Netherlands

Contemporary formal models aim to capture group polarization as the result of deliberation between rational agents. Paradigmatic models do, however, rely on rather limited agents, casting doubt on the conclusion that group polarization can be rationally reconstructed. In this paper, we use a recently developed Bayesian agent-based model of deliberation to investigate this conclusion. This model avoids problems we identify in a group of influential Bayesian polarization models. Our case study shows that a simple mechanism produces realistic patterns of group polarization: limited exchange of evidence across a sparse social network.  We reflect on what our results mean for our formal understanding of rational group polarization.0

CogSci 2024

Rational Polarization: Sharing Only One’s Best Evidence Can Lead to Group Polarization

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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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** 
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**Thursday, July 25** 
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) 
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) 
1415: [Invited Symposium: Dynamics between minds and the environment](https://underline.io/events/465/sessions?eventSessionId=18016&searchGroup=lecture) 
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) 

**Friday, July 26** 
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) 
1030: [Rumelhart Symposium: Childhood as exploration](https://underline.io/events/465/sessions/18038/lecture/100346-childhood-as-exploration) 
1415: [Elman Prize Symposium](https://underline.io/events/465/sessions?eventSessionId=18053&searchGroup=lecture) 
1600: [Invited Symposium: Dynamics between minds](https://underline.io/events/465/sessions?eventSessionId=18065&searchGroup=lecture) 
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) 

**Saturday, July 27** 
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) 
1000: [Glushko Talks](https://underline.io/events/465/sessions?eventSessionId=18086&searchGroup=lecture) 
1500: [Invited Symposium: Dynamics within minds](https://underline.io/events/465/sessions?eventSessionId=18100&searchGroup=lecture) 


#### **CogSci 2024 Program Book**
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**The Program Book can be downloaded [here](https://drive.google.com/file/d/1yRVp1cBqRnAbdYi4GXWV_pZeyWNsRquv/view?usp=sharing).**

#### **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** 
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** 
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.

Humans seem to arbitrate between automatic and controlled processing by optimizing a trade-off between cognitive effort and performance. Previous research has described ways of how these costs and benefits can be quantified and how the trade-off between them can be performed. However, it remains unclear how the costs should be weighed relative to the benefits and how the cost of the arbitration mechanism itself factors in. Here, we derive measures for these separate factors from a single objective: the variational free energy. We demonstrate that by minimizing this objective, the trade-off between automatic and controlled processing as well as meta-control is optimized implicitly. As a proof of concept, we show that the congruency and proportion congruency effects in the Stroop task directly result from this optimization, given an environment with specific statistical regularities.0

A Rational Trade-Off Between the Costs and Benefits of Automatic and Controlled Processing

According to a common view in epistemology, a set of propositions is justified if it is coherent. Similarly, a new proposition should be accepted if it is coherent with the accepted body of beliefs. But what is coherence? And what, in turn, justifies the above claims? To answer these questions, various Bayesian measures of epistemic coherence have been proposed. Most of these measures are based on the prior probability distribution over the corresponding propositional variables. We criticize this ``static'' conceptualization of coherence and propose instead that the coherence of an information set is related to how well the information set responds when each of the propositions it contains is confirmed by an independent and partially reliable information source. The elaboration of this idea will show that the proposed ``dynamic'' perspective has several advantages and solves some open problems of coherentist epistemology. It also has implications for our understanding of reasoning and argumentation in science and beyond.0

A New Posterior Probability-Based Measure of Coherence

Metaphors are often used to intuitively communicate about movement. Here, expert pianists played two melodies while keeping eight different metaphors in mind, contrasting arousal level, valence direction, and metaphor type (action-related and emotion-related metaphors). Measures of keystroke timing and velocity were analyzed to assess the relative contribution of metaphor content and melodic note sequence to motor performance, alongside ratings of semantic similarity between metaphors. Using Bayesian multilevel models, results indicate that the arousal level of the metaphor has the most influence on keystroke force, average tempo, and tempo variability. Additionally, interactions with valence are seen for the timing measures, and for both valence and type in force. No effects of the melody sequence were found. Similarity ratings of metaphor pairs indicate that mental similarities largely mirror performance similarities. These findings show the potential effects of mental imagery on motor performance and have implications for teaching complex movements in practical settings.0

Metaphors in music performance: from semantics and motor performance to expressive communication

Emotions, including stress and anxiety, strongly influence cognition and learning experiences. This study investigates the impacts of stress on cognitive load during learning, considering baseline anxiety levels and fluctuating stress. With a focus on technology-based learning, a web-based HTML introduction module was used. Using a social stress test, 15 participants underwent a stressful situation during learning, while the control group of 15 were in a neutral condition. Results indicate significantly elevated stress levels in the experimental group throughout the experiment, with a corresponding decrease in learning performance. For high perceived difficulty, the stressed condition demonstrated a significant increase in response time compared to the control condition. In contrast, when experiencing low perceived difficulty, a significant difference in response time across conditions was not found. Findings emphasise the importance of managing stress in educational contexts to optimise learning outcomes in the evolving landscape of technology-based learning.0

