices.

Meditation aims to improve ones psychological capacities by encouraging a calm and focused mind. Studies have ob-served positive benefits of meditation on health and cognition, such as reduced anxiety and enhanced executive control.Meditation has even been shown to alter brain structure and function. These benefits are mainly observed in long-termmeditators, with few studies examining the effects of short-term meditation. The current study investigated whether thereare immediate benefits of meditation. Electroencephalography was recorded while cognitive tasks were completed, wealso collected subjective well-being measures before and after exposure to either a brief meditation or a relaxation story.Post-intervention reaction time was shorter in meditators compared to the relaxation story. Both groups exhibited increasedwell-being, smaller N2s, and larger P3bs post-intervention. These results suggest that while mindfulness meditation mayimprove conflict monitoring, both interventions appear to improve well-being. Overall, there may be immediate benefitsof meditation for even novice meditators.

A hallmark of natural language is the innovative reuse of ex-isting words. We examine how adjectives extend over timeto describe nouns and form previously unattested adjective-noun pairings. Our approach is based on the idea of chainingthat postulates word meaning to extend by linking novel ref-erents to existing ones that are close in semantic space. Wetest this proposal by exploring a set of models that learn toinfer adjective-noun pairings from historical text corpora fora period of 150 years. Our findings across three diverse setsof adjectives support a chaining mechanism that is sensitiveto semantic neighbourhood density, best captured by an exem-plar model of category extension. This work sheds light on thegenerative cognitive mechanisms of word usage extension.

Infants learn to imitate and recognize words at an early age,but phonemic awareness develops at a later age, guided byacquisition of literacy for example. We investigate ahypothesis that speech representations in the brain are formedpartly due to articulatory-acoustic learning, and theserepresentations may be used as a basis when learning anadditional mapping to phonemes. We train a convolutionalrecurrent neural network, having an articulatory branch and aphonemic branch for multitask learning. When trained withreal conversational speech and aligned synthesizedarticulation, it is shown that the use of the articulatoryrepresentation boosts phoneme recognition accuracy, whenthe first convolutional layers are shared between the twobranches. It is hypothesized that representations involved inspeech perception formed in the brain during childhood maybe partly based on articulatory learning, and an additionalmapping from these low-level speech representations tophonemes has to be learned.

Our laboratory is involved in the development of an intelligent agent that operates a remotely piloted aircraft with twohuman teammates that communicate using text chat. The task is well-defined, but there are potentially numerous andunpredictable inputs during varied 40 minute missions. To assure reliability of agent behavior, we must run a largenumber of missions and analyze the behavior of the agent at milliseconds resolution. To support this requirement, we havedeveloped 1) a scripting language and control system that drives a mission with simulated teammates and environmentalevents, 2) scripted missions using actual chat input from a previous study, 3) output files for each mission that trace agentactions, situation state, and program events, and 4) scripts that analyze the output files based on performance heuristicsand differences from known-good output. This framework allows us to verify complex agent behavior as developmentprogresses.

Visual comparisons are ubiquitous in STEM education. We suggest that visual comparisons are carried out by a structuralalignment process that draws correspondences between analogs based on relational structure (Sagi, Gentner, & Lovett,2012). The spatial arrangement of images can influence visual comparisons by increasing or decreasing competitionfrom incorrect correspondences (Matlen, Gentner, & Franconeri, 2020). The present study tested whether this could beleveraged to help children compare complex STEM-related images. Seventh graders were shown drawings of skeletonscontaining an anomalous bone, either solo or paired with a correct standard. Children were more accurate at finding theanomaly when given a correct standard to compare to. On especially difficult trials in which skeletons were shown innon-canonical orientations (e.g., a cow oriented vertically), performance was enhanced when the spatial placement of thetwo skeletons was direct, minimizing competing correspondences. Thus, direct placement may help students comparecomplex unfamiliar images.

The perception of degraded speech input is essential in everyday life and is a major challenge in a variety of clinicalsettings, including for cochlear implant users. We investigated English speakers perception of noisy speech via an audio-visual lexical decision paradigm that modulated cross-modal integration, adaptive modulation of task difficulty, and ex-plicit feedback on response accuracy. We then tested whether proficiency with this task transferred to the perception ofnoisy audio stimuli in a post test. Although we observed a processing advantage for bimodal stimuli during training,particularly in the adaptive training condition, we did not observe any benefit from these conditions in the post test, nor abenefit associated with providing explicit feedback. These results are discussed in relation to other studies of audio-visualintegration and learning to perceive noisy speech, which may have observed different results due to more extensive trainingand different baseline proficiency levels.

Individuals are readily able to extract and encode statistical information from their environment (or statistical learning). However, the bulk of the literature has primarily focused on conditional statistical learning (i.e. the ability to learn joint and conditional relationships between stimuli), and has largely neglected distributional statistical learning (i.e. the ability to learn the frequency and variability of distributions). In this paper, we investigate how and how well distributional learning can be measured by exploring the relationship between and psychometric properties of two measures: discrimination judgements and frequency estimates. Reliable performance was observed in both measures across two different distributional learning tasks (natural and artificial). Discrimination judgements and frequency estimates also significantly correlated with one another in both tasks, and performance on all tasks accounted for the majority of variance across tasks (55%). These results suggest that distributional learning can be measured reliably, and may tap into both the ability to discriminate between relative frequencies and to explicitly estimate them.

