# Investigating the Timescales of Language Processing with EEG and Language Models

arXiv: https://arxiv.org/abs/2406.19884 · HTML: https://mlsanity.com/paper/2406.19884/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [Syntactic Structure from Deep Learning](https://mlsanity.com/paper/2004.10827/) (2020)
- [Finding Syntax in Human Encephalography with Beam Search](https://mlsanity.com/paper/1806.04127/) (2018)
- [Disentangling Syntax and Semantics in the Brain with Deep Networks](https://mlsanity.com/paper/2103.01620/) (2021)
- [Bayesian Modeling of Language-Evoked Event-Related Potentials](https://mlsanity.com/paper/2207.03392/) (2022)

## Abstract (arXiv)

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.