Comprehensive datasets used for modeling endeavours in syllogistic reasoning research usually contain only a single conclusion per task for each subject. However, this means that no information about the other conclusions is provided, preventing the distinction between conclusions that were rejected, conclusions deemed to be valid but not the preferred conclusion and conclusions that were not considered at all. In this work, we present a multiple choice dataset containing all conclusions that participants considers valid. The data is compared to datasets with other response designs, and an extensive evaluation is performed to assess the impact of the response design on the predictive performance of cognitive models. Finally, our results are discussed and put into perspective.

Directed forgetting (DF) studies demonstrate that humans can intentionally forget item information. In the presented study, participants learned three lists of words. After studying the first two lists (L0+L1), we cued half of the participants to forget these lists before learning a new list (L2), the other half remembered all three lists. Typically, such a forget instruction impedes recall of previously-studied to-be-forgotten words but enhances memory for subsequent to-be-remembered items. Instead of recalling the words, we asked participants to select the list a word was studied in, assessing how DF affected both item- and list-memory. In line with the context-change hypothesis, list-memory for L1 did not differ between the two groups suggesting that even if recall of to-be-forgotten words is typically impaired, list-memory is still intact. Furthermore, after the forget instruction, participants’ list-memory was enhanced particularly for early L2 words, providing evidence for a reset of encoding or rehearsal processes.

In syllogistic reasoning research, humans are predominantly evaluated on their capabilities to judge whether a conclusion necessarily follows from a set of premises. To tackle this limitation, we build on work by Evans, Handley, Harper, and Johnson-Laird (1999), and present two studies where we asked participants for possible and likely conclusions. Combined with previous data (containing necessary), we present a comprehensive dataset with responses for all syllogisms, offering individual patterns for all three argument types - a first of its kind. We discovered that likely serves as a middle ground between possible and necessary, paving the way to further investigate biases and preferences. Generally, individuals were able to handle the different notions, yet tended to interpret quantifiers in a pragmatic way, overlooking logical implicatures. Finally, we tested mReasoner, an implementation of the Mental Model Theory, and concluded that it was not able to capture the patterns observed in our data.

Judging if a conclusion follows logically from a given set of premises can depend much more on the believability than on the logical validity of the conclusion. This so-called belief bias effect has been replicated repeatedly for many decades now. An interesting observation is, however, that process models for deductive reasoning and models for the belief bias have not much of an overlap - they have largely been developed independently. Models for the belief bias often just implement first order logic for the reasoning part, thereby neglecting a whole research field. This paper aims to change that by presenting a first attempt at substituting the first order logic components of two models for belief, selective scrutiny and misinterpreted necessity, with two state of the art approaches for modeling human syllogistic reasoning, mReasoner and PHM. In addition, we propose an approach for extending the traditionally dichotomous predictions to numerical rating scales thereby enabling more detailed analysis. Evaluating the models on a dataset published with a recent meta-analysis on the belief bias effect, we demonstrate the general success of the augmented models and discuss the implication of our extensions in terms of the limitations of the current focus of research as well as the potential for future investigation of human reasoning.

Over time, there has been a change in how students acquire and exchange information, with laptops and smartphones becoming increasingly important. The use of technology has evolved from being restrained du to the classroom to being crucial due to the COVID-19 pandemic. As education shifts towards hybrid models, students are now expected to learn at home, which can be challenging as excessive technology usage and a lack of self-discipline can lead to more distractions. This paper examines the effects of the influence of these distractions with the help of two concepts similar to assignments in students' lives: text comprehension & memorization, as well as example-based learning, in which the function of an apparatus was to be tested and described. The results show that distraction does not affect text comprehension but decreases information retention. Additionally, participants required more trials and repetitions to understand schemes in example-based learning when distracted.

In human reasoning, the logical form and content of assertions has an impact on the conclusions people derive. Yet, research in different reasoning domains often focuses primarily either on the structure (syllogisms) or on content effects (conditionals). Both explore human reasoning capabilities and response patterns aiming to get a better understanding of how humans reason, so a further step would be to examine whether effects are carried over from one domain to another. To gain a deeper understanding of the general mechanisms underlying reasoning, it is valuable to adopt perspectives from other domains. Towards that, we conducted a study where the syllogistic content was "translated" from established conditional contents that focus on suppressing logically (in-)valid inferences. We found a belief bias effect - a decrease of the effect of the logical structure, but not a suppression effect related to logical (in-)validity, alternatives and disablers, like in conditionals.

In assessing the sense of agency (SoA) in the field, researchers are often confronted with the limitations of conventional methods (e.g., retrospectivity, task-disruption). Generally, facial analysis offers a promising possibility to overcome these obstacles by directly collecting objective data in real-time and without any need for contact. This paper examines the applicability of face tracking for SoA assessment by investigating whether facial expressions can be used to derive information about the extent to which users feel in control in interactions with technical systems. In a laboratory experiment (N = 27) based on the dot control task, we manipulated SoA via task difficulty and explored the effects on facial expression patterns. Three action units in the region of the mouth were observed to be characteristic facial indicators of SoA. These findings support the suitability of facial expressions for SoA evaluation as well as the potential of face tracking for monitoring SoA in various practical fields of application (e.g., automated driving).

Syllogistic reasoning is one of the oldest domains of reasoning research and has made great advances in understanding and modeling human reasoning processes. However, the field was mostly focused on a traditional set of quantifiers originating in first-order logic, thereby neglecting the large variety of quantifiers humans use when engaging in reasoning in their everyday life. The present work makes three main contributions: (I) we conducted a study yielding a dataset covering all traditional syllogisms and tasks containing generalized quantifiers ``most'' and ``most not'', providing a starting point for existing theories and models to transition to generalized quantifiers. (II) based on the dataset, we analyze the impact that the additional quantifiers have on the reasoning behavior. (III) We investigated the reasoning behavior with respect to the difference between traditional and generalized quantifiers, gaining insights into some of the peculiarities of the domain of generalized syllogisms.

The prevailing focus on aggregated data and the lacking group-to-individual generalizability it entails have recently been iden-tified as a major cause for the low performance of cognitivemodels in the field of syllogistic reasoning research. This arti-cle attempts to add to the discussion about the performance ofcurrent syllogistic reasoning models by considering the param-eterization capabilities some cognitive models offer. To thisend, we propose a model evaluation setting targeted specifi-cally toward analyzing the capabilities of a model to fine-tuneits inferential mechanisms to individual human reasoning data.This allows us to (1) quantify the degree to which models areable to capture individual human reasoning behavior, (2) ana-lyze the efficiency of the parameters used by models, and (3)examine the functional differences between the prediction ca-pabilities of competing models on a more detailed level. Weapply this method to two state-of-the-art models for syllogisticreasoning, mReasoner and the Probability Heuristics Model,analyze the obtained results and discuss their implication withrespect to the general field of cognitive modeling.

Given considerable advancements in automated driving systems, the day when autonomous vehicles will be regularly present in our everyday life is impending. It is, therefore, very significant to put emphasis on the effect that giving up autonomy might have on an individual. We take into consideration an experimental data set regarding participants' reported discomfort levels to tackle the following questions: How can we represent a discomfort measurement in a meaningful way? Using this representation, can future discomfort reactions be predicted? We identify key features, identify baseline models, and develop a new approach based on the k-nearest neighbor model to considerably improve the prediction of individual user's discomfort measurements. A discussion of limits and potentials concludes the paper.