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In some cases, cognitive function can change owing to external factors without physiological effects. This is considered to be influenced by emotional arousal caused by schema activity. This study analyzed the relationships between emotional arousal and task performance to determine whether schema can lead to emotional arousal and enhance task performance. Emotional arousal was assessed based on pupil dilation using eye tracking, and task performance was measured based on reaction times to the stimuli. No main effect of condition on reaction time was observed. However, the experimental results showed that the larger the pupil dilation, the shorter the reaction time, but only when schema activation stimuli were present. These findings suggest that schema-induced emotional arousal may improve performance without the intake of substances with physiological effects. This may also offer insight into mechanisms underlying the placebo effect, though further investigation is required.

CogSci 2025

Schema-Induced Emotional Arousal Enhances Task Performance:  A Pupillometric Investigation of Top-Down Cognitive Influence

quantitative behavior

emotion perception

semantic memory

statistics

eye tracking

psychology

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. 

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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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#### 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.

Recommender systems personalize content delivery based on user’s interaction history. However, not all clicks result from deliberate decisions—many arise from intuitive reactions. Inspired by the dual process theory, we argue that intuitive clicks are primarily driven by System 1, reacting to superficial cues, while analytical clicks involve deeper processing by System 2, considering the semantic meaning and long-term preference. However, existing models overlook these cognitive mechanisms. To address this, we propose DualRec, a novel recommendation method that models both intuitive and analytical behaviors. DualRec encodes items using language models, leveraging shallow layers for superficial understanding (System 1) and deep layers for semantic comprehension (System 2). It employs Transformer-based encoders with two attention mechanisms to capture intuitive “fast” and analytical “slow” click patterns. A learnable fusion layer balances these behaviors. Extensive experiments demonstrate that DualRec outperforms existing methods and highlights the importance of integrating both cognitive processes in recommendations.

Clicking, Fast and Slow: Towards Intuitive and Analytical Behaviors Modeling for Recommender Systems

Cross-Domain Sequential Recommendation (CDSR) predicts user behavior by leveraging historical interactions across multiple domains, focusing on modeling cross-domain preferences through intra- and inter-sequence item relationships. Inspired by human cognitive processes, we propose Hierarchical Attention Fusion of Visual and Textual Representations (HAF-VT), a novel approach integrating visual and textual data to enhance cognitive modeling. Using the frozen CLIP model, we generate image and text embeddings, enriching item representations with multimodal data. A hierarchical attention mechanism jointly learns single-domain and cross-domain preferences, mimicking human information integration. Evaluated on four e-commerce datasets, HAF-VT outperforms existing methods in capturing cross-domain user interests, bridging cognitive principles with computational models and highlighting the role of multimodal data in sequential decision-making.

Cognitive-Inspired Hierarchical Attention Fusion With Visual and Textual for Cross-Domain Sequential Recommendation

Brain-to-Image reconstruction aims to recover visual stimuli perceived by humans from brain activity. However, the reconstructed visual stimuli often missing details and semantic inconsistencies, which may be attributed to insufficient semantic information. To address this issue, we propose an approach named Fine-grained Brain-to-Image reconstruction (FgB2I), which employs fine-grained text as bridge to improve image reconstruction. FgB2I comprises three key stages: detail enhancement, decoding fine-grained text descriptions, and text-bridged brain-to-image reconstruction. In the detail-enhancement stage, we leverage large vision–language models to generate fine-grained captions for visual stimuli and experimentally validate its importance. We propose three reward metrics (object accuracy, text-image semantic similarity, and image-image semantic similarity) to guide the language model in decoding fine-grained text descriptions from fMRI signals. The fine-grained text descriptions can be integrated into existing reconstruction methods to achieve fine-grained Brain-to-Image reconstruction.

Improving Brain-to-Image Reconstruction via Fine-Grained Text Bridging

Electroencephalogram (EEG) has become an important indicator reflecting emotions. Due to its natural graph structure characteristics, it has made significant progress in the emotional recognition using graph convolutional networks (GCN). However, existing methods face limitations: (1) insufficient integration of psychological prior knowledge, limiting the utilization of brain activity patterns, and (2) simplistic node relationship construction, neglecting the universality and functional connectivity of brain regions. Therefore, we propose a dual-path parallel graph convolutional network (DP-GCN). The first path leverages psychological prior knowledge to segment electrodes into brain regions and employs an attention mechanism to integrate features. The second approach employs a data-driven method, using a sparse stacked autoencoder to reconstruct brain region features, while a learnable, input-independent adjacency matrix captures EEG patterns associated with emotions. Finally, a cross-attention mechanism integrates features from both paths. DP-GCN has been evaluated on public dataset, achieving an accuracy of 82.69%±4.16%, demonstrating its competitive performance.

