United States

The detrimental impact of the long-overdue pandemic has been widely acknowledged. However, the varied responses to its unprecedented challenges, particularly among young adults in college, are not well known. This study explores the role of psychological immunity in COVID-related trauma among Indian students. A significant proportion of students reported psychological distress, depression, and stress during the second wave between May and June 2021, with COVID-19 infection and related worries correlating with poorer mental health. Mediation analysis indicated that psychological flexibility negatively predicted distress, depression, and stress, while avoidance coping strategies showed a positive association with these outcomes. These findings suggest that psychological flexibility serves as a protective buffer against the impact of the pandemic, fostering resilience, while avoidant coping exacerbates its adverse effects. Interventions like Acceptance and Commitment Therapy (ACT) may enhance psychological flexibility and mitigate maladaptive coping, improving student mental well-being.

CogSci 2025

Psychological Flexibility and Coping Strategies Influence Well-Being: The Mediating Role of COVID-19 Related Stressors During the Second Wave Among Indian Students

emotion disorder

behavioral science

mood

psychology

survey

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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<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
<br>[**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)
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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. 





poster

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

<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/amvr/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>

#### 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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#### 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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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

The rapid development of large language models (LLMs) has spurred their applications to tabular data. 
Prior research has investigated prompt engineering and external tool integration (e.g., code interpreters) to enhance LLMs' table comprehension, yet existing approaches struggle to generalize across diverse table-based reasoning tasks due to task-specific fixed workflows. Additionally, LLMs' inherent limitations, including hallucination and unreliable reasoning, necessitate iterative refinement mechanisms for robust performance.
Inspired by cognitive-inspired problem-solving strategies, where iterative reflection and tool augmentation improve decision-making, we propose TableCritic, a functionally grounded computational framework for adaptive table reasoning. Following the functional-structural model taxonomy, our framework operates at the algorithmic level, implementing a dynamic feedback loop: (1) LLM-driven self-critique evaluates intermediate outputs and (2) tool-guided error correction. This task-agnostic architecture achieves performance transparency through modular tool composition while avoiding neurobiological implementation constraints.
Experiments show that TableCritic significantly improves accuracy and outperforms baselines across various table-based tasks.

TableCritic: Refine Table Reasoning via Self-Criticism and Tool Library

Unfamiliar accents can pose a challenge to speech recognition. However, listeners often adapt quickly to novel accents, and even generalize this adaptation across talkers with the same accent. We investigate how such cross-talker generalization---critical to effective speech perception---is achieved. We take advantage of advances in automatic speech recognition to test whether comparatively simple similarity-based inferences can explain cross-talker generalization in human listeners. We use the latent perceptual space learned by the HuBERT model---shaped by the statistics of the speech signal and the objective to recognize speech---to meaningfully measure the similarity between talkers' pronunciation. We find that word-level similarity in this latent space predict listeners' ability to successfully generalize across talkers. We discuss consequences for theories of adaptive speech perception. In particular, our results explain why cross-talker variability is not a prerequisite for cross-talker generalization (contrary to influential accounts).

Latent speech representations learned through self-supervised learning predict listeners’ generalization of adaptation across talkers

An influential mathematical model of memory, the temporal
context model (TCM), posits that we encode items and their
associations with temporal context (Howard & Kahana, 2002).
Temporal context is conceived of as a recency-weighted av-
erage of past experiences. Critically, the model assumes that
when an item is retrieved later, the associated temporal con-
text is also obligatorily retrieved. Existing evidence for the
idea of retrieved temporal context primarily comes from free-
recall studies. However, free recall introduces some critical
confounds that are difficult to resolve (Folkerts et al., 2018)
and also encourages memory strategies that may mimic tem-
poral context effects (Hintzman, 2011). To address these con-
founds, we investigate temporal context using an image recog-
nition task. Schwartz et al. (2005) examined temporal con-
text in an image recognition task using a short-list-based ex-
perimental design, and found that temporal context influenced
recognition performance. Building on this, we use the Natural
Scenes Dataset (NSD) to show that reinstating temporal con-
text enhances recognition accuracy even across substantially
longer timescales. We demonstrate that images that were tem-
porally closer during encoding facilitated the recognition of
each other. Critically, we show that this influence falls off with
temporal distance at encoding only when the temporal context
is successfully retrieved, as predicted by TCM. Furthermore,
the slope of this temporal gradient increases as a function of
the strength of the influence of the retrieved temporal context.
These findings extend our understanding of temporal context
effects in episodic memory by showing that temporal context
is retrieved even in tasks that do not encourage linking between
items as a memory strategy.

Behavioral signatures of temporal context retrieval during continuous recognition

Research on implicit learning using the artificial grammar learning (AGL) paradigm has traditionally relied on tasks that promote active engagement, such as memorization, repetition, or rule discovery during the exposure phase. This study examined whether limited exposure, devoid of active engagement tasks, enables participants to distinguish between grammatical and ungrammatical sequences in both simple and complex artificial grammars. Participants performed above chance on the grammaticality task across both conditions but appeared to rely on explicit strategies to a greater degree than reported in previous AGL studies. These findings highlight the critical role of exposure conditions and suggest that exposure to letter strings without active engagement may not sufficiently restrict learning to implicit processes.

