Netherlands

As children age, the ability to make decisions about perceptual information improves in terms of both speed and accuracy. However, understanding the delicate changes within both the decision-making process and the ability to optimize the trade-off between speed and accuracy with age remains a challenge. This study employed the diffusion decision model to investigate age-related developments in perceptual decision-making. Additionally, the impact of practice and end-of-block feedback on achieving optimal decision-making was investigated. We gathered behavioral data from 299 children aged 6 to 12 and 50 adults while they performed a motion discrimination task. Adults and older children had narrower decision criteria, higher drift rates, and shorter non-decision times compared to younger children. Furthermore, individuals tended to approach the optimal policy as they aged, and for both children and adults, practicing and receiving detailed feedback could speed up the attainment of the optimal policy.

**Authors:**

Zeinab Majidifard: Shahid Beheshti University; Jamal Amani Rad: Shahid Beheshti University

CogSci 2024

Tuning the speed-accuracy trade-off in optimal decision policies during development

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

Human decision-making is filled with a variety of paradoxes demonstrating deviations from rationality principles. Do state-of-the-art artificial intelligence (AI) models also manifest these paradoxes when making decisions? As a case study, in this work we investigate whether GPT-4, a recently released state-of-the-art language model, would show two well-known paradoxes in human decision-making: the Allais paradox and the Ellsberg paradox. We demonstrate that GPT-4 succeeds in the two variants of the Allais paradox (the common-consequence effect and the common-ratio effect) but fails in the case of the Ellsberg paradox. We also show that providing GPT-4 with high-level normative principles allows it to succeed in the Ellsberg paradox, thus elevating GPT-4’s decision-making rationality. We discuss the implications of our work for AI rationality enhancement and AI-assisted decision-making.

**Authors:**

Ardavan S. Nobandegani: McGill University; Irina Rish: Mila - Quebec AI Institute; Thomas Shultz: McGill University

Decision-Making Paradoxes in Humans vs Machines: The case of the Allais and Ellsberg Paradoxes

This study examines the alignment of deep learning model representations with those of humans, focusing on the typicality effect, where certain instances of a category are considered more representative than others. Previous research, limited to single modalities and few concepts, showed modest correlations with human typicality judgments. This study expands the scope by evaluating a wider array of language (N=8) and vision (N=10) models. It also considers the combined predictions of language+vision model pairs, alongside a multimodal CLIP-based model. The investigation encompasses a larger concept range (N=27) than prior work. Our findings reveal that language models align more closely with human typicality judgments than vision models. Additionally, combined language+vision models, as well as the multimodal CLIP model, demonstrate improved prediction of human typicality data. This study advances the understanding of ML models' conceptual alignment with human cognition and contributes a new image set for vision model concept evaluation.

**Authors:**

Siddhartha Vemuri: Georgia Institute of Technology; Raj Sanjay Shah: Georgia Institute of Technology; Sashank Varma: Georgia Tech

How Well Do Deep Learning Models Capture Human Concepts? The Case of the Typicality Effect

The paper investigates the development of conversational skills in preschool children, focusing on their ability to adopt flexible roles in dialogues. We specifically analyze children's coordinated behavior in question-response-follow-up sequences, both as Initiators and Responders, using a longitudinal French corpus of child-caregiver spontaneous interactions. While preschool children showed growing sophistication in their ability to initiate and respond appropriately within conversations, they still had qualitative differences with adults, especially as initiators, suggesting further development beyond preschool. The findings contribute to our understanding of how conversational skills develop in early childhood and the role these skills play in broader cognitive and social development.

**Authors:**

Morgane Peirolo: Aix-Marseille University; Zihan Xu: Aix-Marseille University; Abdellah Fourtassi: Aix-Marseille University

Development of Flexible Role-Taking in Conversations Across Preschool

As children enter elementary school, their understanding of the ordinal structure of numbers transitions from a memorized count list of the first 50-100 numbers to knowing the successor function and understanding the countably infinite. We investigate this developmental change in two neural network models that learn the successor function on the pairs (N, N+1) for N in (0, 98). The first uses a one-hot encoding of the input and output values and corresponds to children memorizing a count list, while the second model uses a place-value encoding and corresponds to children learning the language rules for naming numbers. The place-value model showed a predicted drop in representational similarity across tens boundaries. Analysis of the latent representation shows that counting across a tens boundary can be understood as a vector operation in 2D space, where the numbers with the same tens place are organized in a linearly separable manner, whereas those with the same ones place are grouped together. A curriculum learning simulation shows that, in the expanding numerical environment of the developing child, representations of smaller numbers continue to be sharpened even as larger numbers begin to be learned. These models set the stage for future work using recurrent architectures to move beyond learning the successor function to simulating the counting process more generally, and point towards a deeper understanding of what it means to understand the countably infinite.

**Authors:**

Vima Gupta: Georgia Institute of Technology; Sashank Varma: Georgia Tech

Understanding Infinity: Neural Network Models of Becoming a “Cardinal Principle Knower”

Wait before you leap: Influence of instruction sequence on Visual search task

Perceived dominance and trustworthiness have both been found to be positive predictors for a candidate’s electoral and employment success. On the other hand, compared to male faces, female faces exhibit a much stronger anti-correlation between perceived trustworthiness and dominance. Together, these two phenomena place women at a distinctive disadvantage to men in electoral and work settings. In this study, we conduct computational analyses on a gender- and race-balanced, publicly available face dataset to examine the provenance of the anti-correlation between perceived dominance and trustworthiness in female faces. By identifying and quantifying the facial features that contribute to each social trait, we find that the female anti-correlation stems predominantly from components unique to female faces (83\%), with the lip region being the main contributor (23\%). Visualization of face featural modifications show that the corners of the mouth curve up and down in opposite directions for perceived trustworthiness and dominance, respectively, in female faces, but in orthogonal directions the same in male faces. By correlating gender specific models with perceived demographic information, we find that female dominance ($F_D$) and trustworthiness ($F_T$) are correlated in opposite directions along most perceived gender, age and race-related demographic dimensions. Male dominance ($M_D$) and trustworthiness ($M_T$) , on the other hand, are correlated in the same direction along race-related dimensions, but otherwise share no significant demographic dimensions (age and gender). In particular, perceived sexual dimorphism strongly drives $F_D$, $F_T$, and $M_D$, but is absent for $M_T$, indicating sexual dimorphism is a strong contributor to the female anti-correlation.

