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首页> 外文期刊>Journal of Cognitive Neuroscience >Evidence for Early Morphological Decomposition in Visual Word Recognition
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Evidence for Early Morphological Decomposition in Visual Word Recognition

机译:视觉单词识别中早期形态分解的证据

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We employ a single-trial correlational MEG analysis technique to investigate early processing in the visual recognition of morphologically complex words. Three classes of affixed words were presented in a lexical decision task: free stems (e.g., taxable), bound roots (e.g., tolerable), and unique root words (e.g., vulnerable, the root of which does not appear elsewhere). Analysis was focused on brain responses within 100-200 msec poststim-ulus onset in the previously identified letter string and visual word-form areas. MEG data were analyzed using cortically constrained minimum-norm estimation. Correlations were computed between activity at functionally defined ROIs and continuous measures of the words' morphological properties. ROIs were identified across subjects on a reference brain and then morphed back onto each individual subject's brain (n = 9). We find evi- dence of decomposition for both free stems and bound roots at the M170 stage in processing. The M170 response is shown to be sensitive to morphological properties such as affix frequency and the conditional probability of encountering each word given its stem. These morphological properties are contrasted with orthographic form features (letter string frequency, transition probability from one string to the next), which exert effects on earlier stages in processing (~130 msec). We find that effects of decomposition at the M170 can, in fact, be attributed to morphological properties of complex words, rather than to purely orthographic and form-related properties. Our data support a model of word recognition in which decomposition is attempted, and possibly utilized, for complex words containing bound roots as well as free word-stems.
机译:我们采用单次试验相关的MEG分析技术来研究形态复杂单词的视觉识别中的早期处理。在词汇决策任务中提供了三类附加词:自由词干(例如应税),绑定词根(例如可容忍)和唯一词根词(例如易受攻击的词,其词根未出现在其他地方)。分析的重点是在先前确定的字母字符串和视觉单词形式区域中,在刺激发生后100-200毫秒内的大脑反应。使用皮质约束的最小范数估计分析MEG数据。计算了功能定义的ROI的活动与单词形态特征的连续测量之间的相关性。在参考大脑上的各个受试者中识别出ROI,然后将其变形回每个受试者的大脑(n = 9)。我们发现在加工的M170阶段,游离茎和结合根均分解。 M170响应显示出对形态特性敏感,例如词缀频率和在给定词干的情况下遇到每个单词的条件概率。这些形态特性与正字形式特征(字母字符串频率,从一个字符串到另一个字符串的过渡概率)形成对比,这些特征对处理的早期阶段(〜130毫秒)产生影响。我们发现,M170上的分解效果实际上可以归因于复杂单词的形态特性,而不是纯粹的拼写和与形式相关的特性。我们的数据支持一种单词识别模型,该模型尝试对包含绑定词根和自由词干的复杂单词进行分解,并可能利用分解。

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