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<p>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 <i>r</i> = 0.188 to <i>r</i> = 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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年11月18日

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  • 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

摘要

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.