期刊文献+

与选择性注意字符的细微结构相关的早内源性成分 被引量:1

EARLY SELECTIVE ATTENTION ENDOGENOUS ERPS COMPONENTS RELATED TO FINE STRUCTURE OF THE PICTOGRAPHIC CHARACTERS
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摘要 在模拟人类自然阅读的方式下,研究了大脑对字符的细微结构进行选择性注意作业时的事件相关脑电位,获得了分布在N1位置及附近时域的与大脑认知过程直接相关的较早内源性成分,对P300的心理学含义有了进一步的了解. In this paper,event related potentials to fine structure of the pictographic characters during emulating true reading in a paradigm of selective attention task were investigated.The early endogenous components that were directly related to the brains cognition process and appeared in the time range of N 1 and N 1 nearby were observed.The clear and unambiguous phychology meaning of P 300 was obtained.
出处 《华中师范大学学报(自然科学版)》 CAS CSCD 北大核心 1997年第3期299-299,共1页 Journal of Central China Normal University:Natural Sciences
基金 国家自然科学基金
关键词 认知过程 选择性注意 事件相关脑电位 P300 cognition process selective attention event related potential emulating true reading early endogenous components
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参考文献2

  • 1陈新浩,中南民族学院学报,1996年,15卷,14页
  • 2魏景汉,心理学报,1995年,27卷,413页

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  • 6Koles Z J,Lind J C,Soong A C K.Spatio-temporal decomposition of the EEG:a general approach to the isolation and localization of sources[J].Electroenceph clinical Neurophysiol,1995,95(4):219-230
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