Netherlands

Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation. However, the agent-based models (ABMs) commonly used for such simulations often over-simplify human behavior. We propose a new approach to simulating opinion dynamics based on populations of Large Language Models (LLMs). Our findings reveal a strong inherent bias in LLM agents towards producing accurate information, leading simulated agents to consensus in line with scientific reality. This bias limits their utility for understanding resistance to consensus views on issues like climate change. After inducing confirmation bias through prompt engineering, however, we observed opinion fragmentation in line with existing agent-based modeling and opinion dynamics research. These insights highlight the promise and limitations of LLM agents in this domain and suggest a path forward: refining LLMs with real-world discourse to better simulate the evolution of human beliefs.

**Authors:**

Yun-Shiuan Chuang: University of Wisconsin - Madison; Agam Goyal: University of Wisconsin - Madison; Nikunj Harlalka: University of Wisconsin - Madison; Siddharth Suresh: University of Wisconsin-Madison; Robert Hawkins: University of Wisconsin-Madison; Sijia Yang: University of Wisconsin - Madison; Dhavan Shah: University of Wisconsin - Madison; Junjie Hu: University of Wisconsin-Madison; Timothy T Rogers: University of Wisconsin- Madison

CogSci 2024

Simulating Opinion Dynamics with Networks of LLM-based Agents

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** 
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** 
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.

poster

Programming is a complex problem-solving domain, requiring the coordination of different types of knowledge and skills. The present study investigates expert and novice programming problem solving by analyzing talk-aloud transcripts and the code generated. Based on this analysis a set of basic goal and step components used by novice and expert programmers are identified, which will inform on the generation of cognitive models in the next phase of this research.

**Authors:**

Maria Vorobeva: Carleton University; Suhaylah B Carim Bacor: Carleton University; Mary Alexandria Kelly: Carleton University

Investigating Expert and Novice Programming Differences on Problems of Varying Complexity

Sketch mapping is a method used to investigate an individual's cognitive map of the surrounding environment. Although the method of sketch mapping is used in numerous experiments to investigate people's spatial perception, there is no comprehensive method to analyze sketch maps. Most methods are quantitative and limited to counting features or determining the (metric) spatial distortion in sketch maps. This paper aims to bridge this gap by proposing a comprehensive sketch map analysis method evaluating the completeness, level of generalization, and qualitative spatial accuracy. Our approach can assist researchers in geography, psychology, and education in systematically evaluating spatial knowledge, independent of specific research questions and experimental scenarios.

**Authors:**

Angela Schwering: University of Muenster; Jakub Krukar: University of Muenster; Charu Manivannan: Institute for geoinformatics, University of Muenster

SketchMapia: A comprehensive way to analyse sketch maps

Humans can learn new concepts from a small number of examples by drawing on their inductive biases. These inductive biases have previously been captured by using Bayesian models defined over symbolic hypothesis spaces. Is it possible to create a neural network that displays the same inductive biases? We show that inductive biases that enable rapid concept learning can be instantiated in artificial neural networks by distilling a prior distribution from a symbolic Bayesian model via meta-learning, an approach for extracting the common structure from a set of tasks. We use this approach to create a neural network with an inductive bias towards concepts expressed as short logical formulas. Analyzing results from previous behavioral experiments in which people learned logical concepts from a few examples, we find that our meta-trained models are highly aligned with human performance.

**Authors:**

Ioana Marinescu: Princeton University; R. Thomas McCoy: Yale University; Tom Griffiths: Princeton University 

Distilling Symbolic Priors for Concept Learning into Neural Networks

This study investigates whether linguistic influences can affect the manifestation of lateral space-valence mappings in people’s minds. Although most oral languages and cultures of the world have expressions and conventions that associate the good with the right space, this association seems to be body-specific: right-handers associate positive concepts with the right side and negative concepts with the left side, left-handers have the opposite association, and the size of the effect of body specificity does not vary with linguistic and cultural conventions. Thus, it is widely believed that this conceptual metaphor only depends on the body. However, sign languages do not seem to have any conventional association between lateral space and valence, and a recent study has shown that signers do not associate valence with lateral space, opening the possibility of a causal influence of language. We here set to replicate this surprising and controversial finding by comparing a sign language group, consisting of Spanish and Chinese Sign Language users, with an oral Spanish control group on the widely applied in this field “Bob task”. Supporting the prior finding, Spanish language participants associated the good with their dominant side of space, but signers did not. We discuss potential interpretations of this result.

**Authors:**

Karla Tann: Maastricht University; Pablo Solana: University of Granada; Marc Ouellet: University of Granada; Heng Li: Sichuan International Studies University; Julio Santiago: University of Granada

Does Sign Language Shape Lateral Space-Valence Associations?

Artificial neural networks trained using deep-learning methods to solve a simple reference game by optimizing a task-specific utility develop efficient semantic categorization systems that trade off complexity against informativeness, much like the category systems of human languages do. But what exact type of structures in the semantic space could result in efficient categories, and how are these structures shaped by the contexts of communication? We propose a NN model that moves beyond the minimal dyadic setup and show that the emergence of convexity, a property of semantic systems that facilitates this efficiency, is dependent on the amount of variability in communication contexts across partners. We use a method of input representation based on compositional vector embeddings that is able to achieve a higher level of communication success than regular non-compositional representation methods, and can achieve a better balance between maintaining the structure of the semantic space and optimizing utility.

