Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.
Department of Psychological Science, University of California, Irvine, CA 92697.
Proc Natl Acad Sci U S A. 2024 Apr 2;121(14):e2313665121. doi: 10.1073/pnas.2313665121. Epub 2024 Mar 26.
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.
面部表情在人际互动中起着核心作用；这些表情被用来预测和影响他人的行为。尽管它们很重要，但量化和分析短暂的面部表情动态仍然是一个研究不足的方法学挑战。在这里，我们提出了一种利用机器学习和网络建模来评估面部表情动态的方法。我们使用临床访谈的视频记录，在 96 名被诊断为精神障碍的人和 116 名从未患精神病的成年人的样本中证明了该方法的实用性。被诊断为精神分裂症的参与者往往从中性表情转变为不常见的表情（例如，恐惧、惊讶），而被诊断为其他精神病（例如，伴有精神病的心境障碍）的参与者则转向悲伤的表情。这种方法广泛应用于正常和异常情绪表达的研究，并可以与远程医疗相结合，以改善精神科评估和治疗。
