Netherlands

The open-ended nature of real world decision-making requires individuals to have a representation of what is possible in a given context. Humans are exceptionally good at solving the problem of narrowing down the potentially unbounded space of possible actions, and have been shown to navigate this space in a specific manner, generating their best options first and their most idiosyncratic options last. Recently, large language models (LLMs) have demonstrated striking performance in communication and reasoning. Given, on the one hand, that the information encoded in LLMs might reflect the underlying conceptual structure employed by the human mind, and, on the other hand, that humans seem to rely on such conceptual structures when generating options, here we ask whether LLMs navigate the space of possible options in a human-like way. We used independent human ratings and semantic similarity to compare options generated by human subjects with those generated by an LLM in a series of open-ended decision contexts. We found that, while LLMs share some of the global patterns of option generation found in humans, the sequences of options they generate tend to exhibit a different trajectory pattern within the semantic space of options relevant for each different context. In particular, LLMs more tends to return to the already visited regions of the semantic space, while humans progress more linearly.

**Authors:**

Alina Dracheva: Dartmouth College; Jonathan Phillips: Dartmouth College

CogSci 2024

Different Trajectories through Option Space in Humans and LLMs

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
 [**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)

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CogSci 2022

The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





poster

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**
<div style="position:relative;padding-top:0;width:900px;height:500px;"><iframe style="position:absolute;border:none;width:100%;height:100%;left:0;top:0;" src="https://online.fliphtml5.com/ebtyf/cble/" seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>


**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.

Interlingual Respeaking (IR) is a new technique that enables real-time subtitling in a different language. This cognitively demanding technique involves collaboration between a language professional and automatic speech recognition software (ASR), creating a human-AI interaction (HAII) environment. Integrating technological tools with an individual's internal cognitive resources establishes an extended cognitive system. However, different types of errors are observed in terms of output accuracy. Our ESRC-funded research found that working memory (WM) (backward span) has a negative relationship with omissions, where content is dropped out (e.g., to save time). Nevertheless, additions, where the human adds content (e.g., to clarify meaning) and correctness, where form-related issues arise (such as grammar mistakes), had an inverse relationship with the N-back Task (the simultaneous maintenance, updating, and processing of WM). These findings suggest that the IR errors involve diverse types of WM resources.

**Authors:**

Anna-Stiina Wallinheimo: University of Surrey; Dr Simon Evans: University of Surrey; Elena Davitti: University of Surrey

How does working memory predict errors in Human-AI Interaction?

Human groups are able to converge to more accurate beliefs through deliberation, even in the presence of polarization and partisan bias --- a phenomenon known as the ``wisdom of partisan crowds.'' Large Language Models (LLMs) are increasingly being used to simulate human collective behavior, yet few benchmarks exist for evaluating their dynamics against the behavior of human groups. In this paper, we examine the extent to which the wisdom of partisan crowds emerges in groups of LLM-based agents that are prompted to role-play as partisan personas (e.g., Democrat or Republican). We find that they not only display human-like partisan biases, but also converge to more accurate beliefs through deliberation, as humans do. We then identify several factors that interfere with convergence, including the use of chain-of-thought prompting and lack of details in personas. Conversely, fine-tuning on human data appears to enhance convergence. These findings show the potential and limitations of LLM-based agents as a model of human collective intelligence.

**Authors:**

Yun-Shiuan Chuang: University of Wisconsin - Madison; Nikunj Harlalka: University of Wisconsin - Madison; Siddharth Suresh: University of Wisconsin-Madison; Agam Goyal: 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

The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents

Saliency-based Explainable AI (XAI) methods have been commonly used for explaining computer vision models, but whether they could indeed enhance user understanding at different levels remains unclear. We showed that for object detection AI, presenting users with AI ’s output for a given input was sufficient for improving feature-level and some instance-level user understanding, particularly for false alarms, and providing saliency-based explanations did not have additional benefit. This was in contrast to previous research on image classification models where such explanations enhanced understanding. Analyses with human attention maps suggested that humans already attended to features important for AI’s output in object detection and thus could infer AI’s decision-making processes without saliency-based explanations. However, it did not enhance users’ ability to distinguish AI’s misses and hits, or system-level understanding. Therefore, the effectiveness of saliency-based explanations is task-dependent, and alternative XAI methods are required for object detection models to better enhance understanding.

**Authors:**

Ruoxi Qi: University of Hong Kong; Guoyang Liu: Shandong University; Jindi Zhang: Huawei; Janet Hsiao: Hong Kong University of Science & Technology

Do Saliency-Based Explainable AI Methods Help Us Understand AI’s Decisions? The Case of Object Detection AI

Empirical work on people’s perceptions of AI advisors has found evidence for both “algorithm aversion” and “algorithm appreciation.” We investigated whether people’s responses to human or AI legal advisors fall along separate paths of central information processing (persuasive message effects) and peripheral processing (source effects of AI vs. human). In two survey studies, people were as strongly persuaded by the advice of an AI as that of a human advisor; nonetheless, people's approval of and trust in the AI advisor was consistently lower. This dissociated response may reconcile the contradictory findings of algorithm aversion and algorithm appreciation.

**Authors:**

Zeynep Aydin: Brown University; Bertram F. Malle: Brown University

Dissociated Responses to AI: Persuasive But Not Trustworthy?

