Aligning brains into a shared space improves their alignment with large language models

Arnab Bhattacharjee, Zaid Zada, Haocheng Wang, Bobbi Aubrey, Werner Doyle, Patricia Dugan, Daniel Friedman, Orrin Devinsky, Adeen Flinker, Peter J. Ramadge, Uri Hasson, Ariel Goldstein, Samuel A. Nastase

Abstract: Recent research demonstrates that large language models can predict neural activity recorded via electrocorticography during natural language processing. To predict word-by-word neural activity, most prior work evaluates encoding models within individual electrodes and participants, limiting generalizability. Here we analyze electrocorticography data from eight participants listening to the same 30-min podcast. Using a shared response model, we estimate a common information space across participants. This shared space substantially enhances large language model-based encoding performance and enables denoising of individual brain responses by projecting back into participant-specific electrode spaces—yielding a 37% average improvement in encoding accuracy (from r = 0.188 to r = 0.257). The greatest gains occur in brain areas specialized for language comprehension, particularly the superior temporal gyrus and inferior frontal gyrus. Our findings highlight that estimating a shared space allows us to construct encoding models that better generalize across individuals. Aligning electrocorticography data into a shared space improves how large language models predict brain activity during language comprehension, enhancing encoding accuracy, cross-participant generalization and denoising—especially in language-selective regions.

External IDs:doi:10.1038/s43588-025-00900-y