Netherlands

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

CogSci 2024

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

### Welcome!
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

You are required to register for this event. **Please register [here](https://2024.aclweb.org/registration). **

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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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To access the site please register [**here**](https://cognitivesciencesociety.org/registration/). 

The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

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

Improved classification accuracy in deep vision models does not come with better predictions of perceptual similarity

This paper explores the cognitive processes underlying how and why trust in informational sources fluctuates. If information from experts and mainstream media is broadly more accurate than peer networks’, why do we sometimes lose trust in experts? Counterintuitively, we often prefer information from authoritative sources, even if they become distrusted. We built a computational model of these dynamics. It includes a decision process sensitive to information processing costs and a learning process driven by prediction error minimization. We hypothesized that human information-processing biases could explain why experts are preferred as default sources of information and why their legitimacy is less resilient than peer networks’ when both provide inaccurate information. We ran simulations over a wide range of parameters and found that the processing advantages of following experts can be outweighed by overreacting to their mistakes. This effect is higher when the environment is unstable and the epistemic authorities are biased.

**Authors:**

Maurício de Jesus Dias Martins: University of Vienna; Stefano Palminteri: École normale supérieure

The rise and fall of social hierarchical systems: a cognitive and information theoretical model

Making a judgment of learning (JOL) during study can impact later test performance. In paired-associates learning, strongly related pairs benefit from JOLs while weakly related pairs do not. The cue-strengthening hypothesis supposes that JOLs strengthen cue-target associations, enhancing future performance on tests sensitive to those associations. The current experiment attempts to generalize this hypothesis, based on paired-associates learning, to encompass feedback-based category learning. We suppose that making JOLs during category learning strengthens participants’ representations of the boundary separating categories in perceptual space. Participants completed a category learning task, with some performing JOLs throughout learning. At test, participants categorized a mix of novel and previously studied stimuli of varying distances from the category boundary. We found evidence of reactivity for test stimuli in all levels of novelty and distance from boundary. We discuss potential implications and generalizations of the cue-strengthening hypothesis that can encompass these results as well as pre-existing results.

**Authors:**

Anthony Cruz: The University of Western Ontario; John Paul Minda: The University of Western Ontario

Was That My Cue? Reactivity to Category-Level Judgments of Learning

The proliferation of artificial intelligence (AI) agents has introduced a new dynamic into the human social environment. This study investigates prosocial behavior in a hybrid human-machine setting, particularly within a gaming environment. Many existing studies on prosocial behavior are conducted in economic game settings in which the agents’ intentions, and whether or not prosocial actions offer benefits, are explicit. This project explores prosocial interactions in spatial environments where the need for help by another agent might not be immediately obvious, and where cognitive processes such as attention, and decision-making processes about the cost of helping are thus likely to play a role. In a baseline study (N = 177), we investigated the likelihood of human agents reciprocating prosocial behavior initiated by an AI player. Results indicated that the low saliency of the AI player’s actions was a primary reason for non-reciprocation. A follow-up study (N = 164) tested whether increasing the salience of the AI’s actions would enhance human prosocial responses. We found support for our hypothesis from analysis of the time-series data and participants’ self-reported post-game questionnaires. This research contributes to the growing field of human-AI cooperation, outlining a vision for a future where technology actively contributes to our collective well-being, and opening up new possibilities for positive transformation in a world increasingly populated by intelligent machines.

**Authors:**

Xinyue Hu: University of California, Irvine; Kumar Akash: Honda Research Institute USA, Inc.; Shashank Mehrotra: Honda Research Institute USA, Inc.; Teruhisa Misu: Honda Research Institute USA, Inc.; Mark Steyvers: University of California, Irvine

Prosocial Acts Towards AI Shaped By Reciprocation And Awareness

Humans are remarkably adaptive instructors who can adjust advice on complex tasks based on a learner’s current knowledge and goals. However, paradigms in cognitive psychology generally examine pedagogy in constrained and discrete tasks, like categorization or feature learning. We examine teaching in continuous domains, where there are theoretically infinite hypotheses, and model how teachers can formulate a computationally tractable Bayesian inference using Gaussian process regression. Taking inspiration from function learning tasks, we investigated how one teaches visual underlying functions by giving pedagogically-informed point examples. Preliminary evidence suggests teachers are sensitive to learners’ priors about continuous functions. For instance, when learners expect a diverse range of function types (linear, quadratic, periodic, etc.) then teachers tend to select examples that help distinguish between those types. Conversely, teachers relaxed this constraint if learners had not seen multiple function types. Our results provide insight into mechanisms of pedagogical guidance in complex, continuous task domains.

**Authors:**

Maya Malaviya: Stevens Institute of Technology; Mark K Ho: Stevens Institute of Technology

Teaching Functions with Gaussian Process Regression

Languages differ in how they form questions that are equivalent to English questions such as "who does John think Maria loves?" in that the correct answer is who John thinks Maria loves, and not who Maria actually loves. Linguists disagree about how Polish makes such inquiries, and to date, no research has investigated how native Polish-speaking adults judge, process or produce these inquiries. In this paper, we investigated the nature of Polish questions via a corpus study, a grammaticality judgment study, and a spoken production study. Taken together, the results of these studies suggest that Polish has several syntactically distinct options for making these sorts of inquiries. Closer examination reveals that discourse context strongly affects which option is preferred. These findings highlight the importance of considering context and the pitfall of studying sentences presented in isolation when evaluating linguistic or psycholinguistic claims.

**Authors:**

Jakub Suchojad: Rutgers University; C. Jane Lutken: Rutgers University; Karin Stromswold: Rutgers University

Some Questions and Answers about Polish Questions

Assessing the levels of motivation in the learning process are pivotal in the daily life of college students, for the learning motivation profoundly impacts their overall academic performance. Yet, the prevailing methods to measure learning motivation levels still predominantly depend on expert evaluation and self-report, advancements in passive smartphone sensing have not been fully utilized in measuring motivation levels in learning process. In this study, we investigate and analyze behaviors and behavioral changes associated with their levels of learning motivation of N= 118 undergraduate college students integrating passive smartphone sensing with self-report survey. We collect a dataset regarding the students’ daily behaviors and self-report responses using a mobile application and questionnaire. Subsequently, we identify a variety of behaviors based on behavioral features captured from passive sensing data, followed by an exploration of the correlations between levels of learning motivation and the identified behaviors. Moreover, we analyze differences in behavioral changes among groups characterized by varying levels of learning motivation. Our study contributes to enhancing psychological assessment approaches by introducing a novel integrated method for more quantified and multidimensional measurement of learning motivation, providing valuable perspectives for assessing and intervening learning motivation in future research endeavors.

**Authors:**

Yu Feng: University of Minho; Tongyu Zhao: University of Minho; Yi Zhang: Jilin University; Yatong Zu: Jilin University; Adriano Tavares: University of Minho; Tiago Gomes: University of Minho; Hao Xu: Jilin University

Innovative Attempt at Enhancing Psychological Assessment: A Preliminary Investigative Study of Measuring College Students' Learning Motivation Levels through the Lens of Passive Sensing via Smartphones

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