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

2026-07-17

AI Summary

This study addresses the lack of systematic comparison between content and function words in their N400 neural responses under varying predictability. Combining high-temporal-resolution EEG with event-related potentials (ERPs) and machine learning decoding, it examines neural activity within the canonical 300–500 ms N400 window under high- and low-cloze probability conditions. The findings reveal, for the first time, that verbs exhibit a significantly stronger N400 predictability effect than nouns and function words, whereas nouns carry more precise predictive information. Furthermore, the study demonstrates that decoding approaches surpass traditional ERP analyses by offering finer-grained insights into the dynamic, top-down predictive representations specific to content words.

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. 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. 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. 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. 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.

Problem

• next-word predictability
• N400
• lexical categories
• EEG
• reading comprehension

Innovation

• EEG decoding
• N400
• next-word predictability
• lexical categories
• ERP analysis