Dual-Path Parallel Graph Convolution Combining Brain Region Partitioning and Data-Driven Learning for EEG Emotion Recognition

Emotion recognition is crucial for applications like human-computer interaction, safe driving, and remote education, with EEG-based methods providing more authentic insights than facial or speech-based approaches. In this paper, we propose an Adaptive Multiview Fusion Transformer (AMFT) that effectively fuses Differential Entropy (DE) and Power Spectral Density (PSD) features in EEG signals. AMFT consists of three main components—a Multi-Perspective Embedder (MPE), a Dual Cross Attention Module (DCAM), and a ClsTransformerEncoder (CTE)—which use multiview projection and iterative cross-attention to combine DE and PSD features. Experiments on the SEED-VII dataset show that AMFT achieves higher accuracy and F1 scores in both multi-class and binary-class emotion recognition tasks, with improved stability compared to existing methods. Ablation studies confirm the key role of the multiview embedder and cross-attention module in boosting performance, offering new insights for EEG-based emotion recognition and biomedical signal analysis. Our code is available at https://github.com/sizhiyier/AMFT.

Adaptive Multiview Fusion Transformer for EEG-Based Emotion Recognition

Research suggests that gestures are crucial in conveying numerical information, particularly during counting tasks involving larger set sizes (Gunderson et al., 2015; Gibson et al., 2018). Besides, children's understanding of numbers develops in stages, with significant milestones around four and five years old (e.g., cardinality). However, the use of gestures by preschoolers during counting tasks, especially with higher set sizes, remains poorly understood. Data were collected from 59 children (Mage = 4;6, 26 girls) who placed beads onto a stick matching dotted cards (1-9) in video-recorded sessions for later gesture coding. Glmer analysis revealed that higher set sizes (4 to 9) correspond to increased gesture use (p<.001). In contrast, set sizes negatively relate to counting accuracy (p<.001). However, gesture use did not significantly relate to counting accuracy (p=0.74). Our findings indicate that gestures are essential to children's numerical understanding regardless of accuracy, particularly when tackling tasks beyond their comprehension level.

Children rely on gestures more when the set size of countable items increase

Reasoning based on relations is essential for human learning and thinking. However, how this ability is acquired remains unclear. Two accounts offer different views: whereas one suggests that relational reasoning is related to cognitive development, the other views it as a learned bias. We conducted two experiments to investigate whether relational reasoning is a learned bias. Experiment 1 tested adults on the Relational Match-to-Sample task. The results revealed that a significant proportion of adults failed to engage in the expected relational reasoning; instead, they relied on the object similarity. Scores on Raven Advanced Progressive Matrices (APM-12) suggest that the preference for similarity is not tied to general cognitive ability. Experiment 2 tested whether similarity-based reasoners can learn relational bias when prompted to generate explanations. The results showed that participants who primarily generated relational explanations successfully learned relational bias. Taken together, this study suggests that relational reasoning is a learned bias.

Relational reasoning as a learned bias: Evidence that generating explanations facilitates relational matching in adults

In many languages, the speakers use taste-related words to express emotions. This preregistered study extends preceding survey study findings by applying attribute conditioning paradigm to examine whether emotional words influence gustatory imagery of nonsense words. Native Japanese-speaking participants underwent an attribute conditioning consisting of five phases: the first evaluative phase, a conditioning phase, the second evaluative phase, a contingency awareness questionnaire, and a contingency memory task. In the evaluative phases, participants rated nonsense words on emotional (i.e., valence, arousal, and familiarity) and taste-related (i.e., sweetness, saltiness, sourness, bitterness, umami, and spiciness) impressions. Emotional words with positive (e.g., happy, vacation) or negative meanings (e.g., sad, thief) were used as unconditioned stimuli. Results demonstrated that nonsense words paired with positive-meaning words were associated with sweetness and umami, while those paired with negative-meaning words were associated with saltiness, sourness, bitterness, and spiciness. These findings provide psycholinguistic insights into metaphorical expression using taste-related words.

Are “sweet talks” literally sweet?: A study of taste imagery evoked by Japanese emotional words

Once the human brain learns a concept, it can easily transfer the learned knowledge across diverse environments without referring back to the original learning materials. Inspired by this cognitive process, Black-Box Domain Adaptation (BBDA) has been purposed to transfer the knowledge learned in a black-box source model to the target domain without any premise for source data or model parameters. Existing BBDA methods mainly rely on knowledge distillation or sample selection with pseudo labels, overlooking the different learning difficulties of classes. This results in easy-learning classes dominating the adaptation process and thus degrades adaptation performance. Motivated by the significant success of Vision-Language models (ViL model), we propose a novel method that integrates the knowledge of ViL model to achieve adaptation while mitigating learning imbalance. Experiments on various datasets demonstrate the effectiveness of the proposed method.

Overcoming Learning Imbalance with Fusing Vision-Language Model Knowledge for Black-Box Domain Adaptation

Face recognition technology raises privacy concerns as face images contain identity and soft biometric attributes. Existing methods struggle to balance privacy, image quality, and identity retention, often neglecting human perception. We propose a diffusion-based identity-preserving face privacy method that enhances privacy at the cognitive level while maintaining identity recognition. Unlike GAN-based approaches, our model generates higher-quality, more diverse, and fine-detail privacy-enhanced faces. It selectively obfuscates identity-critical regions and enables flexible attribute modifications via natural language prompts, eliminating reliance on predefined classifiers. Additionally, our method significantly reduces inference time from minutes to seconds, improving practical feasibility. Experiments show superior performance over state-of-the-art methods in both algorithmic and human cognition-based evaluations, effectively confusing human observers while ensuring reliable machine-based identity recognition.

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Clicking, Fast and Slow: Towards Intuitive and Analytical Behaviors Modeling for Recommender Systems

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