EEG-based neurophysiological indicators in pronoun resolution using feature analysis

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods Pub Date : 2025-04-29 DOI:10.1016/j.jneumeth.2025.110462

Yingyi Qiu , Wenlong Wu , Yinuo Shi , Hongjuan Wei , Hanqing Wang , Ziao Tian , Mengyuan Zhao

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引用次数: 0

Abstract

Background

Pronoun resolution is a crucial aspect of language comprehension, yet its underlying neural mechanisms remain poorly understood. While previous studies have explored individual linguistic factors, a systematic analysis of Electroencephalography (EEG)-based neurophysiological indicators across different resolution cues (gender, verb bias, and discourse focus) remains unexplored, limiting our understanding of neural-cognitive processes.

New method

We developed an approach combining ReliefF feature selection and Linear Discriminant Analysis (LDA) to analyze EEG data from twenty participants during pronoun resolution tasks. The method examined neural indicators focusing on power spectral density (PSD) and time-domain features, including Zero-Crossing Rate and Peak-to-Peak amplitude.

Results

We identified crucial neural indicators across 14 channels and 4 frequency bands, highlighting PSD features in specific channels (AF3, AF4, FC6, F4, T7, T8, and O2) across theta, beta, and gamma bands. Gender-cue processing exhibited enhanced neural responses in prefrontal and temporal regions with shorter reaction times (748.77 ms) compared to verb bias (903.20 ms) and discourse focus (948.92 ms).

Comparison with existing methods

Unlike previous studies examining individual linguistic factors, our approach simultaneously analyzed multiple resolution cues. The method achieved significant above-chance classification accuracy (49.08 % vs. 33.33 %) across three linguistic factors. This multi-factor analysis provides a more nuanced understanding of pronoun resolution processes than traditional single-factor studies.

Conclusions

Our findings suggest a more efficient, feature-driven processing mechanism for gender-cue resolution, contrasting with more complex, reasoning-dependent processing of verb semantics and discourse cues. These insights have implications for developing computational models of language processing and potential clinical applications for language disorders.

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基于脑电图的代词识别神经生理指标的特征分析

代词解析是语言理解的一个重要方面，但其潜在的神经机制尚不清楚。虽然以前的研究已经探索了个体语言因素，但基于脑电图（EEG）的神经生理指标在不同分辨率线索（性别、动词偏见和话语焦点）上的系统分析仍未被探索，这限制了我们对神经认知过程的理解。本文提出了一种结合ReliefF特征选择和线性判别分析（LDA）的方法，对20名被试在代词分辨任务中的脑电数据进行分析。该方法检测了以功率谱密度（PSD）和时域特征为重点的神经指标，包括过零率和峰对峰幅度。结果我们确定了14个通道和4个频段的关键神经指标，突出了特定通道（AF3、AF4、FC6、F4、T7、T8和O2）在theta、beta和gamma波段的PSD特征。与动词偏向（903.20 ms）和话语聚焦（948.92 ms）相比，性别线索处理在前额叶和颞叶区域的神经反应增强，反应时间（748.77 ms）较短。与以往研究单个语言因素的方法相比，我们的方法同时分析了多个分辨率线索。该方法在三个语言因素上取得了显著的高于机会的分类准确率（49.08 % vs. 33.33 %）。与传统的单因素研究相比，这种多因素分析提供了对代词解析过程更细致入微的理解。结论与动词语义和语篇线索的复杂推理依赖加工相比，我们的研究结果表明性别线索的处理机制更高效、特征驱动。这些见解对开发语言处理的计算模型和语言障碍的潜在临床应用具有重要意义。

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

Journal of Neuroscience Methods 医学-神经科学

CiteScore

7.10

自引率

3.30%

发文量

226

审稿时长

52 days

期刊介绍： The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.