United States

Temporal-spatial metaphors can be differed across languages, and such cross-linguistic influences may affect people’s spatial conceptualization of time. Mandarin (including gestures) has different spatial metaphors for time than Chinese Sign Language (CSL). This paper investigated whether native Mandarin speakers’ mental space-time mappings change after learning CSL for 14 weeks. Sixty native Mandarin speakers who had no prior knowledge of sign language took a pretest and posttest of space-time mappings before and after taking a CSL course. The results showed that participants changed their temporal-spatial mappings after learning CSL. Specifically, they had more sagittal space-time mappings and fewer lateral ones than before. They also had more “future-in-front/ past-in-back” mappings consistent with CSL space-time mappings. Furthermore, these changes were more significant in high-proficiency learners than in low-proficiency learners. Our results not only demonstrate an effect of bodily experience on time conceptions, but also suggest that sign language can impact spatial temporal reasoning.

CogSci 2025

The effect of learning Chinese Sign Language on spatial conceptualisation of time in hearing Mandarin speakers

cognition of time

language and thought

quantitative behavior

learning

linguistics

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.
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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. 





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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.

Gaze Interaction Behavior Recognition (GIBR) plays a significant role in understanding social behaviors and diagnosing mental health conditions. However, existing methods are limited by inadequate task modeling, resulting in suboptimal performance. To address this issue, we model the GIBR task as a spatiotemporal reasoning problem integrating three modalities: emotion, gaze, and position. Based on this, we propose GIBRNet, which enhances the representation of gaze interaction tendencies through an Emotion-Aware Refinement Matrix and dynamically aggregates multi-frame, multi-modal information using GP-GNN, enabling more precise interaction behavior reasoning. Comparative experiments on the VACATION dataset demonstrate that GIBRNet significantly outperforms existing approaches. Additionally, we constructed a GIBR dataset suite, consisting of three extended datasets, for generalization evaluation, demonstrating GIBRNet's superiority. All datasets and code are publicly available.

GIBRNet: A Multimodal Spatiotemporal Reasoning Network Integrating Emotion, Gaze, and Position for Gaze Interaction Behavior Recognition

Understanding how psychological constructs are formalized into measurable representations remains a central challenge in cognitive science. This study examines large language models (LLMs) as cognitive artifacts capable of decomposing and formalizing such constructs. We evaluate LLMs’ conceptual alignment with human cognition across three tasks: predicting psychological attributes from text, generating theoretical construct explanations, and creating psychometrically valid assessment items. Results show strong representational fidelity for personality traits (average r=0.582), moderate alignment for depression (r=0.515), and emerging capabilities for anxiety (r =0.259). Expert evaluations confirm that LLMs produce theoretically coherent construct explanations (mean rating=4.33/5), while generated questionnaires exhibit convergent validity with established measures. Our findings position LLMs as effective tools for exploring cognitive representation systems and suggest that theory-driven prompting enables reliable, automated questionnaire development. This work bridges AI formalization techniques with core questions in cognitive science, offering a scalable framework for studying and constructing psychological measurement tools.

Cognitive Representation in Large Language Models: Formalizing Psychological Constructs for Automated Questionnaire Generation

Large language models (LLMs) excel at personality prediction but are often impractical for deployment due to high computational demands. This work introduces cognitive distillation with Chain-of-Thought calibration, a novel framework for transferring structured reasoning from large LLMs to smaller, efficient models. Inspired by cognitive architectures like ACT-R, our method aligns intermediate inference steps using exemplars from single or multiple LLMs. A 1.5B-parameter model distilled through this process surpasses Qwen1.5-110B in predictive accuracy, achieving a 28% improvement in Pearson correlation while using just 1.36% of its parameters. Ablation studies reveal that moderate sampling diversity and multi-model ensembles enhance cognitive skill transfer and construct validity. These findings demonstrate that high-level reasoning can be efficiently and faithfully transferred, enabling psychometrically robust personality assessment in resource-constrained settings. This approach bridges AI and cognitive science, offering a scalable path toward plausible, cognitively grounded language models.

