Netherlands

Spatial orientation and spatial navigation are important abilities. However, large individual differences are common in these spatial abilities, yet the field lacks satisfying explanations about the origin of such differences. 

In this work, we measured the eye-tracking data of 26 participants who freely explored a large city (244 buildings) in virtual reality for 150 min. After the exploration, participants performed a pointing-to-building task in the same city. For the analysis, we transform the eye-tracking data into gaze-graphs and calculate graph-theoretical measures. We then model participants’ mean task performance with a linear model using global gaze-graph measures (R²=0.41). Moreover, a linear model with graph diameter only, results in an R² of 0.4; thus, graph diameter can explain 40% of the variance in mean task performance of participants. 

Overall, our results show visual behavior, specifically gaze-graph diameter, to be a strong predictor of individual differences in spatial navigation performance.

**Authors:**

Jasmin L. Walter: University of Osnabrück; Vincent Schmidt: University of Osnabrück; Sabine U. König: University of Osnabrück; Peter König: University of Osnabrück

CogSci 2024

Visual behavior during spatial exploration explains individual differences in performance of spatial navigation tasks

### 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. 
<br><br>
* **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.
<br><br>
* **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.
<br><br>

#### 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.

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**<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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**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**<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.
<br><br>

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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.

We developed three cognitive models using ACT-R’s learning process for abacus gestures, which is a way to represent numbers 0-99 with a set of human fingers. We compared the models’ predictions with existing human data. The results suggest the developed models can learn abacus gestures depending on the structure of the declarative memory. The early results indicate that defining many chunks (N=100) for a model cannot simulate the learning process and, therefore, does not correlate with human data. To tackle this problem, we modified the structure of chunks to have fewer chunks to show the learning procedure more accurately and realistically. Therefore, fewer chunks with overlaps are defined for numbers to align with reported participant interviews. Learning curves from the models with fewer chunks are closely correlated with human data across five task blocks. These models with different declarative knowledge representations and configurations contribute to the procedures of abacus learning.

**Authors:**

Lingyun He: The Pennsylvania State University; Duk Hee Ka: The Pennsylvania State University; Md Ehtesham-Ul-Haque: Pennsylvania State University; Syed Masum Billah: Pennsylvania State University; Farnaz Tehranchi: The Pennsylvania State University

Cognitive Models for Abacus Gesture Learning

An important aspect of mathematical and computational thinking is algorithmic thinking––the analysis of systems, algorithms, and natural processes. A critical aspect of algorithmic thinking is estimating the growth of functions with increasing input size. In this study, we asked 178 participants to estimate values of seven common functions in algorithmic analysis and other fields, log(n), sqrt(n), nlog(n), n^2, n^3, 2^n, n!, to understand this intuitive ability. Estimates were fit against the actual values for all functions. Participants showed linearization bias: sublinear functions were best fit by a linear function, and superlinear functions were best fit by a cubic (i.e., polynomial) function, even those that grow much faster (e.g., $n!$). Participants estimated logarithmic functions least accurately. These results provide an understanding of intuitions about the growth of functions and set the stage for future studies on how to best improve people’s reasoning about functions more generally.

**Authors:**

Vijay Marupudi: Georgia Tech; Jeffrey K. Bye: University of Minnesota; Sashank Varma: Georgia Tech

Estimating the growth of functions

Orientation competence, the ability to determine one's location and heading direction, stands as one of the most fundamental skills. Maps are important for navigators providing the relevant information for spatial orientation. Researchers have thus investigated navigational map reading competence, wayfinding strategies, and performance in many experiments facing similar challenges to assess navigation behaviour and performance of participants in real and virtual environments in a systematic manner.
This paper introduces GeoGami, a free and open-source research software tailored for training and evaluating map-reading competence in navigational studies. Research identified a set of sub-competencies relevant for navigational map-reading. Our research software supports the assessment and training of these sub-competencies through tasks that are tailored to a specific sub-competency. In this paper, we explain the unique design of the GeoGami research software, which supports diverse setups of navigational experiments and map-reading trainings while systematically assessing performance. The key contribution of this paper lies in demonstrating how theoretically defined navigational map reading competencies can be implemented in a GPS enabled software and how a systematically designed research software can effectively harness the diverse capabilities of digital maps and location-based systems for research and training purposes.

**Authors:**

Angela Schwering: University of Muenster; Mitko Aleksandrov: The Institute for Geoinformatics (ifgi); Yousef Qamaz: Institute for Geoinformatics

GeoGami: A Research Software for Training and Measuring Navigational Map Reading Competence

This study explores the relationship between predictability, as measured by surprisal, and processing difficulty in code comprehension. We investigate whether similar mechanisms govern the processing of programming and natural languages. Previous research suggests that programmers prefer and produce more predictable code, akin to natural language patterns. We utilize eye-tracking data from the Eye Movements in Programming (EMIP) dataset to examine the impact of surprisal on various eye movement measures. Contrary to expectations based on natural language processing, our results reveal that surprisal does not significantly influence fixation metrics. Additionally, regressions in code reading show an unexpected inverse relationship with surprisal, suggesting that readers have different reasons for making regressions while reading code versus natural text. These findings contribute insights into the unique dynamics of code comprehension and opens avenues for further research in this domain.

