), 王亚鹏2(), 康铁君1, 党宸1 1西北师范大学心理学院, 甘肃省行为与心理健康重点实验室, 兰州 730070
2北京师范大学认知神经科学与学习国家重点实验室, 北京 100875
收稿日期:2019-06-06出版日期:2020-01-15发布日期:2019-11-21通讯作者:刘建邑,王亚鹏E-mail:jianyiliu_nwnu@163.com;wangya_peng@163.com基金资助:国家自然科学基金地区项目(31960181)The automatic processing of changes in emotion: Implications from EMMN
DING Xiaobin1, LIU Jianyi1(), WANG Yapeng2(), KANG Tiejun1, DANG Chen1 1 Key Laboratory of Behavioral and Mental Health of Gansu Province, School of Psychology, Northwest Normal University, Lanzhou 730070, China
2 State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
Received:2019-06-06Online:2020-01-15Published:2019-11-21Contact:LIU Jianyi,WANG Yapeng E-mail:jianyiliu_nwnu@163.com;wangya_peng@163.com摘要/Abstract
摘要： 人脑如何自动化加工瞬息万变的情绪信息?研究者们在借鉴听觉通道的失匹配负波(mismatch negativity, MMN)研究的基础上, 进一步发展出了表情失匹配负波(expression mismatch negativity, EMMN), 以此作为视觉情绪信息前注意加工的重要指标。与以往的一般视觉线索的视觉失匹配负波(visual mismatch negativity, vMMN)有所区别, EMMN研究专注于人脑如何自动化加工瞬息万变的情绪信息。当前的研究主要探讨了不同类型的面部表情、不同性别、高低流体智力个体的EMMN差异, 以及自闭症、抑郁症、精神分裂症等异常个体EMMN的特点。此外, 从预测编码的角度阐释了EMMN的机制。今后的研究有必要聚焦EMMN在临床诊断和治疗中的应用, 考察不同情绪线索EMMN的特点, 并进一步揭示EMMN的神经机制。
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