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Snoddy and Kurtz (2020) demonstrated spontaneous transfer of relational categories to new learning. Recognition memory data suggested that transfer was driven by schematization during learning. In the present study, we explored whether schema abstraction underlies transfer and recognition effects. Participants were assigned to condition based on the type of initial learning: classification with comparison, supervised observation with comparison, single-item supervised observation, or baseline (no learning). After initial learning, participants underwent a study phase and recognition test on novel stimuli followed by a target category learning task involving the same underlying category structures expressed in a new domain. During the recognition test, all conditions led to increased false alarms relative to baseline. Only the comparison conditions exhibited analogical transfer on the target category learning task. Results suggest that comparison facilitates the transfer of relational categories (due to schema abstraction), but recognition memory effects may be driven by more general categorization mechanisms.

CogSci 2021

Comparison Promotes the Spontaneous Transfer of Relational Categories

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
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* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
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* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
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#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

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### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year!<br><br>

**Your Program Chairs,**
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**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

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To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

Learning the sounds of a new language depends on the ability to learn novel auditory categories. Multidimensional categories, whether speech or nonspeech, can be learned through feedback and different category structures are proposed to recruit separate cognitive and neural mechanisms. There is substantial individual variability in learning; however, it is rare to compare learning of different categories in the same individuals. Understanding the sources of variability has theoretical implications for category learning. In this study, we trained the same participants on three types of multidimensional auditory categories. Participants learned nonspeech rule-based, nonspeech information-integration, and Mandarin speech categories. Learning of all category types was related across individuals and differences in working memory similarly supported learning across tasks. There was substantial variability in learning outcomes and strategies used to learn the categories. There are multiple paths to successful learning and appreciation of individual variability is essential to understanding the mechanisms of learning.

Individual Variability in Strategies and Learning Outcomes in Auditory Category Learning

Team interaction involves the division of labor and coordination of actions between members to achieve a shared goal. Although the dynamics of interactions that afford effective coordination and performance have been a focus in the cognitive science community, less is known about how to generate these flexible and adaptable coordination patterns. This is important when the goal is to design artificial agents that can augment and enhance team coordination as synthetic teammates. Although previous research has demonstrated the negative impact of model-based agents on the pattern of interactions between members using recurrence quantification methods, more recent work utilizing deep reinforcement learning has demonstrated a promising approach to bootstrap the design of agents to team with humans effectively. This paper explores the impact of artificial agent design on the interaction patterns that are exhibited in human-autonomous agent teams and discusses future directions that can facilitate the design of human-compatible artificial agents.

Interaction Flexibility in Artificial Agents Teaming with Humans

Many category learning experiments use supervised learning (i.e., trial-by-trial feedback). Most of those procedures use deterministic feedback, teaching participants to classify exemplars into consistent categories (i.e., the stimulus i is always classified in category k). Though some researchers suggest that natural learning conditions are more likely to be inconsistent, the literature using probabilistic feedback in category learning experiments is sparse. Our analysis of the literature suggests that part of the reason for this sparsity is a relative lack of flexibility of current paradigms and procedures for designing probabilistic feedback experiments. The work we report here offers a novel paradigm (the Probabilistic Prototype Distortion task) which allows researchers greater flexibility when creating experiments with different p(category|feature) probabilities, and also allows parametrically manipulating the amount of randomness in an experimental task. In the current work, we offer a detailed procedure, implementation, experimental results and discussion of this novel procedure. Our results suggest that by designing experiments with our procedures, the experimental setup allows subjects to achieve the desired classification performance.

Designing probabilistic category learning experiments: The probabilistic prototype distortion task

Using the visual-word paradigm, the present study investigated the effects of word onset and offset masking on the time course of non-native spoken-word recognition in the presence of background noise. In two experiments, Dutch non-native listeners heard English target words, preceded by carrier sentences that were noise-free (Experiment 1) or contained intermittent noise (Experiment 2). Target words were either onset- or offset-masked or not masked at all. Analyses showed that onset masking delayed target word recognition more than offset masking did. These results suggest that – in line with contemporary models of spoken-word recognition – non-native listeners strongly rely on word onset information when hearing words in noise.

The Effects of Onset and Offset Masking on the Time Course of Non-Native Spoken-Word Recognition in Noise

Traditional statistics instruction emphasizes a .05 significance level for hypothesis tests. Here, we investigate the consequences of this training for researchers’ mental representations of probabilities –– whether .05 becomes a boundary, i.e., a discontinuity of the mental number line, and alters their perception of differences between p-values. Graduate students (n = 25) with statistical training viewed pairs of p-values and judged whether they were ‘similar’ or ‘different’. After controlling for covariates, participants were more likely and faster to judge p-values as ‘different’ when they crossed the .05 boundary (e.g., .047 vs. .052) compared to when they did not (e.g., .027 vs. .032). This categorical perception effect suggests that traditional statistical instruction creates a psychologically real divide between so-called statistically significant and non-significant p-values. Such a distortion is undesirable given modern approaches to statistical reasoning that de-emphasize dichotomizing p-values.