Cognitive Distillation with Parameter-Efficient LLMs: Chain-of-Thought Calibration for Personality Prediction

As Large Language Models (LLMs) become more prevalent, concerns about their safety, ethics, and potential biases have risen. Systematically evaluating LLMs' risk decision-making tendencies, particularly in the ethical domain, has become crucial. This study innovatively applies the Domain-Specific Risk-Taking (DOSPERT) scale from cognitive science to LLMs and proposes a novel Ethical Decision-Making Risk Attitude Scale (EDRAS) to assess LLMs' ethical risk attitudes in depth. We further propose a novel approach integrating risk scales and role-playing to quantitatively evaluate systematic biases in LLMs. Through systematic evaluation of multiple mainstream LLMs, we assessed the "risk personalities" of LLMs across multiple domains, with a particular focus on the ethical domain, and revealed and quantified LLMs' systematic biases towards different groups. This helps understand LLMs' risk decision-making and ensure their safe and reliable application. Our approach provides a tool for identifying biases, contributing to fairer and more trustworthy AI systems.

Quantifying Risk Propensities of Large Language Models: Ethical Focus and Bias Detection through Role-Play

The emergence of large language model (LLM) has promoted the research progress in many fields, but it still faces challenges in imitating human logical reasoning, especially in the step-by-step reasoning of complex tasks and zero-shot logical cognition. To address these challenges, we propose a multi-step symbolic reasoning strategy that decomposes complex tasks into subtasks and optimizes the decomposition using a subtask verification module. Moreover, we also introduce a new zero-shot symbolic module which can help improve the model’s reasoning ability on unseen samples with symbolic representation and logical schemes. We evaluated our method on four reasoning datasets: the industrial private dataset Ship Assembly Technology and the public datasets ProntoQA, ProofWriter, and OpenBookQA. Our framework demonstrates substantial improvements in reasoning interpretability and generalization capacity compared to existing prompting paradigms. The proposed method establishes a new pathway for enhancing LLMs' cognitive architectures through symbolic system integration, showing strong potential for efficient knowledge transfer to downstream applications while preserving human-understandable reasoning traces.

Improving Cognitive Capability of Large Language Model: A Multi-Step Symbolic Reasoning Approach

In recent times, emotion recognition based on electroencephalograms (EEGs) has found extensive applications. Although numerous approaches leveraging CNN and Transformer have been put forward for automatic emotion recognition and have achieved commendable performance, several challenges remain: (1) Transformer-based models are proficient at capturing long-term dependencies within EEG signals. However, their quadratic computational complexity poses a significant hurdle. (2) Models that combine Transformers with convolutional neural networks (CNNs) often fail to effectively capture the coarse-to-fine temporal dynamics of EEG signals.
State Space Models (SSMs), exemplified by Mamba, have emerged as a promising solution. They not only showcase outstanding capabilities in modeling long-range interactions but also maintain a linear computational complexity, which is highly advantageous. To address these challenges head-on, we introduce Emotion-Mamba, an innovative framework designed specifically for EEG-based emotion recognition. The proposed framework initiates the process by employing the CNN Encoder to extract information from both the temporal and spatial dimensions of EEG signals. Subsequently, the extracted feature information is relayed to the Hierarchical Coarse-to-Fine Bi-Mamba (HBM) block, which is adept at efficiently processing these features. Furthermore, a Dense Temporal Fusion (DTF) module has been incorporated. This module capitalizes on the multi-level, purified temporal information sourced from CNN Encoder and HBM blocks, with the aim of bolstering decoding accuracy. We conduct comprehensive evaluations of Emotion-Mamba using the SEED and SEED-V datasets. The experimental findings unequivocally demonstrate that our proposed approach surpasses the existing state-of-the-art methods.