Understanding individual differences in metacognitive ability may provide novel insights into how we think about ourown thinking. Past research has revealed individual differences in the extent to which grandiose and vulnerable narcissistsare metacognitively miscalibrated with respect to cognitive ability (Littrell, Fugelsang, & Risko, 2019). Building off ofthis work, we present a study examining the relations between trait narcissism across different cognitive tasks (e.g., verbalability, memory) and measures of metacognitive ability (e.g., bias, relative accuracy). Results indicate that while grandioseand vulnerable narcissists did not differ with respect to performance on cognitive tasks, they did significantly differ in theirperformance on certain metacognitive metrics. These results contribute to both our understanding of narcissism, individualdifferences in metacognitive ability, and the relation between different measures of metacognitive ability.

We test here the two-way influence of word and sentence level network topologies on learning. Participants viewed a self-paced stream of ”letters” in the form of novel glyphs. Glyphs were shown individually with words separated by spacesand sentences denoted with a prompt. In one condition, streams were generated via a walk along a scale-free graph at bothlevels, with nodes corresponding to either single glyphs (word level) or single words (sentence level). In a mismatchedcondition, sentences were generated from a graph with a scale-free degree distribution and words were instead generatedfrom a random graph. After exposure to the streams, participants completed familiarity judgments on words and sentences.Interestingly, performance on the word test was enhanced for participants exposed to mismatched topologies. Future workwill tease apart whether: (1) contrasting topologies boost learning; or (2) words that do not display scale-free degreedistribution are inherently easier to learn.

The SNARC effect is demonstrated in number judgment tasks when subjects are faster to respond to higher values withresponses made on the right and to lower values with responses made on the left. This effect has been found to beimpervious to handedness but works best for single digit values. Researchers speculate the reason for this robust effectto be a Mental Number Line (MNL) from which numbers, proceeding from 0-9 are oriented in a horizontal fashion fromleft to right. This follows when people consistently use text that proceeds from left to right, but for 1 or 2 billion ofpeople worldwide, text orientation proceeds right to left or top to bottom. The current experiments investigated whetherthe SNARC effect would be found among Egyptian Arabic-English biliterates who are highly proficient in both languagesand for whom reading and writing proceed from right-to-left, except for the numbering system. To our knowledge we havefound the first ever demonstration of the typical SNARC effect among this population.

One hallmark of human reasoning is that we can bring to beara diverse web of common-sense knowledge in any situation.The vastness of our knowledge poses a challenge for the prac-tical implementation of reasoning systems as well as for ourcognitive theories – how do people represent their common-sense knowledge? On the one hand, our best models of so-phisticated reasoning are top-down, making use primarily ofsymbolically-encoded knowledge. On the other, much of ourunderstanding of the statistical properties of our environmentmay arise in a bottom-up fashion, for example through asso-ciationist learning mechanisms. Indeed, recent advances in AIhave enabled the development of billion-parameter languagemodels that can scour for patterns in gigabytes of text from theweb, picking up a surprising amount of common-sense knowl-edge along the way—but they fail to learn the structure of co-herent reasoning. We propose combining these approaches, byem- bedding language-model-backed primitives into a state-of-the-art probabilistic programming language (PPL). On twoopen-ended reasoning tasks, we show that our PPL modelswith neural knowledge components characterize the distribu-tion of human responses more accurately than the neural lan-guage models alone, raising interesting questions about howpeople might use language as an interface to common-senseknowledge, and suggesting that building probabilistic modelswith neural language-model components may be a promisingapproach for more human-like AI.

Understanding how we acquire semantic knowledge is a central topic in cognitive science. In a now classic paper, Rogersand McClelland (2003) explored how a parallel distributed processing (PDF) model could recreate several important phe-nomena in semantic memory including how concepts are acquired, lost due to semantic dementia, and become organizedhierarchically. One well known limitation of this model, which was acknowledge by the original authors, is that thefeatures used in the model were largely hand coded. In this project we revisit this classic PDP account in light of mod-ern advances in neural network techniques. In particular, we show that we can recreate several of the predictions of theRogers and McClelland (2003) model in a network trained directly on raw pixel information from category exemplars.These results add realism to the original model while also showing how the principles of the model generalize to higherdimensional input spaces.

A key challenge for children in language acquisition is to learnthe mapping of words to mental categories, since this mappingvaries greatly from language to language. The errors childrenmake in this process are very informative regarding the devel-opment of lexical semantic categories; in particular, how chil-dren overextend a word to an inappropriate exemplar providesa window onto the mechanisms that underlie their categoriza-tion processes. We perform a large-scale quantitative analysisof the detailed patterns of children’s errors in the domain ofcolor, finding evidence that these error patterns are driven byan interaction between domain general principles of catego-rization, and children’s developing knowledge of the seman-tics of color. Our results suggest that, while domain generalprocesses play a role throughout development, their influencevaries across ages according to their use of domain specific(conceptual) knowledge, which gradually increases over time.