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Prospect Theoretic Loss aversion (λ) suggests we overvalue losses compared to gains during decision making but the influence of age on loss aversion is unclear. We conducted a random-effects meta-analysis on 7 studies that measured loss aversion and the age of participants. The pooled estimate (β=0.0886, 95%CI [0.0087 0.1686] revealed an effect of age on loss aversion. Considering age as continuous (n=1103, 5 studies; β = -0.005233, t= -2.511, p = 0.0122) or categorical (n=1856, 8 studies; β=0.00856, t= 0.111, p = 0.912) reflected substantial differences. Dividing participants broadly (&lt; 46 and &gt; 45 years) to explore differences in estimates of loss aversion (λ&gt;1, λ =1, λ&lt;1), also found significant differences, X2 (2, 1856)=7.5362, p = 0.0231. These findings underscore the veracity of meta-analysis for advancing and explicating previously underexplored aspects of decision theory.

CogSci 2025

A Meta-analysis of Age-related effect on Loss Aversion

behavioral science

cognitive development

decision making

psychology

### Welcome to CogSci Conference 2025!

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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.

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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.

Prospect Theoretic Loss aversion (λ) suggests we overvalue losses compared to gains during decision making but the influence of age on loss aversion is unclear. We conducted a random-effects meta-analysis on 7 studies that measured loss aversion and the age of participants. The pooled estimate (β=0.0886, 95%CI [0.0087 0.1686] revealed an effect of age on loss aversion. Considering age as continuous (n=1103, 5 studies; β = -0.005233, t= -2.511, p = 0.0122) or categorical (n=1856, 8 studies; β=0.00856, t= 0.111, p = 0.912) reflected substantial differences. Dividing participants broadly (< 46 and > 45 years) to explore differences in estimates of loss aversion (λ>1, λ =1, λ<1), also found significant differences, X2 (2, 1856)=7.5362, p = 0.0231. These findings underscore the veracity of meta-analysis for advancing and explicating previously underexplored aspects of decision theory.

poster

Automatic emotion recognition using electroencephalography (EEG) signals has garnered significant attention in recent years. While multi-source domain adaptation methods provide a promising framework for cross-subject emotion recognition, the distributional discrepancies among different source domains often result in negative transfer. To address these challenges, we propose a two-stage Dynamic Domain Selection and Pseudo-label Refinement (DDSPR) model. In the first stage, we introduce a novel Dynamic Domain Selection (DDS) module and an Agent Domain Adaptation Strategy (ADAS) to dynamically select and align source domains. In the second stage, a confidence-based pseudo-label correction strategy is employed to refine target domain labels and mitigate noise. We evaluate the proposed model through cross-subject experiments on the SEED and SEED-IV datasets, achieving accuracies of 91.50% ± 7.05 and 78.05% ± 13.56, respectively.The results demonstrate its effectiveness in emotion recognition performance.

DDSPR: Dynamic Domain Selection and Pseudo-label Refinement for Cross-Subject EEG-based Emotion Recognition

Communication is essential for enhancing the cognition and cooperation of agents in multi-agent reinforcement learning (MARL). However, existing methods often rely on predefined and rigid cognitive patterns, which cannot adapt to dynamic environmental changes and complex inter-agent interactions. In this work, we introduce the Hierarchical Cognitive Graph Autoencoder (HCGA), an adaptive framework that addresses these limitations. HCGA represents inter-agent messages as nodes in a graph with learnable edges, employs a grouping mechanism to integrate related local information into compact latent representations, and then applies hierarchical aggregation to construct a comprehensive global cognition. This approach effectively distills essential information and adaptively uncovers cognitive patterns from dynamic environments, thereby enhancing the overall robustness and efficiency of cognitive processing in MARL tasks. Experimental results demonstrate that HCGA significantly outperforms state-of-the-art methods across various MARL tasks, highlighting its robustness, adaptability, and efficiency.

Hierarchical Cognitive Graph Autoencoder for Multi-Agent Reinforcement Learning

Large Language Models (LLMs) have been extensively employed in Legal Judgment Prediction (LJP) in recent years. However, existing LLM-based methods often fail to effectively simulate the cognitive processes of human judges, particularly in keyword extraction, leading to suboptimal predictions. Inspired by cognitive science, we propose KnowJudge, a knowledge-driven framework, which explicitly models the cognitive process of legal decision-making, leveraging keyword extraction and precedent-based enhancement to guide LLMs in structured legal reasoning. By integrating external legal knowledge tailored to fact descriptions, it refines keyword identification and selects relevant case precedents, thereby mitigating ambiguity in legal judgment.
Unlike conventional methods that rely on fine-tuning, KnowJudge improves performance purely through cognitive-process simulation. Experiments on five benchmarks show that KnowJudge outperforms baseline methods, including both general and legal LLMs.

