United States

Abstract
Gestures are crucial cues in emotion communication, yet little is known about how specific emotions elicited through different stimuli link to gesture production. The present study investigated how gesture frequency and type (representational vs. nonrepresentational) varied across specific emotion categories (i.e., happiness, anger, sadness, and neutral) elicited by visual stimuli and written narratives. In a within-subject design, participants (n=38) retold emotionally charged movie clips and written narratives for each emotion. The results showed that the participants overall produced fewer representational gestures while describing sadness compared to happiness and neutral, and anger compared to neutral. Interestingly, the participants overall produced more nonrepresentational gestures in narrative descriptions than in movie clip descriptions. However, gesture frequency and type did not significantly differ across movie clips and narratives. These results underscore the importance of accounting for both stimulus type and specific emotion categories when examining the role of gestures in emotion communication.

CogSci 2025

The Role of Gesture in Emotion Communication: Patterns Across Emotional Categories and Stimulus Types

embodied cognition

language production

psychology

emotion

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

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CogSci 2021

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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### 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. 
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* **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.
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* **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.
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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

In recent years, the remarkable performance of large language models (LLMs) in tasks such as legal judgment prediction (LJP) has garnered widespread attention. An increasing number of LLMs have been successfully implemented to assist judges in performing various legal tasks. However, their robustness and reliability in complex judicial scenarios remain a subject of debate, particularly when confronted with real-world legal cases. Existing research often overlooks the systematic evaluation of these LLMs in terms of judicial fairness, robustness and other ethical considerations. To fill this gap, we propose a novel benchmark that integrates authentic legal cases to evaluate the robustness of LLMs in the legal judgment prediction (LJP) task. Our work establishes foundational safety standards for applying LLMs in the legal domain.

JUDICIOUS: Evaluating Robustness of Large Language Models in the Legal Realm

We propose a method for measuring the consistency of ordinal annotations based on a pre-trained embedding vector space. Intuitively, our method finds a direction in the embedding space along which data points align as closely as possible to their annotated ranks. The proposed approach guarantees a globally optimal solution that is free from approximation errors. Thus, it yields a unique consistency measure given a dataset with human-provided ordinal annotations and a pre-trained embedding model. This feature facilitates a wide range of applications, including not only ordinal prediction but also the unsupervised detection of annotation errors within datasets, as well as consistency assessment of stage-based scales (e.g., whether the transitions “beginner to intermediate” and “intermediate to advanced” form linear progressions in the embedding space) during dataset construction. We evaluate our method using real-world datasets with ordinal annotations to demonstrate its effectiveness.

Measuring the Semantic Consistency of Ordinal Annotations via Text Embedding Spaces and Its Applications

The Flesch-Kincaid Grade Level (FKGL) is a widely used readability measure for English. However, the formula involves English-specific word counts, making it difficult to establish a comparable formula in other languages. This study provides a theoretical analysis of the FKGL formula, uncovering its fundamental principles. Unlike prior work, we demonstrate that the formula can be reinterpreted as representing the “average syllable count per sentence.” While vocabulary breadth expands with increasing grade levels, the range of syllables per word remains constant, regardless of age or grade. This invariance likely contributes to FKGL’s enduring applicability. We validate our framework using empirical evaluations with the British National Corpus (BNC), confirming the theoretical soundness of our approach. Furthermore, we extend the FKGL formula to other languages, proposing a multilingual readability metric. This research not only deepens our understanding of existing readability measures but also provides a robust framework for cross-linguistic readability comparisons.

Constructing Multilingual Readability Metrics for Cross-Language FKGL Comparisons

Emotional prosody---vocal cues that convey affect---profoundly influences how listeners interpret a speaker’s intentions. We conducted two studies comparing AI- and human-generated emotional speech. In Study 1 (N=38), participants categorized five emotions (happy, sad, angry, neutral, fear) expressed by human voices and by an advanced text-to-speech (TTS) system. Human recordings exhibited higher overall accuracy (79.82% vs. 72.65%) and were rated significantly more natural, an effect partially explained by micro-perturbations (e.g., jitter, shimmer) that enhanced perceived authenticity. In Study 2 (N=53), these validated stimuli were incorporated into short scenarios, with each speaker labeled as either ``human'' or ``AI.'' Even when participants heard identical clips, those informed that the speaker was human exhibited greater trust and empathy, resulting in higher donation and advice-following rates. Although contemporary TTS systems effectively convey broad affective states, explicit AI labeling reduces perceived credibility and social engagement, underscoring the critical role that preexisting expectations play in human--AI communication.

When Machines Speak with Feeling: Investigating Emotional Prosody, Authenticity, and Trust in AI vs. Human Voices

Manga has gained global popularity, yet how its visual elements, such as characters, text, and panel layouts, reflect the uniqueness of individual works remains underexplored. This study investigates the contribution of panel layouts to manga identity through both quantitative and qualitative analysis. We trained a deep learning model to classify manga titles based solely on facing page images, and performed ablation experiments by removing characters and text, retaining only panel frame structures. Using 10,122 images from 104 works in 12 genres in the Manga109 dataset, we demonstrate that panel layouts alone enable high-accuracy classification. Grad-CAM visualizations further reveal that the models focus on layout features such as size, spacing, and alignment. These findings suggest that panel layouts encode work-specific stylistic patterns and support visual narrative comprehension, highlighting their role as a key component of manga's visual identity.