Implicit and Explicit Knowledge after Limited Exposure to Artificial Grammars of Various Complexity 

Inspired by human visual attention, deep neural networks have widely adopted attention mechanisms to learn locally discriminative attributes for challenging visual classification tasks. However, existing approaches primarily emphasize the representation of such features while neglecting precise localization, which often leads to misclassification caused by shortcut biases. This limitation becomes more pronounced when models are evaluated on transfer or out-of-distribution datasets. In contrast, humans leverage prior object knowledge to quickly localize and compare fine-grained attributes, a capability especially crucial in complex and high-variance classification scenarios. We introduce Gaze-CIFAR-10, a human gaze time-series dataset, along with a dual-sequence gaze encoder that models the precise sequential localization of human attention on distinct local attributes. In parallel, a Vision Transformer (ViT) is employed to learn the sequential representation of image content. Through cross-modal fusion, our framework integrates human gaze priors with machine-derived visual sequences, effectively correcting inaccurate localization in image feature representations.

Gaze-Guided Learning: Avoiding Shortcut Bias in Visual Classification

Personalized recommendation aims to recommend candidate items to users based on their preferences by simulating their cognitive decision-making process. User-item interaction data typically follows a power-law distribution. However, existing works usually learn the representations of users and items in Euclidean space, resulting in a mismatch between the data volume space and the embedding space, which causes significant distortion in the representations. Moreover, the presence of cognitive biases, such as conformity, can also introduce distortion in representation learning. Therefore, we propose a Debiased Hyperbolic Recommendation model, called DHRec. Specifically, first, we choose to model the representations of user and item in hyperbolic space, which has exponential growth capabilities. Second, in addition to the user-item interaction graph, we also construct semantic graphs to capture the semantic neighbor information of users and items. Then, by adjusting the weights of neighbor nodes, we learn debiased representations of users and items, effectively alleviating the bias caused by conformity. Finally, we compute the predicted scores between user and candidate items in hyperbolic space. Extensive experiments on three datasets demonstrate that our model surpasses the strongest baseline, achieving a 11.04% and 10.09% improvement on Recall and NDCG, respectively.

DHRec: A Debiased Hyperbolic Recommendation Model

Modeling episodic memory (EM) remains a significant challenge in both neuroscience and AI, with existing models either lacking interpretability or struggling with practical applications. This paper proposes the Vision-Language Episodic Memory (VLEM) framework to address these challenges by integrating large-scale pretrained models with hippocampal attractor dynamics. VLEM leverages the strong semantic understanding of pretrained models to transform sensory input into semantic embeddings as the neocortex, while the hippocampus supports stable memory storage and retrieval through attractor dynamics. In addition, VLEM incorporates prefrontal working memory and the entorhinal gateway, allowing interaction between the neocortex and the hippocampus. To facilitate real-world applications, we introduce EpiGibson, a 3D simulation platform for generating episodic memory data. Experimental results demonstrate the VLEM framework’s ability to efficiently learn high-level temporal representations from sensory input, showcasing its robustness, interpretability, and applicability in real-world scenarios.

Towards a Vision-Language Episodic Memory Framework: Large-scale Pretrained Model-Augmented Hippocampal Attractor Dynamics

Cognitive impairment is a common disease among middle-aged and elderly people, which seriously affects health outcomes and quality of life, and carries a risk of progressing to severe stages such as dementia. Early identification is beneficial for timely intervention and treatment. This study proposes a new model for predicting cognitive impairment that integrates static and dynamic data, including medical, demographic, and social relationship features. It combines Granger causality with deep learning and uses multiple metrics to evaluate model performance. The performance comparison results between our model and the baseline model demonstrate that our model's predictions have a certain level of accuracy. In addition, causal features derived from Granger causality analysis are used to identify cognitive impairments. Statistical analysis shows that the selected features have statistical significance, further verifying the robustness of our model and its potential for predicting cognitive impairment.

Prediction of Cognitive Impairment in Middle-aged and Elderly People: A Method Based on Granger Causality

The resilience of social networks hinges on identifying users whose departure causes cascading collapse, influenced by both topology and social cognition, such as spatial relationship constraints. Existing studies often overlook how cognitive and behavioral factors shape network fragility. This paper introduces a cognitive-computational framework to detect critical collapsed nodes under spatial constraints, using the (k, σ)-core model to integrate social cohesion (k-core) and spatial thresholds (σ). We propose a pruning algorithm leveraging spatial locality for efficient querying of collapsed nodes and formalize the problem of finding optimal collapsed nodes as an NP-hard task. Our greedy heuristic prioritizes nodes with the most significant cascading impact, similar to human strategies in crises. Experiments on eight real-world networks show our model outperforms topology-only baselines in predicting collapse patterns, especially in spatially-embedded communities. Our findings highlight how spatial constraints and social cohesion amplify systemic fragility, providing insights for designing cognitively-aligned interventions to boost network resilience, bridging computational analysis with cognitive science.

Detecting Critical Collapsed Nodes in Social Networks: A Cognitive Model of Resilience under Spatial Constraints

The diversification of attacks jeopardizes cyberspace's normal operation. This paper proposes a new Harris Hawks Optimization Based on Multiple Strategies (MS-NHHO), inspired by humans' limited cognitive load, collective decision-making, and dynamic learning mechanisms for processing complex information. This paper utilizes the elite chaos reverse learning strategy to improve the algorithm's convergence speed and population diversity. Then, the dynamic adaptive weights are introduced into the escape energy decline mechanism to improve the algorithm's global exploration and local exploitation ability. Finally, the Gaussian random walk strategy enhances the algorithm's anti-stagnation ability. The experimental results confirm the usefulness of the three optimization strategies. Meanwhile, MS-NHHO exhibits satisfactory performance in terms of computational cost, detection performance, and efficiency in several scenarios.

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TableCritic: Refine Table Reasoning via Self-Criticism and Tool Library

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