心理与行为研究 ›› 2015, Vol. 13 ›› Issue (5): 691-697.

• 应用心理学 • 上一篇    下一篇

抑郁症患者的面部表情识别研究*

傅小兰1,王辉1,2,范伟1,3   

  1. 1 中国科学院心理研究所,北京 100101
    2 解放军第306医院门诊部,北京 100101
    3 认知与人类行为湖南省重点实验室,湖南师范大学,长沙 410082
  • 收稿日期:2015-08-20 出版日期:2015-09-20 发布日期:2015-09-29
  • 通讯作者: 傅小兰,E-mail:fuxl@psych.ac.cn。
  • 基金资助:
    国家重点基础研究发展计划(2011CB302201)和国家自然科学基金项目(61375009)。

Study on the Recognition of Facial Expressions in Patients with Major Depression

Fu Xiaolan1, Wang Hui1,2, Fan Wei1,3   

  1. 1 Institute of Psychology, Chinese Academy of Sciences, Beijing 100101;
    2 The Outpatient Department of PLA 306th Hospital, Beijing 100101;
    3 Hunan Province Key Laboratory of Cognition and Human Behavior, Hunan Normal University, Changsha 410081
  • Received:2015-08-20 Online:2015-09-20 Published:2015-09-29

摘要: 抑郁症患者的情绪信息加工已成为前沿科学问题和研究热点,其特异模式或许可以作为检测抑郁的有效指标。研究发现,与正常人相比,抑郁症患者对正性情绪信息的反应水平以及对情绪表情(尤其是悲伤表情)的辨别能力都有所降低,不仅抑制负性情绪的能力受损,而且在社会情境下精确判断面部表情微弱改变的能力也受损。本文系统梳理了有关抑郁症患者面部表情识别研究及其主要发现,并借鉴对精神分裂症患者微表情识别训练的研究结果提出有必要开展抑郁症患者微表情识别及训练的研究,最后分析该领域目前存在的问题并提出未来研究展望。

关键词: 抑郁症, 面部表情识别, 面部微表情识别

Abstract: Emotional information processing in depression patients has become the forefront of scientific issues and research focus, its specific model may be used as an effective indicator for depression. Previous studies have revealed that the level of response to positive emotional information was reduced and the ability to identify emotional expressions(especially sad expression)was impaired, not only the ability to restrain negative emotions was impaired but also the ability to accurately detect micro expression in the social context. In the current paper we provide an overview on the main findings and advances in previous research on the recognition of facial expressions in depression patients, and draw on the results of the training of micro expression recognition in patients with schizophrenia, and then investigate the feasibility of the development of the micro expression recognition training in patients with depression. Finally, we analyse the problems in the field and put forward the future research directions.

Key words: depression, facial expression recognition, facial micro expression recognition.

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