The question of whether people store absolute magnitude information or relative local comparisons of magnitudeshas remained unanswered despite persistent efforts over the last three decades to resolve it. Absolute identification is one of themost rigorous experimental benchmarks for evaluating theories of magnitude representation. We characterize difficulties withboth absolute and relative accounts of magnitude representation and propose an alternative account that potentially resolvesthese difficulties. We postulate that people store neither long-term internal referents for stimuli, not binary comparisons ofsize between successive stimuli. Rather, they obtain probabilistic judgments of size differences between successive stimuliand encode these for future use, within the course of identification trials. We set up a Bayesian ideal observer model for theidentification task using this representation of magnitude and propose a memory-sampling based approximation for solving it.Simulations suggest that the model adequately captures human behavior patterns in absolute identification.

This paper provides empirical evidence that human decision- makers use prospective regret minimization as their dominant decision strategy when regret calculations are cognitively easier to perform, and use expected utility maximization when they aren't. We designed a simple decision problem wherein utility maximization and expected regret minimization yield distinctly difference choices, and manipulated the cognitive effort involved in making regret calculations across respondent samples to arrive at our results. While previous research has associated ecological considerations like sense of responsibility and familiarity with this difference, we show that, at least in experimental settings, cognitive calculability in regret space appears to predominantly drive this difference. We also show that this preference for regret minimization can be countermanded by changing the distribution of options presented to the respondent, posing a challenge to simple sequential accounts of strategy selection learning which sequence strategy selection and application in order.

The classic drift diffusion model of the 2AFC choice processassumes that observers select evidence accumulation thresh-olds to optimize some desired level of accuracy across the ex-periment. We argue that it is more ecologically natural to as-sume that decision-makers set this threshold adaptively, usinginformation from recent trials to adjust it for upcoming ones.To test this hypothesis, we designed and conducted a pair ofrandom dot motion discrimination experiment where the co-herence parameter that controls task difficulty varies across tri-als in a predictable manner. To analyze data from these exper-iments, we also designed a hierarchical drift diffusion modelthat allows decision-makers to adapt their evidence thresholdbased on the trend of difficulty of previous trials. Our resultssuggest that observers rationally integrate cross-trial informa-tion about trial difficulty into perceptual decision-making byadjusting their internal evidence thresholds. We briefly discussthe implications of the existence of such trial-level effort infer-ence on contemporary models of the choice process.

We present a series of propositions that explains why people find sitting quietly in a dark room strongly aversive (Wilson et al., 2014). (i) Conflict-monitoring is an essential cognitive function; likely performed at the level of information processing conflicts (Botvinick et al, 2001) (ii) Memory is sensitized to processing conflicts; if a conflict has not been resolved in real-time, it is recalled when the mind is disengaged (iii) This is mind-wandering (Smallwood et al, 2003) (iv) Since mind-wandering privileges conflict recall for resolution, and resolving conflicts requires effort, mind-wandering becomes aversive (v) To avoid mind-wandering, a common strategy is to increase intensity of activity, so mind has no time to wander (vi) But increasing density of activity increases the number of possible information conflicts, which further deepens aversion to sitting quietly (vii) This is anxiety Understanding the cognitive mechanics of this spiral of anxiety may help break it

Computational models of multiple object tracking (MOT) presuppose the existence of non-conceptual indexes in visual perception, and as a result predict that ID (identification) performance on MOT tasks should be no worse than tracking performance for the same stimuli. However, empirical evidence suggests that ID performance is worse than tracking performance in MOT. We propose a computational model of MOT that is able to account for several empirical results related to tracking performance without the use of indexes and thus avoids yoking tracking performance to ID performance. We also test our model empirically, contrasting it with an existing index-based model, and show that an assumption that avoids indexes and instead incorporates an explicit (rather than an implicit) mechanism for identity maintenance accounts well for the variation in ID performance with increasing number of targets in MOT with visually identical objects.

Many real-life decisions, such as booking a vacation or selecting a partner, involve relatively cost-free sampling of options up to a terminal decision point, beyond which the choice becomes costly to reverse. Such problems can be formulated as optimal stopping problems (OSPs), such as the famous secretary problem. Although human behavior on optimal stopping problems has been studied extensively, much of the literature has focused on the behavior of individual decision-makers operating using a binary payoff function. In this study, we use an OSP with a continuous payoff function to study how individuals’ decisions differ from the collective decision of groups of three members working together. An independent threshold model offered the best explanation for the behavior of both individuals and groups. We found groups performed significantly better than individuals, with individuals consistently waiting too long to make a choice relative to the optimal strategy. Groups are also more decisive in following their internal thresholds, which are also different than a simple average of member thresholds. Finally, we also found a lack of long-term learning in OSPs for groups, a trend previously documented in individuals.

Video game playing is an extremely structured domain where algorithmic decision-making can be tested without adverse real-world consequences. While prevailing methods rely on image inputs to avoid the problem of hand-crafting state space representations, this approach systematically diverges from the way humans actually learn to play games. In this paper, we design object-based input representations that generalize well across a number of video games. Using these representations, we evaluate an agent's ability to learn games similar to an infant - with limited world experience, employing simple inductive biases derived from intuitive representations of physics from the real world. Using such biases, we construct an object category representation to be used by a Q-learning algorithm and assess how well it learns to play multiple games based on observed object affordances. Our results suggest that a human-like object interaction setup capably learns to play several video games, and demonstrates superior generalizability, particularly for unfamiliar objects. Further exploring such methods will allow machines to learn in a human-centric way, thus incorporating more human-like learning benefits.

The empirical measurement of mental effort is an important problem in the cognitive sciences. Recently, researchers have adopted econometric tools to attempt to characterize mental effort in terms of monetary costs foregone. Such efforts yield a very helpful calculation device - a money utility of mental effort. However, since the opportunity cost of applying mental effort in any given situation is measured with respect to time rather than money in most ecologically reasonable settings, it is even more desirable to obtain a measure of the time utility of mental effort. In the absence of direct measurements of men- tal effort, such a task has proved econometrically challenging. We use a recently developed direct measure of mental effort to characterize its time utility, finding that it is approximately linear in effort. We discuss some implications of this result for current theories of mental effort, as well as for practical applications.