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自然阅读已知语句时的预测的神经基础:一项脑电图和眼动共定位研究。

Neural Bases of Predictions During Natural Reading of Known Statements: An Electroencephalography and Eye Movements Co-registration Study.

机构信息

Laboratorio de Inteligencia Artificial Aplicada, Departamento de Computación/Instituto de Ciencias de la Computación, Universidad de Buenos Aires/CONICET, Argentina.

Instituto de Lingüística, Facultad de Filosofía y Letras, Universidad de Buenos Aires, Argentina.

出版信息

Neuroscience. 2023 May 21;519:131-146. doi: 10.1016/j.neuroscience.2023.03.024. Epub 2023 Mar 31.

Abstract

Predictions of incoming words performed during reading have an impact on how the reader moves their eyes and on the electrical brain potentials. Eye tracking (ET) experiments show that less predictable words are fixated for longer periods of times. Electroencephalography (EEG) experiments show that these words elicit a more negative potential around 400 ms (N400) after the word onset when reading one word at a time (foveated reading). Nevertheless, there was no N400 potential during the foveated reading of previously known sentences (memory-encoded), which suggests that the prediction of words from memory-encoded sentences is based on different mechanisms than predictions performed on common sentences. Here, we performed an ET-EEG co-registration experiment where participants read common and memory-encoded sentences. Our results show that the N400 potential disappear when the reader recognises the sentence. Furthermore, time-frequency analyses show a larger alpha lateralisation and a beta power increase for memory-encoded sentences. This suggests a more distributed attention and an active maintenance of the cognitive set, in concordance to the predictive coding framework.

摘要

阅读过程中对即将到来的单词的预测会影响读者的眼球运动和大脑的电活动。眼动追踪(ET)实验表明,较不可预测的单词会被注视更长时间。脑电图(EEG)实验表明,当一次只阅读一个单词(注视阅读)时,这些单词在单词出现后约 400 毫秒(N400)会产生更负的电位。然而,在注视阅读先前已知的句子(记忆编码)时,没有 N400 电位,这表明从记忆编码的句子中预测单词的机制与对常见句子的预测不同。在这里,我们进行了一项眼动-脑电图配准实验,参与者阅读常见和记忆编码的句子。我们的结果表明,当读者识别出句子时,N400 电位会消失。此外,时频分析显示记忆编码句子的 alpha 侧化和 beta 功率增加更大。这表明注意力分布更广泛,并且与预测编码框架一致,对认知集进行了积极的维护。

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