United States

Approximately 31% of the global population is insufficiently active, leading to 3.2 million deaths annually. Sedentary behavior increases the risk of cancer, heart disease, and metabolic disorders. To improve adherence to behavior change interventions, this study introduces personalized humorous nudging for walking behavior. Following a Randomized Control Trial (RCT) design, participants (N=100) received either humorous or non-humorous nudges via a Telegram-based chatbot, “WalkMore,” encouraging daily walks for 7 days. We used the AICTP smart nudge framework (Activity, Influence, Content, Time frame, Presentation) and added humor to the presentation component. Results showed a significant relationship between walking and humor (F=6.48, p&lt;0.05). Manipulation check items reported the nudges’ average funniness rating as 3.48 in experimental group and average personalization rating as 3.64 in control group. The findings indicate efficacy of the proposed augmented nudge framework for behavior change.

CogSci 2025

WalkMore: Exploring the Role of a Personalized Humorous Nudge Architecture on People’s Walking Behavior

field studies

behavioral science

quantitative behavior

comparative analysis

psychology

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Approximately 31% of the global population is insufficiently active, leading to 3.2 million deaths annually. Sedentary behavior increases the risk of cancer, heart disease, and metabolic disorders. To improve adherence to behavior change interventions, this study introduces personalized humorous nudging for walking behavior. Following a Randomized Control Trial (RCT) design, participants (N=100) received either humorous or non-humorous nudges via a Telegram-based chatbot, “WalkMore,” encouraging daily walks for 7 days. We used the AICTP smart nudge framework (Activity, Influence, Content, Time frame, Presentation) and added humor to the presentation component. Results showed a significant relationship between walking and humor (F=6.48, p<0.05). Manipulation check items reported the nudges’ average funniness rating as 3.48 in experimental group and average personalization rating as 3.64 in control group. The findings indicate efficacy of the proposed augmented nudge framework for behavior change.

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. 
<br><br>
* **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.
<br><br>
* **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.
<br><br>

#### 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.

A key challenge in cognitive modeling is capturing how emotional states modulate internal cognitive parameters. While cognitive architectures such as ACT-R (Adaptive Control of Thought-Rational) provide a principled framework for simulating memory and decision-making, their emotional components remain underexplored. This study examines how individual differences in emotional states, particularly anxiety and affective valence, are reflected in core memory-related parameters of ACT-R. Across two experiments using a digits recall task, we introduced emotional variation via affective stimuli and applied a model-fitting procedure to estimate individual values for the mismatch penalty and activation threshold. Results from the second experiment revealed significant correlations between state anxiety and both parameters, suggesting that emotional traits systematically shift memory retrieval dynamics. Our findings offer empirical support for integrating emotion into cognitive architectures without introducing ad hoc modules, and contribute to broader efforts to align the Common Model of Cognition with affective science. This work highlights the potential of inverse modeling as a tool for understanding the emotion–cognition interface and opens new avenues for modeling individual differences in affect-sensitive cognitive systems.

Emotional Parameters in Cognitive Architecture: Examination Through Simple Memory Performance

Considering the effect of collaborative learning based on the Interactive-Constructive-Active-Passive (ICAP) theory, learners can deepen their understanding by engaging in interactive activities in which they elaborate their knowledge by integrating others’ elaborated knowledge. However, it is unclear from the comparison between worked-out and erroneous worked-out examples whether they facilitate interactive and deepen understanding. Therefore, this study examines the effects of erroneous worked-out examples in collaborative learning. We employed a cognitive model based on Adaptive Control of Thought-Rational (ACT-R) to present concept map examples based on learners' relevant knowledge. Errors were adopted link error because links are important for understanding knowledge. The results showed that the worked-out example improved learning performance, but did not facilitate the collaborative learning process. Moreover, the erroneous worked-out example enhanced learning performance by facilitating interactive. These results provide insights into the effect of an erroneous worked-out example and a strategy for presenting it.