Large Language Models have taken the cognitive science world by storm. It is perhaps timely now to take stock of the various research paradigms that have been used to make scientific inferences about "cognition" in these models, human cognition, or learning more generally. We define several non-mutually-exclusive "metaphors" for these research paradigms---GPT-ology, Alien Intelligences, and Existence Proofs---and review recent papers that have studied LLMs under these paradigms. In doing so, we discuss their claims as well as challenges to scientific inference under these various paradigms. We highlight several outstanding issues about LLMs that have to be addressed to push our science forward: closed-source vs open-sourced models; forming conventions on new task "hyperparameters" like instructions and prompts; reproducibility; and (the lack of visibility of) training data.

**Authors:**

Desmond Ong: The University of Texas at Austin

GPT-ology, Alien Intelligences, Existence Proofs: How should we think about LLMs in Cognitive Science?

This research explores the capabilities of Large Language Models (LLMs) to engage in abstract and concrete thought processes, challenging the common belief that LLMs are incapable of human-like, abstract thinking. Drawing upon the Construal Level Theory (Trope & Liberman, 2010), we demonstrate how prompts tailored for each construal level (abstract versus concrete) influence LLMs’ performance in tasks requiring different cognitive approaches. Our key findings include: 1) LLMs exhibit a statistically significant difference in construal level depending on the prompt conditions, and 2) LLMs display superior performance in tasks aligned with the prompted construal level; sentiment analysis in concrete conditions and natural language inference in abstract conditions. This research contributes to the scientific understanding of LLMs, offering practical insights for their effective use in tasks necessitating diverse cognitive capabilities.

**Authors:**

Seung Joo Yoo: Seoul National University; Sangah Lee: Seoul National University

Large Language Models Show Human-Like Abstract Thinking Patterns: A Construal-Level Perspective

The human cognitive system exhibits remarkable flexibility
and generalization capabilities, partly due to its ability to form
low-dimensional, compositional representations of the envi-
ronment. In contrast, standard neural network architectures
often struggle with abstract reasoning tasks, overfitting, and
require extensive data for training. This paper investigates the
impact of the relational bottleneck—a mechanism that focuses
processing on relations among inputs—on the learning of fac-
torized representations conducive to compositional coding. We
demonstrate that such a bottleneck not only improves general-
ization and learning efficiency but also aligns network perfor-
mance with human-like behavioral biases. Networks trained
with the relational bottleneck developed orthogonal represen-
tations of the datasets latent features, reflecting the factor-
ized structure thought to underlie human cognitive flexibil-
ity. Moreover, the relational network mimics human biases
towards regularity without pre-specified symbolic primitives,
suggesting that the bottleneck fosters the emergence of abstract
representations akin to symbols.

**Authors:**

Declan I. Campbell: Princeton University; Jonathan Cohen: Princeton University

A Relational Inductive Bias for Dimensional Abstraction in Neural Networks

Previous research about the intensity of emotional facial expressions has relied on stimulus sets of morphed facial expressions that have been generated artificially. Ecologically valid open-access facial stimulus sets with varying intensities of multiple different expressions are rare. However, there is a growing need for a validated facial stimulus set that would include multiple levels of intensities. This study aimed to develop and test the psychometric properties of a stimulus set with real facial expressions (8 men and 8 women) with 11 intensity levels for five facial expression categories: angry, happy, neutral, surprised and sad. 52 individuals rated the valence, arousal and intensity of the 656 stimuli. Descriptive statistics, internal consistency of the rating for each stimulus, emotion category, and intensity level were described. The stimuli and summary data are available upon request (https://osf.io/f8ews/).

**Authors:**

Gerly Tamm: Ghent University

Development and Validation of the Facial Expression Intensity Stimulus Set (FEISS)

We present a novel theory and neural process model of visual selective attention to answer long-standing questions in the field of visual attention. We show that the model with fixed parameter values can explain the unexpected efficiency of triple conjunction search (Nordfang & Wolfe, 2014), the influence of a task-irrelevant size singleton on search (Proulx, 2007), and how a third correlated but task-irrelevant feature improves search efficiency (Found, 1998). It also accounts for critical findings in the attention capture literature without the need to introduce different modes (Bacon & Egeth, 1994), signal-suppression (Gaspelin, Leonard, & Luck, 2015; Gaspelin & Luck, 2018; Lien, Ruthruff, & Hauck, 2021) or an attentional window (Theeuwes, 1992, 2023), shedding new light on recent debates.

**Authors:**

Raul Grieben: Ruhr-Universität Bochum; John Spencer: University of East Anglia; Gregor Schöner: Ruhr-Universität Bochum

Visual selective attention: Priority is all you need

Models of bottom-up visual attention such as the ``saliency map'' predict overt gaze under laboratory conditions while subjects view static images or videos while seated. Here, we show that the saliency map model predicts gaze at similar rates even when applied to video from a head-camera as part of a wearable eye-tracking system (Tobii Pro Glasses 2) while subjects drive an automobile or are passively driven while sitting in the front passenger-side seat. The ability of saliency to predict gaze varies depending on the driving task (saliency better predicts passenger gaze) and external conditions (saliency better predicts gaze at night). We further demonstrate that predictive performance is improved when the head-camera video is transformed to retinal coordinates before feeding it to the saliency model.

**Authors:**

Richard E Veale: Kyoto University; Kenji Murase: Mazda Motor Corporation; Masayuki Watanabe: Mazda Motor Corporation; Tadashi Isa: Kyoto University

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