Wolf Karsten
Clinical Director, Marienheide Mental Health Centre, Germany.
Dialogues Clin Neurosci. 2015 Dec;17(4):457-62. doi: 10.31887/DCNS.2015.17.4/kwolf.
Research into emotions has increased in recent decades, especially on the subject of recognition of emotions. However, studies of the facial expressions of emotion were compromised by technical problems with visible video analysis and electromyography in experimental settings. These have only recently been overcome. There have been new developments in the field of automated computerized facial recognition; allowing real-time identification of facial expression in social environments. This review addresses three approaches to measuring facial expression of emotion and describes their specific contributions to understanding emotion in the healthy population and in persons with mental illness. Despite recent progress, studies on human emotions have been hindered by the lack of consensus on an emotion theory suited to examining the dynamic aspects of emotion and its expression. Studying expression of emotion in patients with mental health conditions for diagnostic and therapeutic purposes will profit from theoretical and methodological progress.
近几十年来，对情绪的研究有所增加，尤其是在情绪识别方面。然而，在实验环境中，对情绪面部表情的研究受到可见视频分析和肌电图技术问题的影响。这些问题直到最近才得以克服。自动计算机面部识别领域有了新的发展；能够在社交环境中实时识别面部表情。本综述探讨了测量情绪面部表情的三种方法，并描述了它们在理解健康人群和精神疾病患者情绪方面的具体贡献。尽管最近取得了进展，但由于缺乏适用于研究情绪动态方面及其表达的情绪理论的共识，对人类情绪的研究受到了阻碍。为了诊断和治疗目的而研究心理健康状况患者的情绪表达将受益于理论和方法上的进步。