Using Erroneous Worked-out Examples for Supporting Collaborative Learning: An Investigation Based on the Cognitive Model of Link Errors using ACT-R

The Optimal Viewing Position (Nazir, Heller &
Sussman, 1992; Brysbaert & Nazir, 2005) for reading
visual words is well studied and documented in most
of alphabetic languages (Grainger, 2022). The
current study investigates the optimal viewing
position in Hindi, written using the Devanagari
script in attempt to understand reading universals
across scripts as suggested by Frost (20120. We
carried out two experiments using the lexical
decision task, one each on words with maatraa (3, 4
and 5 varna) and without maatraa (2, 3, and 4 varna).
Maatras are diacritics used to manifest vowel
pronunciations with aksharas in Hindi and can be be
marked all around the base akshara, adding to the
graphemic complexity of a Hindi word (for a detail
review see, Share et al., 2015; Rimzhim et al., 2021;
Verma et al., 2021). The results demonstrate robust
optimal viewing position effects in terms of reaction
times with a U-shaped curve in relation to initial
fixation position and the optimal viewing position
has been found to be slightly left to the centre of the
word. These seem to be similar to the OVP findings
reported for European languages (Brysbaert & Nazir,
2005) and may add information about reading
universals across scripts and writing systems.
Keywords: Optimal Viewing Position, Hindi,
Lexical Decision Task

Universals in Visual Word Recognition: Investigating the Optimal Viewing Position for Visual Words in Hindi

Emotion recognition from electroencephalogram (EEG) signals is critical for applications in mental health, human-computer interaction, and adaptive systems. However, existing methods struggle with modeling long-term dependencies and addressing the ambiguity between emotion classes. To address these challenges, we propose Mamba-CCA, a novel framework that combines Selective State Space Modeling (SSM) with a Class Confusion-Aware Attention (CCA) mechanism. Mamba-CCA leverages the efficiency of Mamba’s linear-time modeling to capture both local and global temporal features in EEG signals while significantly reducing computational costs. The CCA mechanism further enhances classification by dynamically resolving ambiguities between emotional classes. Experimental results on the SEED and SEED-V datasets demonstrate that Mamba-CCA achieves state-of-the-art classification accuracies of 96.02% and 83.54%, respectively, surpassing the previous best model, CSET-CCA, by 0.84% and 1.48%. Additionally, Mamba-CCA reduces inference time by 20.12% and computational cost by 21%, making it highly suitable for real-time applications.

Mamba-CCA: An Efficient Framework for EEG Emotion Recognition

Driver vigilance estimation plays a critical role in preventing fatigue-related traffic incidents. Current multimodal methods leveraging EEG and EOG signals often suffer from high computational costs due to the reliance on self-attention mechanisms like Transformers. To address these challenges, we propose a novel framework, Selective Attention Sequential Model (SAMM), which integrates a dynamic channel attention mechanism and the Mamba sequence modeling approach. By replacing traditional Transformer modules with Mamba's selective state spaces, our model achieves linear-time complexity while effectively capturing both local and global features.The SAMM framework fuses EEG and EOG signals using early fusion and employs a deep channel attention mechanism to enhance localized feature extraction. Mamba further complements this by efficiently modeling global dependencies in multimodal data, thus reducing computational costs while maintaining high accuracy. Extensive experiments on public datasets, SEED-VIG and SADT, demonstrate that SAMM achieves state-of-the-art performance with a significant reduction in inference time.

SAMM: A Selective Attention Sequential Model for EEG-EOG Vigilance Estimation

Joint improvisation is central to how we navigate the social world, engage and maintain social interactions, and perceive interactions between other people. This project investigates people’s ability to distinguish between joint and individual actions (contemporary joint vs. solo dance improvisation) and the information they use to make this determination. In Experiment 1, participants were asked to identify whether two people were improvising dance movements together or alone. Experiment 2 explored how much people’s decision-making relies on information about the dancers’ facial expressions and gaze direction. Overall, results showed we can accurately identify improvised joint actions, even when the actors’ faces and gaze direction are occluded.

