The predictive brain: Neural correlates of word expectancy align with large language model prediction probabilities

Predictive coding theory suggests that the brain continuously anticipates upcoming words to optimize language processing, but the neural mechanisms remain unclear, particularly in naturalistic speech. Here, we simultaneously recorded EEG and MEG data from 29 participants while they listened to an audio book and assigned predictability scores to nouns using three LLMs: one BERT model and two multilingual LLaMA models. Our results show that higher predictability is associated with reduced neural responses during word recognition, as reflected in lower N400 amplitudes, and with increased anticipatory activity before word onset. EEG data revealed increased pre-activation in left fronto-temporal regions, while MEG showed a tendency for greater sensorimotor engagement in response to low-predictability words, suggesting a possible motor-related component to linguistic anticipation. These findings provide new evidence that the brain dynamically integrates top-down predictions with bottom-up sensory input to facilitate language comprehension. To our knowledge, this is the first study to demonstrate these effects using naturalistic speech stimuli, bridging computational language models with neurophysiological data. Our findings provide novel insights for cognitive computational neuroscience, advancing the understanding of predictive processing in language and inspiring the development of neuroscience-inspired AI.