Automating the analysis of facial emotion expression dynamics: A computational framework and application in psychotic disorders

• 成果类型: Article
• 署名作者: Hall, Nathan T.; Hallquist, Michael N.; Martin, Elizabeth A.; Lian, Wenxuan; Jonas, Katherine G.; Kotov, Roman
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of California System; University of California Irvine; State University of New York (SUNY) System; Stony Brook University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9129
• DOI: 10.1073/pnas.2313665121
• 发表日期: 2024-04-02
• 关键词: self-report; daily-life; schizophrenia; phenomenology; individuals; experience; models; pain

摘要

Facial emotion expressions play a central role in interpersonal interactions; these displays are used to predict and influence the behavior of others. Despite their importance, quantifying and analyzing the dynamics of brief facial emotion expressions remains an understudied methodological challenge. Here, we present a method that leverages machine learning and network modeling to assess the dynamics of facial expressions. Using video recordings of clinical interviews, we demonstrate the utility of this approach in a sample of 96 people diagnosed with psychotic disorders and 116 never- psychotic adults. Participants diagnosed with schizophrenia tended to move from neutral expressions to uncommon expressions (e.g., fear, surprise), whereas participants diagnosed with other psychoses (e.g., mood disorders with psychosis) moved toward expressions of sadness. This method has broad applications to the study of normal and altered expressions of emotion and can be integrated with telemedicine to improve psychiatric assessment and treatment.