Authors
Sun, H., Jangraw, D. C.
Abstract
Encoding models offer a principled framework for linking computational representations of language to neural activity, but most electroencephalography (EEG) evidence for brain--language alignment comes from tightly controlled, word-by-word reading paradigms. Whether such alignment is detectable during naturalistic reading, and how it is affected by lapses in attention, remains unclear. We addressed these questions using ROAMM, a multimodal dataset containing simultaneous EEG and eye-tracking recordings with time-resolved mind-wandering (MW) annotations from 44 participants reading naturalistic texts. Ridge regression encoding models were trained to predict fixation-aligned EEG spectral power and fixation-related potentials (FRPs) from five word-embedding models (GloVe, word2vec, BERT, GPT-2, and Llama 3). Using permutation testing with false discovery rate correction, we found statistically reliable brain--language alignment across both feature types, with contextual embeddings outperforming static embeddings. Spectral alignment was strongest in the alpha and low-beta bands over parietal electrodes, while FRP-based alignment peaked 200--300 ms after fixation onset over central and parietal-occipital regions. Leveraging ROAMM's span-level MW annotations, we further show that brain--language alignment is systematically reduced during MW, an effect that was substantially larger for oscillatory (PSD) than for event-related (FRP) features. These findings demonstrate that modern language-model representations are reflected in EEG activity during naturalistic reading despite the modality's inherent noise, and that fluctuations in attention constitute an underappreciated source of variability in brain--language encoding studies.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 20 Aug 2026.
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