Netherlands

Learning traps are false beliefs that entrench themselves by discouraging the exploration required to correct them. In previous lab experiments, these learning traps have proven remarkably difficult to prevent. Here, we investigate whether learning traps remain stable in contexts in which both individual and social learning are possible. In two of our three experiments, we found that learners trapped by a false belief were significantly more likely to escape a learning trap when they were able to observe another decision-maker&#39;s choices (without observing their outcomes). However, social learning was not a panacea. Social learning was constrained by the challenge of inferring others&#39; beliefs, and trapped learners struggled to learn from partners with sub-optimal decision rules, even when their partner&#39;s choices were informative. Collectively, these results suggest that while social learning can help overcome the limits of individual learning, learning from others comes with its own challenges and limitations.

**Authors:**

Rheza Budiono: New York University; Catherine Hartley: New York University; Todd M Gureckis: New York University

CogSci 2024

How does social learning affect stable false beliefs?

### 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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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.

Learning traps are false beliefs that entrench themselves by discouraging the exploration required to correct them. In previous lab experiments, these learning traps have proven remarkably difficult to prevent. Here, we investigate whether learning traps remain stable in contexts in which both individual and social learning are possible. In two of our three experiments, we found that learners trapped by a false belief were significantly more likely to escape a learning trap when they were able to observe another decision-maker's choices (without observing their outcomes). However, social learning was not a panacea. Social learning was constrained by the challenge of inferring others' beliefs, and trapped learners struggled to learn from partners with sub-optimal decision rules, even when their partner's choices were informative. Collectively, these results suggest that while social learning can help overcome the limits of individual learning, learning from others comes with its own challenges and limitations.

**Authors:**

Rheza Budiono: New York University; Catherine Hartley: New York University; Todd M Gureckis: New York University

technical paper

The 46th Annual Meeting of the Cognitive Science Society was an in-person meeting held in Rotterdam, The Netherlands at the Postillion Hotel & Conference Centre.

**ON-DEMAND PROGRAM ACCESS AFTER THE CONFERENCE**<br>
Recordings of the invited program are now available. You can access them by clicking on the 'Schedule' icon on the left. Select view by week and navigate to the day and time slot of the recording you wish to view. Click on the time slot to access the recording.

Recordings available (click on each recording to view):

**Thursday, July 25**<br>
0900: [Keynote Speaker Morgan Barense on Enhancing real-world event memory](https://underline.io/events/465/sessions/17997/lecture/99084-dynamics-between-minds-and-the-environment)<br>
1000: [Gleitman Award Winner Isabelle Dautriche on Language Foundations: Insights from acquisition, communication, cognition, and more](https://underline.io/events/465/sessions/18000/lecture/99085-gleitman-talk)<br>
1415: [Invited Symposium: Dynamics between minds and the environment](https://underline.io/events/465/sessions?eventSessionId=18016&searchGroup=lecture)<br>
1700: [Rumelhart Prize Presentation Speaker Alison Gopnik on Exploit, explore, empower: Three ages and three intelligences](https://underline.io/events/465/sessions/18028/lecture/99161-rp1-rumelhart-prize-presentation)<br>

**Friday, July 26**<br>
0900: [C.L. de Carvalho-Heineken Prize Keynote Speaker Kia Nobre on Focusing in memory](https://underline.io/events/465/sessions/18035/lecture/99162-hp1-c-l-de-carvalho-heineken-prize-keynote-address)<br>
1030: [Rumelhart Symposium: Childhood as exploration](https://underline.io/events/465/sessions/18038/lecture/100346-childhood-as-exploration)<br>
1415: [Elman Prize Symposium](https://underline.io/events/465/sessions?eventSessionId=18053&searchGroup=lecture)<br>
1600: [Invited Symposium: Dynamics between minds](https://underline.io/events/465/sessions?eventSessionId=18065&searchGroup=lecture)<br>
1745: [Keynote Speaker Andrea E. Martin on Neural dynamics encode the structure and statistics of language](https://underline.io/events/465/sessions/18077/lecture/99271-neural-dynamics-encode-the-structure-and-statistics-of-language)<br>

**Saturday, July 27**<br>
0900: [Keynote Speaker Gregor Schöner on How higher cognition emerges from the dynamics of strongly interacting neural populations](https://underline.io/events/465/sessions/18083/lecture/99273-dynamics-within-the-mind)<br>
1000: [Glushko Talks](https://underline.io/events/465/sessions?eventSessionId=18086&searchGroup=lecture)<br>
1500: [Invited Symposium: Dynamics within minds](https://underline.io/events/465/sessions?eventSessionId=18100&searchGroup=lecture)<br>


#### **CogSci 2024 Program Book**
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#### **Letter from the President**

Welcome to the 2024 Meeting of the Cognitive Science Society in Rotterdam! CogSci 2024 brings together a large community of cognitive scientists who have traveled here from around the world, as well as a smaller group of remote presenters. I would like to extend an especially warm welcome to our first-time CogSci meeting attendees, and hope that 
their experience inspires them to join our future meetings for many years to come.