A Dense Convolutional Bi-Mamba Framework for EEG-Based Emotion Recognition

Assessing learning motivation and strategy (LMS) in specific situations can more accurately reflect students' self-regulation learning ability. However, traditional assessment methods, such as subjective evaluations and self-reports, are time-consuming, burdensome, and not well-suited to the dynamic nature of situational assessments. To address this, we presented the LLM-based agents, which enable intelligent generation of situational tasks and interactive assessment. Specifically, Master defines the theme and storylines, Designers generate situational tasks, Evaluator reviews the content quality, and Interactor controls the interactive assessment with users. The results of a user study with 97 university students demonstrated the reliability and validity of our approach and the significant enhancement of the user experience. The results further clarify the relationship among indicators of LMS. This study provides a novel paradigm and solution for situational assessment of LMS and offers valuable theoretical insights for intervention research targeting related indicators.

Cognitive Measurement with Generative AI: A Novel Interactive Situational Assessment of Learning Motivation and Strategy Using LLM Multi-Agents

Mobile phones provide an opportunity for a symbiotic interaction between humans and machines, which allows phones to collect human-centric data at anytime and anywhere. However, low-quality answers, which refer to the wrong answers, may be provided by users when they are asked excessive questions or in unsuitable contexts (e.g., driving). To solve this problem, we aim to design a methodology to collect more correct answers. We propose to use answer reaction time to annotate answer quality, to find a suitable number of daily questions, and the context factors that need to be considered according to their history records. We validated our methodology via the public dataset, which was collected by an extensive four-week in-the-wild study at the University of Trento, Italy. The results reveal that the context information and the number of daily questions are factors that can impact user answer behavior. These factors, therefore, influence the answer quality.

Improving Human Answers Quality by Machine Questions Number and Context Factors

Face recognition is shown to involve a prolonged developmental process where one gradually develops a more consistent but idiosyncratic visual routine with increasing age. In contrast to this developmental trend, here we found that aging was associated with decreased eye movement consistency during face recognition, which in turn contributed to declined face recognition performance. Although aging was not associated with changes in eye movement pattern, a less eyes-focused eye movement pattern predicted poorer face recognition performance together with lower eye movement consistency and age in older adults, suggesting that the idiosyncratic eye movement patterns acquired during early adulthood continue to account for their face recognition performance. Their decreased eye movement consistency was associated with declines in selective attention and inhibition control, suggesting difficulties in the execution of the learned visual routines. These findings have important implications for ways to facilitate older adults’ face recognition to promote healthy aging.

The Role of Eye Movement Consistency in Aging-Related Decline in Face Recognition

Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, their ability to detect and react to misinformation remains an open question, particularly in comparison to human cognitive mechanisms. This study investigates how LLMs and humans react to misinformation by analyzing their performance across five categories of errors: intellectual, common sense, reasoning, misleading, and logical errors. We construct the ErrorQuestionDataset, comprising 346 misinformation-related questions, and conduct an empirical study involving five state-of-the-art LLMs (ChatGPT-4o, Gemini-1.5flash, DeepSeek-v3, Hunyuan-Large, GLM-v4Flash) and 251 human participants. Our findings reveal distinct response patterns: while LLMs rely on statistical correlations and pattern recognition, humans leverage contextual reasoning and domain-specific knowledge. The results indicate that LLMs generally achieve higher accuracy than humans in error detection tasks, but their performances lack depth in reasoning-based assessments. Additionally, we identify five primary performance types—affirmation, negation, hesitation, questioning, and off-topic reactions—providing insights into the  cognitive differences between LLMs and human cognition. Our study contributes to the broader understanding of misinformation detection and offers implications for enhancing the robustness and reliability of LLMs in real-world applications. Our code and dataset are available at https://github.com/anonymous-submission8888.

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GIBRNet: A Multimodal Spatiotemporal Reasoning Network Integrating Emotion, Gaze, and Position for Gaze Interaction Behavior Recognition

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