The brain computes dynamic facial movements for emotion categorization using a third pathway

• 成果类型: Article
• 署名作者: Yan, Yuening; Zhan, Jiayu; Garrod, Oliver G. B.; Ince, Robin A. A.; Jack, Rachael E.; Schyns, Philippe G.
• 署名单位: University of Glasgow; Peking University; Peking University; Peking University; Peking University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9187
• DOI: 10.1073/pnas.2423560122
• 发表日期: 2025-06-24
• 关键词: fusiform face area; acquired prosopagnosia; neural activity; visual-cortex; perception; RECOGNITION; expressions; motion; meg; trustworthiness

摘要

Emerging theories in cognitive neuroscience propose a third brain pathway dedicated to processing biological motion, alongside the established ventral and dorsal pathways. However, its role in computing dynamic social signals for behavior remains uncharted. Here, participants (N = 10) actively categorized dynamic facial expressions synthesized by a generative model and displayed on different face identities-as happy, surprise, fear, anger, disgust, sad-while we recorded their MEG responses. Using representational interaction measures that link facial features with MEG activity and categorization behavior, we identified within each participant a functional social pathway extending from the occipital cortex to the superior temporal gyrus. This pathway selectively represents, communicates, and integrates facial movements that are essential for the behavioral categorization of emotion, while task-irrelevant identity features are filtered out in the occipital cortex. Our findings uncover how the third pathway selectively computes complex dynamic social signals for emotion categorization in individual participants, offering computational insights into the dynamics of neural activity. Significance We present evidence for a third social brain pathway dedicated to processing dynamic social signals-specifically facial expressions-for behavioral emotion categorization. Using generative technology, we isolated facial movements (action units, AUs) while controlling static face identity features. Participants viewed and categorized these emotion-specific facial models while we tracked their brain activity using MEG. Results revealed a functional pathway from the occipital cortex to MT, bank of the STS, and STG that selectively represents, communicates, and integrates dynamic AUs, filtering out static identity features. By precisely controlling stimulus features, our method provides a transparent glass box view of neural mechanisms. This reproducible approach establishes a foundation in computational social neuroscience, linking stimuli, brain processing, and social perception behaviors.