Low-frequency neural activity tracks syntactic information through semantic mediation

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

Brain and Language Pub Date : 2025-02-01 DOI:10.1016/j.bandl.2025.105532

Yuan Xie , Peng Zhou , Likan Zhan , Yanan Xue

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引用次数: 0

Abstract

How our brain integrates single words into larger linguistic units is a central focus in neurolinguistic studies. Previous studies mainly explored this topic at the semantic or syntactic level, with few looking at how cortical activities track word sequences with different levels of semantic correlations. In addition, prior research did not tease apart the semantic factors from the syntactic ones in the word sequences. The current study addressed these issues by conducting a speech perception EEG experiment using the frequency-tagging paradigm. Participants (N = 25, Mean_age = 23;4, 16 girls) were asked to listen to different types of sequences and their neural activity was recorded by EEG. We also constructed a model simulation based on surprisal values of GPT-2. Both the EEG results and the model prediction show that low-frequency neural activity tracks syntactic information through semantic mediation. Implications of the findings were discussed in relation to the language processing mechanism.

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低频神经活动通过语义中介跟踪句法信息。

我们的大脑如何将单个单词整合成更大的语言单位是神经语言学研究的中心焦点。以往的研究主要在语义或句法层面探讨这一主题，很少关注皮层活动如何追踪具有不同语义相关性水平的单词序列。此外，以往的研究并未将词序列中的语义因素与句法因素分开。本研究利用频率标记范式进行语音感知脑电图实验，解决了这些问题。研究对象（N = 25, mean = 23，女生4,16）听不同类型的序列，并记录其脑电活动。我们还基于GPT-2的惊奇值构建了模型仿真。脑电结果和模型预测均表明，低频神经活动通过语义中介跟踪句法信息。研究结果的意义与语言加工机制有关。

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.