**Authors:**

Nicole Dodd: University of California, Davis; Skyler Jove Reese: University of California, Davis; Emily Morgan: UC Davis

Investigating the Relationship Between Surprisal and Processing in Programming Languages

Textual data as can be retrieved from the Web is mostly the outcome of human minds. Simple patterns are over-represented, e.g. in passwords (abcdef is more frequent than arfbxg) or among explicit numbers (1000 is more frequent than 1282). Our aim here is to suggest that pattern frequencies match some predictions of cognitive models based on simplicity. Conversely, the observation of actual frequencies offers an opportunity to infer particular cognitive mechanisms involved in the generation of words or numbers.

**Authors:**

Jean-Louis Dessalles: Institut Polytechnique de Paris; Giovanni Sileno: University of Amsterdam

Observing Simplicity Bias in Human-generated data

Decision diffusion models are commonly used to explain the processes underlying decision-making. Many software options exist for cognitive scientists to fit diffusion models to data; however, they tend to lack customizability beyond existing model formulations that are already built into them, stymying new theoretical contributions. We introduce FlexDDM, a new Python package that requires minimal coding to develop new diffusion models. The package is equipped with four standard models of cognitive conflict tasks and a suite of fitting techniques. Our development of FlexDDM aims to broaden the accessibility and applicability of computational methods in cognitive science, thereby accelerating theoretical innovation and contributing to advancements in the field of behavioral sciences.

**Authors:**

Kyle LaFollette: Case Western Reserve University; Joy Fan: Case Western Reserve University; Alessandra Puccio: Case Western Reserve University; Heath A. Demaree: Case Western Reserve University

FlexDDM: A flexible decision-diffusion Python package for the behavioral sciences

Acute stress, a circumstance where a situation's demand's exceed an organism's regulatory capacity, triggers a cascade of physiological and psychological changes, including heightened perspiration, cortisol levels, and anxiety. Existing research has focused on acute stress' effect on cognition in basic tasks of executive functioning, but its effect on computationally hard tasks is not well understood. Here, in a within-participants laboratory experiment we test for an effect of acute stress on decision-making in the 0-1 Knapsack Decision Problem (KP), a canonical computationally hard task. We find that acute stress, induced via the Trier Social Stress Test (TSST), leads to impaired decision-making performance in the KP. Particularly among cortisol responders, higher stress levels were associated with lower accuracy and higher time on task. Our findings bridge the gap between research on basic cognitive tasks and real-world decisions, building a more nuanced understanding of how acute stress affects complex decision-making.

**Authors:**

Karlo Doroc: University of Melbourne

Acute stress impairs performance in a computationally hard cognitive task

We introduce CogME, a cognition-inspired, multi-dimensional evaluation metric designed for AI models focusing on story understanding. CogME is a framework grounded in human thinking strategies and story elements that involve story understanding. With a specific breakdown of the questions, this approach provides a nuanced assessment revealing not only AI models' particular strengths and weaknesses but also the characteristics of the benchmark dataset. Our case study with the DramaQA dataset demonstrates a refined analysis of the model and the benchmark dataset. We argue the need for metrics based on understanding the nature of tasks and designed to align closely with human cognitive processes. This approach provides insights beyond traditional overall scores and paves the way for more sophisticated AI development targeting higher cognitive functions.

**Authors:**

Minjung Shin: Seoul National University; Seongho Choi: Seoul National University; Yu-Jung Heo: KT; Minsu Lee: Seoul National University; Byoung-Tak Zhang: Seoul National University; Jeh-Kwang Ryu: Dongguk University

CogME: A Cognition-Inspired Multi-Dimensional Evaluation Metric for Story Understanding

We introduce the CAUS (Curious About Uncertain Scene) dataset, designed to enable Large Language Models, specifically GPT-4, to emulate human cognitive processes for resolving uncertainties. Leveraging this dataset, we investigate the potential of LLMs to engage in questioning effectively. Our approach involves providing scene descriptions embedded with uncertainties to stimulate the generation of reasoning and queries. The queries are then classified according to multi-dimensional criteria. All procedures are facilitated by a collaborative system involving both LLMs and human researchers. Our results demonstrate that GPT-4 can effectively generate pertinent questions and grasp their nuances, particularly when given appropriate context and instructions. The study suggests that incorporating human-like questioning into AI models improves their ability to manage uncertainties, paving the way for future advancements in Artificial Intelligence (AI).

**Authors:**

Minjung Shin: Seoul National University; Donghyun Kim: Dongguk university; Jeh-Kwang Ryu: Dongguk University

CAUS: A Dataset for Question Generation based on Human Cognition Leveraging Large Language Models

Over the last years, advancements in deep learning models for computer vision have led to a dramatic improvement in their image classification accuracy. However, models with a higher accuracy in the task they were trained on do not necessarily develop better image representations that allow them to also perform better in other tasks they were not trained on. In order to investigate the representation learning capabilities of prominent high-performing computer vision models, we investigated how well they capture various indices of perceptual similarity from large-scale behavioral datasets. We find that higher image classification accuracy rates are not associated with a better performance on these datasets, and in fact we observe no improvement in performance since GoogLeNet (released 2015) and VGG-M (released 2014). We speculate that more accurate classification may result from hyper-engineering towards very fine-grained distinctions between highly similar classes, which does not incentivize the models to capture overall perceptual similarities.

**Authors:**

Fritz Günther: Humboldt-Universität zu Berlin; Marco Marelli: University of Milano-Bicocca; Marco Petilli: University of Milano-Bicocca

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