Decoding EEG Signals to Explore Next-Word Predictability in the Human Brain

解码脑电信号：探索人类大脑对下一个词的可预测性

Abstract: Humans invented reading and have passed down this complex skill across generations through language. This study provides empirical evidence of the neural mechanisms underlying bottom-up (related to high-order linguistic structure) and top-down (related to next-word predictability) processes, which interact to guide comprehension during reading.

摘要： 人类发明了阅读，并通过语言将这一复杂的技能代代相传。本研究为阅读过程中引导理解的自下而上（与高阶语言结构相关）和自上而下（与下一个词的可预测性相关）过程的神经机制提供了实证证据，这些过程相互作用以指导阅读理解。

While previous studies have focused on either the N400 effects of predictability or lexical categories, research on how predictability influences N400 responses across different lexical categories is limited, mainly due to constraints in publicly available datasets.

尽管以往的研究主要集中在可预测性的 N400 效应或词汇类别上，但由于公开数据集的限制，关于可预测性如何影响不同词汇类别的 N400 响应的研究仍然有限。

Here, we examine how predictability influences brain responses, recorded at millisecond resolution using electroencephalography (EEG), with a focus on the N400 time window (300-500 ms post-stimulus) across different lexical and grammatical categories.

在此，我们研究了可预测性如何影响大脑响应。我们使用脑电图（EEG）以毫秒级分辨率记录数据，重点关注不同词汇和语法类别在 N400 时间窗口（刺激后 300-500 毫秒）内的表现。

Our results indicate that significant differences in N400 responses between high and low cloze probability levels were more pronounced for content words than function words. Among the two primary content categories, verbs exhibited greater N400 differences than nouns, while nouns carried more distinct information about their predictability than verbs.

研究结果表明，在高完形填空概率（cloze probability）和低完形填空概率水平之间，N400 响应的显著差异在实词中比在虚词中更为明显。在两个主要的实词类别中，动词表现出的 N400 差异比名词更大，而名词携带的关于其可预测性的信息比动词更具区分度。

Moreover, we demonstrate that the decoding technique is more effective than the event-related potential (ERP) traditional analysis in capturing more detailed and distinct representations of cognitive processes over time.

此外，我们证明了与传统的事件相关电位（ERP）分析相比，解码技术在捕捉随时间变化的认知过程的更详细、更独特的表征方面更为有效。