**Authors:**

Christine H Lind: University of California San Diego; Angela J Yu: UC San Diego

A computational analysis of gender differences in face-based perception of trustworthiness and dominance

Code tracing involves simulating at a high level the steps a computer takes when it executes a computer program. This is a fundamental skill needed for programming activities, but one that novices find challenging. Thus, work is needed on how to support novice programmers in this activity. We conducted an experimental study with university students (N = 56) learning to code trace in two conditions, namely peer tutoring and solo code tracing. Our primary outcome variable was learning. However, since how students feel is also an important factor in educational settings, we also measured student emotion. Contrary to prior work in other domains, there was no significant benefit of peer tutoring and self-reported levels of emotion were similar in the two conditions; Bayesian statistics provided evidence for the null model in the majority of cases.

**Authors:**

Ronessa Dass: Carleton University; Kaleigh St. Jacques: CArleton University; Kasia Muldner: Carleton University

Peer tutoring vs. solo activities: Effects on learning and emotion

Numerous studies have shown that number word learning is a protracted process. One challenge facing children learning the meaning of number word such as “one”, “two”, or “three” is that number words refer to a property of a set and not to individual objects. In this study, we focused on a sample of children who have not learned the meaning of small number words such as “two” and “three” and tested whether children could learn number words from examples of sets that help them focus on set size. Specifically, the experimental training condition included examples that highlight a common relational structure between sets through varying object properties in the sets (e.g., three yellow stars and three red hearts are both “three”), whereas the control condition did not vary object properties(e.g., two sets of three yellow stars with different spatial arrangement). We trained two- and three-knowers (N = 65) on the next number (i.e., three or four) and assessed their learning with a Two-Alternative-Forced-Choice task and Give-a-Number task. Overall, we found weak effects of training. We discuss our findings in the broader literature on number word learning and explore the possibility of analogical reasoning as a mechanism of number word learning.

**Authors:**

Theresa Elise Wege: Loughborough University; Rebecca Merkley: Carleton University; Sara Jasim: York University; Daniel Ansari: Western University; Pierina Cheung: Nanyang Technological University

“Three yellow stars and three red hearts: Can subset-knowers learn number word meanings from multiple examplars?

Symmetry, a fundamental concept in perception and language, poses an interpretative challenge due to the disparity between its formal definition and linguistic expression. Formal symmetry is often distorted when expressed linguistically, such that e.g., 'North Korea is similar to Red China' is interpreted differently from 'Red China is similar to North Korea' despite their logical equivalence (Tversky, 1977). Gleitman et al. (1996) found this interpretive asymmetry stems from the syntactic positions of arguments, such that symmetry is restored when both arguments are on equal syntactic footing (e.g., a Conjoined NP Intransitive, “North Korea and China are similar”). Here a novel eye-tracking method tested how syntax and lexical semantics contribute to symmetrical interpretations. Participants were asked to rapidly sort spoken utterances by clicking on visible folders marked with a symmetrical or asymmetrical icon. Commitments to symmetry based on syntactic evidence emerged rapidly as the sentence unfolded over time.

**Authors:**

Ugurcan Vurgun: University of Pennsylvania; Heesu Yun: University of Pennsylvania; Barbara Landau: Johns Hopkins University; John Trueswell: University of Pennsylvania

Real-time processing of symmetrical predicates: Semantic categorization over time

When reading temporarily ambiguous garden-path sentences, misinterpretations sometimes linger past the point of disambiguation. This phenomenon has traditionally been studied in psycholinguistic experiments using online measures such as reading times and offline measures such as comprehension questions. Here, we investigate the processing of garden-path sentences and the fate of lingering misinterpretations using four large language models (LLMs): GPT-2, LLaMA-2, Flan-T5, and RoBERTa. The overall goal is to evaluate whether humans and LLMs are aligned in their processing of garden-path sentences and in the lingering misinterpretations past the point of disambiguation, especially when extra-syntactic information (e.g., a comma delimiting a clause boundary) is present to guide processing. We address this goal using 24 garden-path sentences that have optional transitive and reflexive verbs leading to temporary ambiguities. For each sentence, there are a pair of comprehension questions corresponding to the misinterpretation and the correct interpretation. In three experiments, we (1) measure the dynamic semantic interpretations of LLMs using the question-answering task; (2) track whether these models shift their implicit parse tree at the point of disambiguation (or by the end of the sentence); and (3) visualize the model components that attend to disambiguating information when processing the question probes. These experiments show promising alignment between humans and LLMs in the processing of garden-path sentences, especially when extra-syntactic information is available to guide processing.

**Authors:**

Andrew Li: Georgia Institute of Technology; Xianle Feng: Georgia Institute of Technology; Siddhant Narang: Georgia Institute of Technology; Austin Peng: Georgia Institute of Technology; Tianle Cai: Georgia Institute of Technology; Raj Sanjay Shah: Georgia Institute of Technology; Sashank Varma: Georgia Tech

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