  This study explores the temporal dynamics of language processing by examining
the alignment between word representations from a pre-trained transformer-based
language model, 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
language models 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 language models and neural activity, this
study advances our understanding of their interaction at fine timescales.