I want to thank this year’s conference co-chairs, Larissa K Samuelson, Stefan Frank, Mariya Toneva, Allyson Mackey, and Eliot Hazeltine, for putting together an amazing series of invited talks and symposia around the theme of Dynamics of Cognition. The co-chairs also deserve credit for handling a record-breaking number of submissions and producing a truly exciting conference program 

Beyond the invited talks and symposia dedicated to this year’s conference theme, I would like to highlight the presentations and events honoring the 
Rumelhart, Elman, Gleitman and C .L . de Carvalho Heineken prize winners, including the Rumelhart reception on Thursday. As you may know, we are recording all of the invited talks and symposia, and all prize-winners’ talks, and will be making this content available after the conference. 

The Cognitive Science Society relies on the volunteer efforts of our Governing Board members who work on matters related to our membership; conference policies; diversity and inclusion, international, and outreach initiatives; prizes and much more. We welcome interest from Society members who would like to become more involved in what we do. The 
Society is deeply grateful to Rick Dale and Andrea Bender, who serve as editors of the two Society journals, Cognitive Science and Topics in Cognitive Science respectively. We especially acknowledge our editors’ generosity in donating their stipends each year to D&I and outreach efforts. 

This year’s Annual Meeting depends critically on the support of Marischal De Armond and his team at Podium Conference Specialists: Sharon Zwack, Cendrine De Vis, Sarah-Kate Burke, and Rachel Waller worked tirelessly to secure the venue and handled a multitude of logistical and organizational issues, from arranging coffee breaks and receptions to making sure that a huge number of posters fits comfortably into the available space. Finally, and importantly, I would like to acknowledge the Cognitive Science Society Executive Officer, Erica Wojcik, for managing the complex and ever expanding sphere of our Society’s activities.

I hope you enjoy this year’s meeting and the many different opportunities to engage with the vibrant community of cognitive scientists gathered here. I also hope you get a chance to explore the great city of Rotterdam with its many attractions, museums, restaurants and scenic views. If you’d like to talk more about our Society, or simply want to say hello, please stop by!

![](https://assets.underline.io/markdown_image/1/image/3c66d0e21fff30e204df1138ffd15173.jpeg)

**Anna Papafragou**  
President, Cognitive Science 
Society 2023-2024

**CODE OF CONDUCT**<br>
By attending the CogSci 2024 Conference, you are required to adhere to the society’s [Code of Conduct](https://drive.google.com/file/d/16-6KkptF0Gn3ZYGJDlpqpwTdImw45Ng0/view?usp=drive_link).

**ABOUT THE COGNITIVE SCIENCE SOCIETY**<br>
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.
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The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Disparate impact rules are formally neutral but indirectly discriminate against protected groups (i.e., by targeting a characteristic that is more prevalent in a given group). Because these rules are not obviously malicious, they have been widely enacted to circumvent policies against explicit discrimination. In a series of four experiments, we show that adults and children are sensitive to the moral implications of disparate impact rules. However, we also find that they are more accepting of these rules when strong justification is provided, compared to rules with no justification. Crucially, demographic differences also impact people's judgments of disparate impact rules and their creators. We find that conservatives and those from groups not directly affected by the rule tend to be more accepting of it. By studying people's reasoning about disparate impact rules, this work aims to identify the mechanisms by which these rules may evade detection. Finally, we discuss how these insights may inform the development of interventions that highlight the problematic effects of indirectly discriminatory policies.