The Effects of Stress and Anxiety in Technology-Based Learning Environments

Since their inception, drift diffusion models have been applied across a wide range of disciplines within psychology to uncover the mental processes that underlie perception, attention, and cognitive control. Our studies contribute to ongoing efforts to extend these models to abstract, social reasoning processes like moral or legal judgment. We presented participants with a set of social rules, while manipulating whether various
behaviors violated the rule’s letter and/or its purpose–––two independent standards by which to decide what constitutes a transgression. In this framework, cases that violate or comply with both a rule’s text and its purpose can be seen as congruent or ‘easy’ cases, and cases that elicit opposing verdicts as incongruent or ‘hard’ cases–––in a manner analogous to widely-studied conflict tasks in cognitive psychology. We recorded 34,573 decisions made by 364 participants under soft time pressure, and investigated whether hierarchical drift diffusion modeling could explain various behavioral patterns in our data. This approach yielded three key insights: (1) judgments of conviction were faster than judgments of acquittal owing to an overall bias (z parameter) toward conviction; (2) incongruent cases produced longer reaction times than congruent cases (an interference effect), due to differences in the rate of evidence accumulation (v parameter) across case-types; and (3) increases in the ratio of congruent-to-incongruent cases amplified the interference effect on reaction times, by fostering greater response caution—revealed by a larger threshold (or a parameter). Thus, our studies document dissociable effects of the drift diffusion components on rule-based decision-making, and illustrate how the cognitive processes that subserve abstract and social decision-making tasks, such as the enforcement of communal and legal rules, may be illuminated through the drift diffusion framework.0

Understanding rule enforcement using drift diffusion models

The present research evaluates a coherence-based network approach to moral judgement. Under this view, judgement is an outcome of achieving coherence between a network of causally interacting beliefs. Consistent with this, despite similar initial views, participants re-evaluated their beliefs and attitudes in support of their judgement, driving polarisation between individuals reporting competing judgements. Different properties of the dynamic network structure determined metacognitive properties of judgement such as confidence and perceived task difficulty. Whilst the judgement formation process involves revising beliefs and values to achieve a coherent arrangement, the nature of the judgement reached depends on the aggregate weight of these beliefs once the revision process is completed.0

A coherence-based approach to moral trade-offs

In this paper, we explored the attribution of causal responsibility in a causal chain of events, where an agent A instructs an intermediate agent B to execute some harmful action which leads to a bad outcome. In Study 1, participants judged B to be more causally responsible, more blameworthy, and more deserving of punishment than A. In Study 2, we explored the effect of proximity on judgments of the two agents by adding a third, subsequent contributing cause, such that B’s action no longer directly caused the final outcome. Participants judged both agents A and B to be less causally responsible and deserving of punishment (but not less blameworthy) when they were less proximal to the outcome, and there were no differences in judgments between the two agents. In Study 3, we varied whether each of the two agents (A and B) intended for the final outcome to occur. We find an interaction between role and intent, where participants only mitigated judgments for A when A did not intend for the outcome to occur – regardless of B’s intent. We discuss possible explanations for our findings and its implications for moral and legal decision-making.0

Attribution of Responsibility Between Agents in a Causal Chain of Events

Decisions about extinction risks are ubiquitous in everyday life and for our continued existence as a species. We introduce a new risky-choice task that can be used to study this topic: The Extinction Gambling Task. Here, we investigate two versions of this task: a Keep variant, where participants cannot accumulate any more earnings after the extinction event, and a Lose variant, where extinction also wipes out all previous earnings. We derive optimal solutions for both variants and compare them to behavioural data. Our findings suggest that people understand the difference between the two variants and their behaviour is qualitatively in line with the optimal solution. Further, we find evidence for risk-aversion in the Keep condition but not in the Lose condition. We hope that this task can facilitate further research on this vital topic.0

Introducing the Extinction Gambling Task

To date, no account of lie-truth judgement formation has been capable of explaining how core cognitive mechanisms such as memory encoding and retrieval are employed to reach a judgement of either truth or lie. One account, the Adaptive Lie Detector theory (ALIED: Street, Bischof, Vadillo, & Kingstone, 2016) is sufficiently well defined that its assumptions may be implemented in a computational model. In this paper we describe our attempt to ground ALIED in the representations and mechanisms of the ACT-R cognitive architecture and then test the model by comparing it to human data from an experiment conducted by Street et al. (2016). The model provides a close fit to the human data and a plausible mechanistic account of how specific and general information are integrated in the formation of truth-lie judgements.0

Combining individuating and context-general cues in lie detection

Human learning does not stop at solving a single problem. Instead, we seek new challenges, define new goals, and come up with new ideas. What drives people to disrupt the existing conceptual landscape and create new things? Here, we examine the decision to create new things under different levels of expected returns. We formalize innovation as stochastically recombining existing ideas, where successful and more complex combinations generate higher returns. This formalization allows us to cast innovation-seeking as a Markov decision process, and derive optimal policies under different settings. Data collected through an online behavioral experiment confirm our prediction that people should invest more time and effort in seeking innovations when they know the chances of success are high and the potential new ideas would be rewarding. However, people also deviate from being optimal, both innovating more and less than they should in different settings.0

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