Categorical perception of p-values

How people represent categories—and how those representations change over time—is a basic question about human cognition. Previous research has suggested that people categorize objects by comparing them to category prototypes in early stages of learning but use strategies that consider the individual exemplars within each category in later stages. However, many category learning experiments do not accurately reflect the environmental statistics of the real world, where the probability that we encounter an object changes over time. Our goal in this study was to introduce memory constraints by presenting each stimulus at intervals corresponding to the power-law function of memory decay. Since the exemplar model relies on the individual’s ability to store and retrieve previously seen exemplars, we hypothesized that adding memory constraints that better reflect real environments would favor the exemplar model more early on compared with later. Confirming our hypothesis, the results illustrate that under realistic environmental statistics with memory constraints, the exemplar model’s advantage over the the prototype model decreases over time.

The Dynamics of Exemplar and Prototype Representations Depend on Environmental Statistics

A new phenomenon is the spread and acceptance of “fake news” on an individual user level, facilitated by social media such as Twitter. So far, state of the art socio–psychological theories and cognitive models focus on explaining how the accuracy
of fake news is judged on average, with little consideration of the individual. This paper takes it to a new level: A breadth of core models are comparatively assessed on their
predictive accuracy for the individual decision maker, i.e., how well can models predict an individual’s decision before the decision is made. To conduct this analysis, it requires the raw responses of each individual and the implementation and adaption of theories to predict the individual’s response. We used two previously collected large data sets with a total of 3309 participants and searched for, analyzed and refined existing classical and heuristic modeling approaches. The results suggest that classical reasoning, sentiment analysis models and heuristic approaches can best predict the “Accept” or “Reject” response of a person. A hybrid model that combines those models outperformed the prediction of all individual models pointing to an adaptive tool-box.

When Does an Individual Accept Misinformation?

Finding and exploiting loopholes is a familiar facet of fable, law, and everyday life. But cognitive, computational, and empirical work on this behavior remains scarce. Engaging with loopholes requires a nuanced understanding of goals, social ambiguity, and value alignment. We trace loophole behavior to early childhood, and we propose that exploiting loopholes results from a conflict in actors' goals combined with a pressure to cooperate. A survey of 260 parents reporting on 425 children reveals that loophole behavior is prevalent, frequent, and diverse in daily parent-child interactions, emerging around ages five to six and tapering off from around ages nine to ten into adolescence. A further experiment shows that adults consider loophole behavior in children as less costly than non-compliance, and children increasingly differentiate loophole behavior from non-compliance from ages four to ten. We discuss limitations of the current work together with a proposal for a formal framework for loophole behavior.

Loopholes, a Window into Value Alignment and the Learning of Meaning

Co-creating meaning in conversation is challenging. Success is often determined by people’s abilities to coordinate their language in strategic ways to signal problems and to align mental representations. Here we explore one set of grounding mechanisms, known as interactive linguistic alignment, that makes use of the ways people re-use, i.e., “align to,” the lexical, semantic, and syntactic forms of others’ utterances. In particular, the focus is on the temporal development of multi-level linguistic alignment and examine how its expression is related to communicative outcomes within a unique collaborative problem-solving paradigm. The primary task, situated within an educational video game, requires creative thinking between three people where the paths for possible solutions are highly variable. We find that over time interactions are marked by decreasing lexical and syntactic alignment, with a trade-off of increasing semantic alignment. However, a greater semantic alignment does not necessarily translate into better team performance. Overall, these findings provide greater clarity on the role of grounding mechanisms in complex and dynamic collaborative problem-solving tasks.

Multi-Level Linguistic Alignment in a Virtual Collaborative Problem-Solving Task

Legal documents, in the form of terms of service agreements and other private contracts, are now an increasingly prevalent part of everyday life. While legal documents have long been acknowledged to be difficult to understand without training, it remains an open question whether the ever-increasing exposure to contracts might have mitigated this difficulty. Moreover, insofar as this difficulty has persisted, there remains no systematic analysis of which linguistic structures contribute most heavily to the processing difficulty of legal texts, nor whether this difficulty is heightened for those with less language experience. Here, we investigate these issues, and in a well-powered experiment find evidence that (a) both recall and comprehension of legal propositions in a contract are hindered by use of a legal register relative to plain-English translations; (b) certain linguistic structures, such as center-embedding, hinder recall to a greater degree than others, such as passive voice; and (c) language experience influences comprehension of legal propositions. Surprisingly, language experience did not influence recall, nor was there an interaction between legal register and language experience on recall or comprehension. These findings suggest that legal language poses heightened difficulties for those with less language experience--who tend to be of lower socioeconomic status and with diminished access to the justice system--and that eliminating complex features of legalese would benefit those of all reading levels.

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Individual Variability in Strategies and Learning Outcomes in Auditory Category Learning

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Individual Variability in Strategies and Learning Outcomes in Auditory Category Learning

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Classification Versus Observation through Within- and Between-Category Comparison

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