Gevins A, Cutillo B, Smith M E
EEG Systems Laboratory and SAM Technology, San Francisco, CA 94105.
Electroencephalogr Clin Neurophysiol. 1995 Feb;94(2):129-47. doi: 10.1016/0013-4694(94)00261-i.
Nine subjects performed a cued S1-S2 matching task in which two sequentially presented visual stimuli (either letter strings or non-verbal graphical patterns) were compared according to verbal (phonemic, semantic, syntactic) or non-verbal (graphic identity) criteria. The Laplacian derivation was used to spatially enhance the topography of averaged evoked potentials (EPs) recorded from 59 scalp electrodes. Several effects distinguished the non-verbal from the verbal conditions. For example, following S1 a P250 EP that reached maximum amplitude over the occipital area was larger for the non-verbal patterns, whereas word and word-like letter strings (but not unfamiliar characters) elicited an N470 in the left temporal region. In anticipation of S2, a CNV-like slow potential was enhanced over posterior regions for the non-verbal stimuli. During the matching interval following S2, a P475 peak was observed to be larger for non-verbal patterns than for letter strings over right frontal and temporal regions. Other effects distinguished the verbal conditions from one another. In particular, following S1 a left frontal P445 potential was enhanced to closed class versus open class words, and following S2 a P620 potential in the left temporal region was enhanced for phonological matching relative to semantic matching. These results suggest that processing of verbal and non-verbal stimuli depends on a network of subprocessors that are regionalized to functionally specialized cortical areas and that operate both sequentially and in parallel in order to extract and synthesize multiple forms of attribute-specific information. In contrast to neuropsychological approaches to the study of pattern recognition and reading, the fine-grain temporal resolution of EP measurements, in combination with the improved spatial resolution obtained through computation of Laplacian derivation wave forms from a large number of electrodes, permits characterization of both the regionalization of subprocesses and the subsecond dynamics of their engagement.
九名受试者进行了一个线索化的S1-S2匹配任务,其中根据语言(音素、语义、句法)或非语言(图形一致性)标准比较两个相继呈现的视觉刺激(字母串或非语言图形模式)。拉普拉斯导数用于在空间上增强从59个头皮电极记录的平均诱发电位(EP)的地形图。有几个效应区分了非语言条件和语言条件。例如,在S1之后,枕叶区域达到最大振幅的P250 EP在非语言模式下更大,而单词和类似单词的字母串(但不是不熟悉的字符)在左颞叶区域引发N470。在预期S2时,非语言刺激在后部区域增强了类似CNV的慢电位。在S2之后的匹配间隔期间,观察到在右额部和颞部区域,非语言模式的P475峰值比字母串的更大。其他效应区分了不同的语言条件。特别是,在S1之后,左额叶P445电位在封闭类单词与开放类单词中增强,在S2之后,左颞叶区域的P620电位在语音匹配相对于语义匹配时增强。这些结果表明,语言和非语言刺激的处理依赖于一个子处理器网络,这些子处理器被区域化到功能专门化的皮层区域,并顺序和并行地运行,以提取和合成多种形式的属性特定信息。与研究模式识别和阅读的神经心理学方法不同,EP测量的精细时间分辨率,结合通过从大量电极计算拉普拉斯导数波形获得的改进空间分辨率,允许表征子过程的区域化及其参与的亚秒级动态。