Zhu Chuanlin, Chen Xinyun, Zhang Jianxin, Liu Zhiying, Tang Zhen, Xu Yuting, Zhang Didi, Liu Dianzhi
Department of Psychology, School of Education, Soochow University, Suzhou, China.
Suzhou Psychiatric Hospital, Suzhou, China.
Front Behav Neurosci. 2017 Oct 17;11:199. doi: 10.3389/fnbeh.2017.00199. eCollection 2017.
Previous studies have focused on the characteristics of ordinary facial expressions in patients with depression, and have not investigated the processing characteristics of ecological micro-expressions (MEs, i.e., MEs that presented in different background expressions) in these patients. Based on this, adopting the ecological MEs recognition paradigm, this study aimed to comparatively evaluate facial ME recognition in depressed and healthy individuals. The findings of the study are as follows: (1) background expression: the accuracy (ACC) in the neutral background condition tended to be higher than that in the fear background condition, and the reaction time (RT) in the neutral background condition was significantly longer than that in other backgrounds. The type of ME and its interaction with the type of background expression could affect participants' ecological MEs recognition ACC and speed. Depression type: there was no significant difference between the ecological MEs recognition ACC of patients with depression and healthy individuals, but the patients' RT was significantly longer than that of healthy individuals; and (2) patients with depression judged happy MEs that were presented against different backgrounds as neutral and judged neutral MEs that were presented against sad backgrounds as sad. The present study suggested the following: (1) ecological MEs recognition was influenced by background expressions. The ACC of happy MEs was the highest, of neutral ME moderate and of sadness and fear the lowest. The response to the happy MEs was significantly shorter than that of identifying other MEs. It is necessary to conduct research on ecological MEs recognition; (2) the speed of patients with depression in identifying ecological MEs was slower than of healthy individuals; indicating that the patients' cognitive function was impaired; and (3) the patients with depression showed negative bias in the ecological MEs recognition task, reflecting the lack of happy ME recognition ability and the generalized identification of sad MEs in those patients.
以往的研究主要关注抑郁症患者普通面部表情的特征，尚未对这些患者的生态微表情（即呈现于不同背景表情中的微表情）的加工特征进行研究。基于此，本研究采用生态微表情识别范式，旨在比较评估抑郁症患者与健康个体的面部微表情识别能力。研究结果如下：（1）背景表情：中性背景条件下的准确率（ACC）往往高于恐惧背景条件下的，且中性背景条件下的反应时间（RT）显著长于其他背景条件下的。微表情类型及其与背景表情类型的交互作用会影响参与者对生态微表情的识别准确率和速度。抑郁类型：抑郁症患者与健康个体在生态微表情识别准确率上无显著差异，但患者的反应时间显著长于健康个体；（2）抑郁症患者将呈现于不同背景下的快乐微表情判断为中性表情，将呈现于悲伤背景下的中性微表情判断为悲伤表情。本研究表明：（1）生态微表情识别受背景表情影响。快乐微表情的准确率最高，中性微表情适中，悲伤和恐惧微表情最低。对快乐微表情的反应显著短于识别其他微表情的反应。有必要对生态微表情识别进行研究；（2）抑郁症患者识别生态微表情的速度慢于健康个体；表明患者的认知功能受损；（3）抑郁症患者在生态微表情识别任务中表现出负性偏差，反映出这些患者缺乏对快乐微表情的识别能力以及对悲伤微表情的泛化识别。