**Authors:**

Aarthi Popat: Yale University; Jamie Amemiya: Occidental College; Gail D. Heyman: UC San Diego; Caren M. Walker: University of California San Diego

The Hair Club for Boys: How children and adults judge disparate impact rules

Spatial demonstratives are deictic expressions used to point to a referent with language. In the standard view, they encode a spatial proximal\distal contrast between “near” (this) and “far” (that) from the speaker. Several studies have shown that such contrast maps on a perceptual contrast between peripersonal and extrapersonal space. Still, other factors beyond spatial distance influence demonstrative choice. Here we investigate whether the proximal/distal contrast maps also onto a more general contrast between being in physical control/not in control of a target referent. Participants were presented with two circles (red and blue) on a screen. They had to move them with the mouse to find the target circle (the one with two gaps). One circle followed the mouse trajectory (controllable), while the other moved randomly in the center of the screen (not controllable). Unknown to the participants, the gaps only appeared if the stimuli crossed a distance threshold. Importantly, participants had to use stimulus controllability to solve the task. They were instructed to answer by indicating the target to the experimenter using this/that and red/blue (in Italian questo/quello and rosso/blu). Results show that participants used the proximal demonstrative more frequently to refer to the target stimulus when in control. These findings suggest that, similarly to spatial distance, physical control influences demonstrative choice.

**Authors:**

Angelo Mattia Gervasi: Sapienza University of Rome; Anna Borghi: Sapienza University of Rome; Francesco Mannella: National Research Council; Luca Tummolini: National Research Council of Italy

Spatial demonstratives and physical control

Toddlers can only say one or two words at a time. What do they choose to talk about? We report preliminary results (N=167; mean; 19.5 months) from a pre-registered online experiment on productive language. Toddlers saw six movies. A curtain opened on an introductory scene, the parent closed their eyes, and a new event happened. The curtain closed and the child was asked what happened. On two trials the unseen event was new to the parent (Novel event); on two trials, one of two animals ate the only food in the scene (Agent ambiguous); on two trials, the only animal ate one of the two foods (Patient ambiguous). We predicted that toddlers would selectively generate informative utterances (i.e., referring to the novel event, the agent, and the patient, respectively). Toddlers' productive language was indeed sensitive to what listeners’ know; however, unlike adults, they selectively referred to information in common ground.

**Authors:**

Bradli Washington: Massachusetts Institute of Technology; Sienna Radifera: Massachusetts Institute of Technology; Melissa Kline Struhl: Massachusetts Institute of Technology; Laura Schulz: Massachusetts Institute of Technology

From the mouths of babes: Toddlers’ early word production favors information in common ground

In this paper we investigate whether subject doubling in French is affected by the Uniform Information Density (UID) principle, which states that speakers prefer language encoding that minimizes fluctuations in information density. We show that, other factors being controlled, speakers are more likely to double the NP subject when it has a high surprisal, thus providing further empirical evidence to the UID principle which predicts a surprisal-redundancy trade-off as a property of natural languages. We argue for the importance of employing GPT-2 to investigate complex linguistic phenomena such as subject doubling, as it enables the estimation of subject surprisal by considering a rather large conversational context, a task made possible by powerful language models that incorporate linguistic knowledge through pre-training on extensive datasets.

**Authors:**

Yiming Liang: Universiteit Gent; Pascal Amsili: Université Sorbonne Nouvelle; Heather Burnett: CNRS; Vera Demberg: Saarland University

Uniform information density explains subject doubling in French

This study investigates whether homophone competitors are activated during typewriting and to which extent such activation is modulated by syntactic category. In two experiments, we compared the typewriting of homophone pairs in high vs. low conflict sentences (i.e., both homophones vs. only one homophone in the sentence, respectively) in a sentence dictation task (Experiment 1) and in a question-answering task (Experiment 2). The homophone pairs either belonged to the same or different syntactic categories. In Experiment 1, we found a homophone interference effect in accuracy, independent of conflict and syntactic category. In Experiment 2, this effect was replicated, but in addition, participants were slower to type homophones in a high vs. a low conflict context. Our results show a robust, lexically-situated homophone interference effect, regardless of conflict and syntactic category, but when deeper processing of the sentence is involved, conflict starts to play a role.

**Authors:**

Merel Muylle: Ghent University; Nazbanou Nozari: Indiana University; Robert Hartsuiker: Ghent University

On idle idols and ugly icons: Do homophones create interference in typing?

Autoregressive Large Language Models (LLMs) trained for next-word prediction have demonstrated remarkable proficiency at producing coherent text. But are they equally adept at forming coherent probability judgments? We use probabilistic identities and repeated judgments to assess the coherence of probability judgments made by LLMs. Our results show that the judgments produced by these models are often incoherent, displaying human-like systematic deviations from the rules of probability theory. Moreover, when prompted to judge the same event, the mean-variance relationship of probability judgments produced by LLMs shows an inverted-U-shaped like that seen in humans. We propose that these deviations from rationality can be explained by linking autoregressive LLMs to implicit Bayesian inference and drawing parallels with the Bayesian Sampler model of human probability judgments.