**Authors:**

Vlad C Nedelcu: University of Edinburgh; Daniel Lassiter: University of Edinburgh; Kenny Smith: University of Edinburgh

Variability in communication contexts determines the convexity of semantic category systems emerging in neural networks

Human cognition is highly flexible– even when posed with novel questions and situations, we are able to manipulate existing knowledge to draw reasonable conclusions. All human cognition requires flexibility, yet we lack a well-justified computational explanation for how humans might learn and manipulate conceptual knowledge in a way that allows for cognitive flexibility. Here, we develop and test a novel model of concept learning that has roots in multitask learning and multidimensional scaling. In contrast to previous models of concepts, which are often defined through a single-task learning mechanism, our multitask learning model frames concepts as latent vector representations that are learned through observations across multiple task domains. Testing using simulated data shows that our multitask model is capable of generalizing knowledge across tasks, and additionally may also generalize better than single-task models within tasks. Our results so far support a multitask learning framework for capturing concept learning and generalization ability.

**Authors:**

Dyana Muller: UC Berkeley; Steven Piantadosi: UC Berkeley

A multitask model of concept learning and generalization

The discrete entities of symbolic systems and their explicit relations make symbolic systems more transparent and easier to communicate. This is in contrast to neural systems, which are often opaque. It is understandable that psychologists often pursue interpretations of human cognition using symbolic characterizations, and it is clear that the ability to find symbolic variables within neural systems would be beneficial for interpreting and controlling Artificial Neural Networks (ANNs). Symbolic interpretations can, however, oversimplify non-symbolic systems. This has been demonstrated in findings from research on children’s performance on tasks thought to depend on a concept of exact number, where recent findings suggest a gradience of counting ability in children’s learning trajectories. In this work, we take inspiration from these findings to explore the emergence of symbolic representations in ANNs. We demonstrate how to align recurrent neural representations with high-level, symbolic representations of number by causally intervening on the neural system. We find that consistent, discrete representations of numbers do emerge in ANNs. We use this to inform the discussion on how neural systems represent quantity. The symbol-like representations in the network, however, evolve with learning, and can continue to vary after the neural network consistently solves the task, demonstrating the graded nature of symbolic variables in distributed systems.

**Authors:**

Satchel Grant: Stanford University; Zhengxuan Wu: Stanford University; Jay McClelland: Stanford University; Noah Goodman: Stanford University

Symbolic Variables in Distributed Networks that Count

How does globalization impact the interaction between perception and language? Building on Berlin and Kay's foundational study of color naming, we recruited 2,280 online participants speaking 22 different languages. We show that color naming maps differ structurally across languages, even among internet users living in (mostly) industrial societies. We use Large Language Models (LLMs) to simulate the limits of globalization by reproducing the naming task with a highly multilingual artificial agent with access to global digital information. We show that while the LLM has access to all languages, it has language-specific color representations and the number of color terms is correlated across humans and LLMs. However, LLMs use more color terms than humans, indicating differences in the representation. These results suggest that globalization has not removed cultural distinctions in color concepts, as language continues to be a key factor in the diversity of perception and meaning.

**Authors:**

Jakob Pete Niedermann: Max Planck Institute for Empirical Aesthetics; Ilia Sucholutsky: Princeton University; Raja Marjieh: Princeton University; Elif Çelen: Max Planck Institute for Empirical Aesthetics; Tom Griffiths: Princeton University ; Nori Jacoby: Max Planck Institute for Empirical Aesthetics; Pol van Rijn: Max Planck Institute for Empirical Aesthetics

Studying the Effect of Globalization on Color Perception using Multilingual Online Recruitment and Large Language Models

Drift-Diffusion Models (DDMs) are a widely-used class of models that assume an accumulation of evidence during a quick decision. These models are often used as measurement models to assess individual differences in cognitive processes, such as an individual’s evidence accumulation rate and response caution. An additional underlying assumption of these models is that there is internal noise in the evidence accumulation process. However, fitting DDMs to experimental choice-response time data alone cannot yield estimates of an individual's evidence accumulation rate, caution, and internal noise at the same time. This is due to an intrinsic joint-unidentifiability of these parameters when fitting DDMs to behavioral data. We introduce methods of estimating these parameters at the same time with additional data types. The methods to estimate model parameters rely on Bayesian inference and simulation-based Bayesian inference. We show why these methods are useful without making strong assumptions.

**Authors:**

Michael D Nunez: University of Amsterdam; Anna-Lena Schubert: University of Mainz; Gidon T. Frischkorn: University of Zurich; Klaus Oberauer: University of Zurich

Using additional data types to identify the unidentifiable components of cognition during decision-making

It has been suggested that humans mentally simulate the outcomes of their actions when making decisions. However, this process can be challenging in real-world decision-making, which typically involves temporally extended decision trees with numerous potential outcomes. Here, we demonstrate with a computational model that temporally extended decisions can be achieved with just a few forward simulations, formalized as rollouts. We also show that, under resource constraints, performing many partial (shallow) rollouts can yield more favorable outcomes than performing fewer full (deep) rollouts. Additionally, our model captures behaviors traditionally attributed to reflexive pruning or satisficing strategies without the need for explicit heuristics, providing an alternative explanation for these phenomena. Finally, we show that the dynamics of value estimation over successive rollouts closely resemble evidence accumulation models. Our framework offers a plausible mechanism for temporally extended decision-making and provides insights into the neural underpinnings underlying this process.

**Authors:**

Sixing Chen: New York University; Kristopher T. Jensen: University College London; Marcelo G Mattar: New York University

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