KnowJudge: A Knowledge-Driven Framework for Legal Judgment Prediction

Accurate recognition of human emotions from EEG signals plays a critical role in affective computing and human-computer interaction. However, existing methods face significant challenges in effectively capturing the sparse, dynamic, and energy-dependent characteristics of brain activity during emotional experiences. To address these challenges, we propose a novel framework, Spatial-Energy-Aware Dynamic Filtering with Sparse Graph Convolutions (SEASGC), which rethinks EEG graph modeling from three perspectives: (1) sparse graph construction to adaptively capture the essential functional relationships between brain regions, (2) dynamic and location-dependent filtering to model nonlinear interactions between EEG nodes, and (3) energy-aware feature aggregation to leverage energy changes as critical indicators of emotional intensity. By explicitly integrating these principles, SEASGC provides a more comprehensive representation of EEG signals for emotion recognition. Extensive experiments on benchmark EEG emotion datasets demonstrate that SEASGC achieves state-of-the-art performance, highlighting its effectiveness and generalizability in modeling the complex spatial-spectral dynamics of EEG signals.

Spatial-Energy-Aware Dynamic Filtering with Sparse Graph Convolutions for EEG Emotion Recognition

The proliferation of DeepFake has engendered widespread societal concerns, positioning its detection as a pressing imperative. Although existing studies have utilized single-subject EEG to distinguish between real and AI-generated content (AIGC), there is still a lack of research exploring dual-brain EEG and multimodal experimental paradigms. This study introduced a novel experimental paradigm, employing EEG hyperscanning to construct a dyadic EEG dataset for AIGC detection. This study employed inter-subject correlation (ISC) analysis to investigate the differences of interpersonal neural synchronization (INS). Additionally, this study proposed a novel neural network model named Squeeze-and-Excitation Depthwise Separable Convolution (SEDSC) for predicting the authenticity of real vs. AIGC. ISC analysis revealed apparent differences in INS under different modalities, valences, and animacy. Specifically, across the four frequency bands, both text and audio modalities elicited higher inter-brain synchronization under real materials than under AIGC materials. SEDSC utilized the phase locking value to assess inter-brain functional connectivity and weighted the inputs from four frequency bands before feeding them into the network for classification. This approach achieved a classification accuracy of 92.42% in distinguishing real from fake content. This study designed a new experimental paradigm and constructed a dataset, confirming that there were evident differences in INS during tasks involving real and AIGC materials. Furthermore, SEDSC successfully predicted the authenticity of the content.

Detection of AI-Generated Contents Based on Dyadic-Brain Neural Synchronization

Parkinson's disease (PD), characterized by its incurable nature, rapid progression, and severe disability, poses significant challenges to the lives of patients and their families. Given the aging population, the need for early detection of PD is increasing. In vitro diagnosis has garnered attention due to its non-invasive nature and low cost. However, existing methods present several challenges: 1) limited training data for facial expression diagnosis; 2) specialized equipment and acquisition environments required for gait diagnosis, resulting in poor generalizability; 3) the risk of misdiagnosis or missed diagnosis when relying on a single modality. To address these issues, we propose a novel multimodal in vitro diagnostic method for PD, leveraging facial expressions and behavioral gait. Our method employs a lightweight deep learning model for feature extraction and fusion, aimed at improving diagnostic accuracy and facilitating deployment on mobile devices. Furthermore, we have established the largest multimodal PD dataset in collaboration with a hospital and conducted extensive experiments to validate the effectiveness of our proposed method.

A Multimodal In Vitro Diagnostic Method for Parkinson’s Disease Combining Facial Expressions and Behavioral Gait Data

Sleep staging is the foundation of sleep analysis. Recent studies have attempted to integrate multimodal signals, such as electroencephalogram (EEG) and electrooculogram (EOG), to enhance the sensitivity of models. However, these attempts still face limitations in effectively merging multimodal signals, particularly in capturing the interplay of global and local information during sleep stages simultaneously. To address this issue, we propose a Multi-granularity Sleep Staging Network (MGSleepNet), which integrates two core modules: the Global Feature Integration module (GFI) and the Fine-grained Information Capture module (FIC). The GFI effectively captures the global features of EEG and EOG signals through multi-scale convolution, channel attention mechanisms, and spatial attention mechanisms. The FIC module obtains fine-grained interaction information between EEG and EOG by segmenting time periods and employing cross-attention mechanisms. The combination of these modules resulted in an accuracy of 83.16% and 82.46% in five-fold cross-validation across subjects on the Sleep-edf-20 and Sleep-edf-78 datasets, respectively. In addition, we also achieved better than opportunity level performance on our own data sets. Finally, the ablation studies confirmed the benefits of integrating global and fine-grained relevance paradigms to enhance sleep staging performance. The model input research indicated that MGSleepNet demonstrates good performance in sleep staging outcomes.

MGSleepNet: A Multi-Granularity Sleep Staging Network Based on EEG and EOG Signals

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.

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

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).

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DDSPR: Dynamic Domain Selection and Pseudo-label Refinement for Cross-Subject EEG-based Emotion Recognition

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