**Authors:**

Jian-Qiao Zhu: Princeton University; Tom Griffiths: Princeton University 

Incoherent Probability Judgments in Large Language Models

Humans extract useful abstractions of the world from noisy sensory data. Serial reproduction allows us to study how people construe the world through a paradigm similar to the game of telephone, where one person observes a stimulus and reproduces it for the next to form a chain of reproductions. Past serial reproduction experiments typically employ a single sensory modality, but humans often communicate abstractions of the world to each other through language. To investigate the effect language on the formation of abstractions, we implement a novel multimodal serial reproduction framework by asking people who receive a visual stimulus to reproduce it in a linguistic format, and vice versa. We ran unimodal and multimodal chains with both humans and GPT-4 and find that adding language as a modality has a larger effect on human reproductions than GPT-4's. This suggests human visual and linguistic representations are more dissociable than those of GPT-4.

**Authors:**

Sreejan Kumar: Princeton University; Raja Marjieh: Princeton University; Byron Zhang: Princeton University; Declan Campbell: Princeton University; MICHAEL Y HU: New York University; Umang Bhatt: New York University; Brenden Lake: NYU; Tom Griffiths: Princeton University 

Comparing Abstraction in Humans and Machines Using Multimodal Serial Reproduction

We investigate whether more accurate representation of syntactic
information in Transformer-based language models is
associated with better alignment to brain activity. We use fMRI
recordings from a large dataset (MOUS) of a Dutch sentence
reading task, and perform Representational Similarity Analysis
to measure alignment with 2 mono- and 3 multilingual
language models. We focus on activity in a region known
for syntactic processing (the Left posterior Medial Temporal
Gyrus). We correlate model-brain similarity scores with the
accuracy of dependency structures extracted from model internal
states using a labelled structural probe. We report three key
findings: 1) Accuracy of syntactic dependency representations
correlates with brain similarity, 2) The link between brain similarity
and dependency accuracy persists regardless of sentence
complexity, although 3) Sentence complexity decreases dependency
accuracy while increasing brain similarity. These results
highlight how interpretable, linguistic features such as syntactic
dependencies can mediate the similarity between language
models and brains

**Authors:**

Abraham Jacob Fresen: University of Amsterdam; Rochelle Choenni: ILLC; Micha Heilbron: University of Amsterdam; Willem Zuidema: University of Amsterdam; Marianne de Heer Kloots: Institute for Logic, Language and Computation

Language Models That Accurately Represent Syntactic Structure Exhibit Higher Representational Similarity To Brain Activity

Large scale neural models show impressive performance across a wide array of linguistic tasks. Despite this they remain, largely, black-boxes - learning vector-representations of their input that prove difficult to interpret. This limits our ability to understand what they learn, and when the learn it, or characterise why they often fail to generalise systematically. To address this we introduce a novel approach to interpretability that looks at the mapping a model learns from sentences to representations as a kind of language in its own right. In doing so we introduce a set of information-theoretic measures that quantify how structured a model's representations are with respect to its input, and when during training that structure arises. Our measures are fast to compute, grounded in linguistic theory, and can predict which models will generalise best based on their representations. We use these measures to describe two distinct phases of training a transformer: an initial phase of in-distribution learning which reduces task loss, then a second stage where representations becoming robust to noise. Generalisation performance begins to increase during this second phase, drawing a link between generalisation and robustness to noise. Finally we look at how model size affects the structure of the representational space, showing that larger models ultimately compress their representations more than their smaller counterparts.

**Authors:**

Henry Conklin: University of Edinburgh; Kenny Smith: University of Edinburgh

Deep Learning as Language Emergence: Noise Drives Generalisation

Cognitive control is thought to regulate the conflict between stability---maintaining the current task in the face of distraction---and flexibility---switching to a new task of greater priority. However, evidence conflicts regarding when and to what extent stability and flexibility trade-off. A normative theory of flexibility and stability may help clarify when and why we should expect such trade-offs to occur. Towards such a theory, we model task-switching as a problem of decision-making under uncertainty, in which the decision-maker must simultaneously infer both the identity of a stimulus and the task governing the correct response to that stimulus. We find that optimal behavior is either extremely stable or extremely flexible, but not both, indicating a normative basis for a trade-off between the two. However, we also show that a sub-optimal but more realistic decision-maker exhibits behavior between these two extremes, and more closely resembles experimental data.

**Authors:**

Seth Madlon-Kay: Duke University; John Pearson: Duke University; Tobias Egner: Duke University

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