Morphological decomposition in Chinese compound word recognition: Electrophysiological evidence

IF 2.1 2区心理学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY

Brain and Language Pub Date : 2023-06-01 DOI:10.1016/j.bandl.2023.105267

Yanjun Wei , Ying Niu , Marcus Taft , Manuel Carreiras

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Abstract

The present study examined the effect of both morphological complexity and semantic transparency in Chinese compound word recognition. Using a visual lexical decision task, our electrophysiological results showed that transparent and opaque compounds induced stronger Left Anterior Negativity (LAN) than monomorphemic words. This result suggests that Chinese compounds might be decomposed into their constituent morphemes at the lemma level, whereas monomorphemic words are accessed as a whole-word lemma directly from the form level. In addition, transparent and opaque compounds produced a similar N400 as each other, suggesting that transparency did not show an effect on the involvement of constituent morphemes during access to the whole-word lemma. Two behavioral experiments additionally showed similar patterns to the EEG results. These findings support morphological decomposition for compounds at the lemma level as proposed by the full-parsing model, and no evidence is found to support the role of transparency during Chinese compound word recognition.

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汉语复合词识别中的形态分解:电生理证据

本研究考察了形态复杂性和语义透明度对汉语复合词识别的影响。使用视觉词汇决策任务，我们的电生理结果表明，透明和不透明的化合物比单形态词诱导更强的左前负性（LAN）。这一结果表明，汉语复合词可能在引理层面上被分解为其组成语素，而单语素词则直接从形式层面作为一个完整的词引理来访问。此外，透明化合物和不透明化合物产生的N400彼此相似，这表明透明化合物在访问整个单词引理的过程中对组成语素的参与没有表现出影响。另外，两个行为实验显示了与脑电图结果相似的模式。这些发现支持完全解析模型提出的在引理水平上对化合物进行形态学分解，并且没有证据支持透明性在汉语复合词识别中的作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Brain and Language 医学-神经科学

CiteScore

4.50

自引率

8.00%

发文量

审稿时长

20.5 weeks

期刊介绍： An interdisciplinary journal, Brain and Language publishes articles that elucidate the complex relationships among language, brain, and behavior. The journal covers the large variety of modern techniques in cognitive neuroscience, including functional and structural brain imaging, electrophysiology, cellular and molecular neurobiology, genetics, lesion-based approaches, and computational modeling. All articles must relate to human language and be relevant to the understanding of its neurobiological and neurocognitive bases. Published articles in the journal are expected to have significant theoretical novelty and/or practical implications, and use perspectives and methods from psychology, linguistics, and neuroscience along with brain data and brain measures.