United States

The Optimal Viewing Position (Nazir, Heller &amp;
Sussman, 1992; Brysbaert &amp; 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 &amp; Nazir,
2005) and may add information about reading
universals across scripts and writing systems.
Keywords: Optimal Viewing Position, Hindi,
Lexical Decision Task

CogSci 2025

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

computer-based experiment

psychology

reading

### 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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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 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

poster

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.

Eye movement behavior during mind wandering in older adults

A Metacognitive Framework for Multi-Table Question Answering
Processing structured data is critical in finance, healthcare, and science. While single-table question answering has advanced, multi-table QA remains challenging due to schema understanding, cross-table reasoning, and complex natural language queries. We propose M2TQA , a novel framework inspired by human cognitive and metacognitive mechanisms. M2TQA integrates metadata extraction, query decomposition, and a metacognitive module to enable interpretable, robust solutions for MTQA. It dynamically simulates human-like reasoning through feedback loops, bridging gaps between natural language understanding and structured data processing. Experiments on four benchmarks show M2TQA outperforms baselines by 94.54% and 33.24% in F1 scores. This work advances MTQA and highlights metacognition’s role in AI, fostering interdisciplinary connections between cognitive science and artificial intelligence.

M2TQA:A Metacognitive Framework for Multi-Table Question Answering

Emotion recognition based on electroencephalography (EEG) plays a significant role in brain-computer interface (BCI) applications. However, individual differences often hinder the generalization of emotion recognition methods to unknown subjects. To address this, we propose an unsupervised domain adaptive model with joint multi-source domain and two-step alignment strategy (JMS2A). The alignment strategy consists of two steps: (1) To capture the structured information from source domain, we combine multiple source domains into a mixed source domain. Simultaneously, a single source domain and the target domain are combined to form a pseudo-target domain, which is then indirectly aligned with the mixed source domain; (2) To extract latent class information from the target domain, we extend supervised contrastive learning to enable direct alignment between source and target domain. We evaluated JMS2A on the SEED and SEED-IV datasets, achieving accuracies of 95.30% and 86.55%, respectively. Experimental results demonstrate that our approach outperforms state-of-the-art methods. The source code is available at https://github.com/cccyangt/JMS2A.

JMS2A: Joint Multi-source Domain and Two-step Alignment Strategy for Cross-subject EEG Emotion Recognition

The Social Intuitionist Model emphasizes the primary role of moral intuitions, rather than analytical moral reasoning, in forming moral judgments. In line with this model, moral emotions, rather than reasoning, are strongly associated with moral actions. This study explored the impact of emotionally evocative film excerpts, specifically those featuring moral dilemmas within varying contexts of social distance, on moral judgment processes. We invited 88 college students to view excerpts from foreign movies and their Chinese remakes under three music conditions: no music, positive music, and negative music. Participants evaluated the characters' actions based on three moral dimensions: Harm, Fairness, and Authority, while also rating their emotional responses. The results indicated that positive music during moral dilemmas elicited strong negative emotional responses. Additionally, viewers judged the protagonist's actions more harshly regarding fairness and placed greater emphasis on social order and authority in Chinese compared to foreign movie excerpts, regardless of the music's emotional valence.

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Mamba-CCA: An Efficient Framework for EEG Emotion Recognition

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