Improvisation in Motion: Exploring How Expertise Affects Perception of Joint Actions

Emotion recognition is a critical area in brain-computer interfaces, with electroencephalography (EEG) shown to be effective for emotional analysis. In domain generalization, cross subject emotion recognition encounters significant generalization challenges, including excessive feature collapse and insufficient capture of EEG features. To tackle these issues, we propose an Anti-Collapse Cross-Domain Consistency Network (AC-CDCN), which leverages Maximum Mean Discrepancy (MMD) to reduce distribution discrepancies between source domains, facilitating the capture of domain-invariant features, and innovatively introduces an Anti-Feature Collapse Strategy (AFCS), which incorporates an Anti-Collapse Domain Discriminator (ACDD) and the code rate loss function, effectively preventing excessive feature collapse. Furthermore, we propose a Flexible Feature Rebalance Module (FlexiReMod), a plug-and-play component that enhances generalization and dynamic feature capture through feature fusion and attention mechanisms. Experimental results indicate AC-CDCN achieved 87.14% (±5.60) and 71.77% (±12.92) accuracy on SEED and SEED-IV datasets, underscoring its significant generalization advantage.

AC-CDCN:A Cross-Subject EEG Emotion Recognition Model with Anti-Collapse Domain Generalization

This study investigates the Horizontal-Vertical Individualism-Collectivism (HVIC) traits of Large Language Models (LLMs), addressing the gap in understanding their cultural and social cognition. HVIC, a cross-cultural psychology framework, offers insights into cognitive patterns shaped by culture. We systematically evaluate multiple LLMs using quantitative (INDCOL scale) method, assessing their intrinsic HVIC traits and ability to simulate cultural and gender-based differences. Our findings reveal LLMs' capacity to capture HVIC nuances, providing a unique lens for studying human cognition through human-LLM comparisons. This research contributes to developing culturally sensitive AI systems and offers new perspectives on human HVIC traits, advancing both theoretical understanding and practical applications of AI.

Unveiling Cultural Cognition in AI: A Systematic Investigation of Horizontal-Vertical Individualism-Collectivism Traits in Large Language Models

Attention deficit hyperactivity disorder (ADHD) and schizophrenia (SCZ) have complex neural mechanisms. This study used fMRI data from resting state and 2-back working memory tasks to analyze the abnormal brain network characteristics of the two. The results showed that ADHD patients had dispersed functional connections under task conditions, and increased node degrees in the prefrontal cortex, insula, anterior cingulate gyrus, and cerebellum, indicating compensatory network reorganization. SCZ patients showed abnormal connections between the default mode network and the limbic cortex, and weakened coupling between the parietal lobe and the attention network, reflecting cognitive integration and emotion regulation defects. Through representational similarity analysis (RSA), it was found that the two diseases had shared abnormal connections in the prefrontal cortex, middle temporal gyrus, and hippocampus, which may be related to working memory regulation disorders. These findings provide potential biomarkers for targeted intervention.

Representational similarity analysis between ADHD and SCZ based on functional brain network

Aging is associated with task-specific changes in eye movements, and thus eye movements during mind wandering (MW) in older adults may differ from young adults. We showed that changes in number of fixations or fixation duration were associated with MW in young adults when searching for information but not older adults, possibly due to aging-related changes in these measures. Similarly, larger variance in pupil diameter change was associated with MW when imagining a scenario in young but not older adults, possibly related to aging-related affect stability. In contrast, lower eye movement consistency was associated with MW when implementing well-learned visual routines in older but not young adults, possibly related to their higher susceptibility to MW interferences. Reduced joint attention with another participant was associated with MW for tasks involving clearly defined strategies for both young and older adults. These results have important implications for monitoring task engagement through eye tracking.

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Emotional Parameters in Cognitive Architecture: Examination Through Simple Memory Performance

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