Investigating the Timescales of Language Processing with EEG and Language Models

Abstract: This study explores the temporal dynamics of language processing by examining the alignment between word representations from a pre-trained transformer-based LLM, and EEG data. Using a Temporal Response Function (TRF) model, we investigate how neural activity corresponds to model representations across different layers, revealing insights into the interaction between artificial LLMs and brain responses during language comprehension. Our analysis reveals patterns in TRFs from distinct layers, highlighting varying contributions to lexical and compositional processing. Additionally, we used linear discriminant analysis (LDA) to isolate part-of-speech (POS) representations, offering insights into their influence on neural responses and the underlying mechanisms of syntactic processing. These findings underscore EEG's utility for probing language processing dynamics with high temporal resolution. By bridging artificial LLMs and neural activity, this study advances our understanding of their interaction at fine timescales.