ISSN 0439-755X
CN 11-1911/B

心理学报 ›› 2013, Vol. 45 ›› Issue (4): 416-426.doi: 10.3724/SP.J.1041.2013.00416

• 论文 • 上一篇    下一篇



  1. (1 陕西省行为与认知心理学重点实验室, 陕西师范大学心理学院, 西安 710062) (2 脑与认知国家重点实验室, 中国科学院心理研究所, 北京 100101)
  • 收稿日期:2012-10-22 发布日期:2013-04-25 出版日期:2013-04-25
  • 通讯作者: 杨玉芳
  • 基金资助:


Neurophysiological Mechanism of Implicit Processing of Vocal Emotion Transition

CHEN Xuhai;YANG Xiaohong;YANG Yufang   

  1. (1 Key Laboratory of Behavior and Cognitive Psychology in Shaanxi Province, School of Psychology, Shaanxi Normal University, Xi’an 710062, China) (2 State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China)
  • Received:2012-10-22 Online:2013-04-25 Published:2013-04-25
  • Contact: YANG Yufang

摘要: 人类迅速高效识别语音情绪变化的神经生理机制仍是一个未解之谜。本研究试图通过对被试内隐加工语音情绪变化的即时脑电数据做时域和频域分析, 揭示语音情绪变化加工的神经生理机制。结果发现语音情绪变化诱发N2/P3复合成分, theta频段能量增加和试次间相位相干系数(ITC)增大, beta频段能量降低和ITC增大。这些神经生理指标受情绪变化模式的调节, 情绪变化越激烈, 诱发N2/P3复合成分的潜伏期越早, theta能量和相位变化更强烈。这些结果说明语音情绪变化加工是一个变化检测与整合的过程, 该过程主要由theta和beta频段能量和试次间相位的变化来实现。

关键词: 语音情绪, 神经振荡, ERP, ERSP, ITC

Abstract: Successful decoding of vocal emotion is critical for social interaction. Specifically, it is adaptively important to detect vocal emotion transition in time, since changes in vocal emotion are important signals for detecting the speakers’ emotion change and are common in spoken interactions. Despite the fact that human beings are born with competence to process vocal emotion transition efficiently, the underlying neurophysiological mechanism remains largely unclear. To answer this question, the present study acquired electroencephalogram (EEG) during implicit processing of two types of vocal emotion transitions (neutral to angry and angry to neutral) and their control unchanged vocal emotion from 15 healthy volunteers. Fifty sentences of neutral content produced by a trained native male actor of Mandarin Chinese in neutral and angry prosodies served as original materials. Then we constructed sentences with vocal emotion transition through cross-splicing. The participants were required to perform sound intensity change judgment when the EEG was recorded. In addition to the ordinary ERP analysis in time domain, we also conducted the analysis in frequency domain including event related spectral power (ERSP) and inter-trail coherence (ITC) to specify the neurophysiological source of vocal emotion transition processing. The results indicated that vocal emotion transition elicited N2/P3 complex, as well as theta (4~6 HZ) band power (ERSP) and inter-trail coherence (ITC) increase, irrespective of transition types. In addition, vocal emotion transition induced beta band power decrease and ITC increase. Moreover, these effects were modulated by transition types, specifically, the more intense transition of emotion resulted in shorter latency of N2/P3 complex, and much stronger increase of theta band ERSP and ITC enhancement, and different features in beta band power change. These findings suggested that the processing of vocal emotion transition is a process consisting of change detection and reintegration, similar to the processing of other auditory materials, such as spoken language and music. Moreover, theta and beta band power change and ITC increase appear to be the main neurophysiological source of the processing of vocal emotion transition. In addition, the present study implies that the analysis in frequency domain can be a useful tool for verifying the neurophysiological mechanism of cognitive processing.

Key words: vocal emotion, neuro-oscillation, ERP, ERSP, ITC