How Panel Layouts Define Manga: Insights from Visual Ablation Experiments

This study explores the psychological impact of AI-generated and user-manipulated images on body image perception, particularly in the context of social media platforms. Focusing on young adults, the research examines their ability to identify A.I.-enhanced, user-enhanced, and unaltered images. Results indicate that participants can readily detect AI-enhanced images due to exaggerated features but struggle to identify subtle alterations from traditional photo-editing apps. Interestingly, participants showed a preference for minimally edited or unaltered images, despite faster detection of AI- enhanced images. Qualitative data suggest a divide in participants' attitudes toward AI manipulation: some expressed concern about its effects on body image and self-esteem, while others expressed indifference. These findings highlight the increasing difficulty in distinguishing authentic content from digital manipulation and raise important questions about rapidly evolving definitions of beauty and authenticity. Overall, findings underscore the need for media literacy interventions to address these challenges.

Perceptions of A.I.-Enhanced Bodies: Autonomy, Authenticity, and Preferences Among Young Adults

Social stimulus often triggers a stress response, influencing learning performance depending on the task’s nature. Previous research shows that social pressure before learning impairs performance in rule-based (RB) learning tasks, relying on hypothesis-testing mechanisms. Conversely, it may enhance performance in information-integration (II) learning tasks, which depend on procedural memory systems.
This study examined the effects of social feedback stimuli (e.g., smiling or angry faces), compared to non-social feedback stimuli (e.g., green circles or red crosses) on performance in RB and II tasks. Participants were assigned to either an RB or II task and received either social or non-social feedback during the task.
Results showed that social feedback significantly improved performance in the II task compared to non-social feedback, but did not enhance RB task performance. These results shed light on the distinct cognitive mechanism underlying categorical learning and emphasize the importance of feedback design in educational and training environments.

Impact of Social Feedback Stimulus on Information-Integration Category and Rule-based Category Learning

Prospect Theoretic Loss aversion (λ) suggests we overvalue losses compared to gains during decision making but the influence of age on loss aversion is unclear. We conducted a random-effects meta-analysis on 7 studies that measured loss aversion and the age of participants. The pooled estimate (β=0.0886, 95%CI [0.0087 0.1686] revealed an effect of age on loss aversion. Considering age as continuous (n=1103, 5 studies; β = -0.005233, t= -2.511, p = 0.0122) or categorical (n=1856, 8 studies; β=0.00856, t= 0.111, p = 0.912) reflected substantial differences. Dividing participants broadly (< 46 and > 45 years) to explore differences in estimates of loss aversion (λ>1, λ =1, λ<1), also found significant differences, X2 (2, 1856)=7.5362, p = 0.0231. These findings underscore the veracity of meta-analysis for advancing and explicating previously underexplored aspects of decision theory.

A Meta-analysis of Age-related effect on Loss Aversion

Automatic emotion recognition using electroencephalography (EEG) signals has garnered significant attention in recent years. While multi-source domain adaptation methods provide a promising framework for cross-subject emotion recognition, the distributional discrepancies among different source domains often result in negative transfer. To address these challenges, we propose a two-stage Dynamic Domain Selection and Pseudo-label Refinement (DDSPR) model. In the first stage, we introduce a novel Dynamic Domain Selection (DDS) module and an Agent Domain Adaptation Strategy (ADAS) to dynamically select and align source domains. In the second stage, a confidence-based pseudo-label correction strategy is employed to refine target domain labels and mitigate noise. We evaluate the proposed model through cross-subject experiments on the SEED and SEED-IV datasets, achieving accuracies of 91.50% ± 7.05 and 78.05% ± 13.56, respectively.The results demonstrate its effectiveness in emotion recognition performance.

DDSPR: Dynamic Domain Selection and Pseudo-label Refinement for Cross-Subject EEG-based Emotion Recognition

Communication is essential for enhancing the cognition and cooperation of agents in multi-agent reinforcement learning (MARL). However, existing methods often rely on predefined and rigid cognitive patterns, which cannot adapt to dynamic environmental changes and complex inter-agent interactions. In this work, we introduce the Hierarchical Cognitive Graph Autoencoder (HCGA), an adaptive framework that addresses these limitations. HCGA represents inter-agent messages as nodes in a graph with learnable edges, employs a grouping mechanism to integrate related local information into compact latent representations, and then applies hierarchical aggregation to construct a comprehensive global cognition. This approach effectively distills essential information and adaptively uncovers cognitive patterns from dynamic environments, thereby enhancing the overall robustness and efficiency of cognitive processing in MARL tasks. Experimental results demonstrate that HCGA significantly outperforms state-of-the-art methods across various MARL tasks, highlighting its robustness, adaptability, and efficiency.

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JUDICIOUS: Evaluating Robustness of Large Language Models in the Legal Realm

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