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

Nature Computational Science (2025)Cite this article

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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.

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Fig. 1: Improving model-based encoding performance with SRM.
Fig. 2: Reconstructing electrode activity via the shared space.
Fig. 3: Comparison of encoding performance for SRM-reconstructed data and original electrode data for different regions of the language network.
Fig. 4: Cross-participant encoding performance via the shared space.

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Data availability

Sample data are available via Zenodo at https://zenodo.org/records/15220273 (ref. 53), and the full raw dataset is publicly available at https://openneuro.org/datasets/ds005574/versions/1.0.0 (ref. 54). Source data are provided with this paper.

Code availability

Code used to analyze the data is publicly available via GitHub at https://github.com/pritamarnab/SRM-Encoding (ref. 55).

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Acknowledgements

We thank our funders: NIH grant DP1HD091948 (U.H.), NIH grant R01NS109367 (A.F.), NIH CRCNS R01DC022534 (U.H.) and J. Insley Blair Pyne Fund (P.J.R. and U.H.)

Author information

Author notes

  1. These authors contributed equally: Ariel Goldstein, Samuel A. Nastase.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA

    Arnab Bhattacharjee & Peter J. Ramadge

  2. Department of Psychology and Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA

    Zaid Zada, Haocheng Wang, Bobbi Aubrey, Uri Hasson & Samuel A. Nastase

  3. New York University Grossman School of Medicine, New York, NY, USA

    Werner Doyle, Patricia Dugan, Daniel Friedman, Orrin Devinsky & Adeen Flinker

  4. New York University Tandon School of Engineering, Brooklyn, NY, USA

    Adeen Flinker

  5. Google Research, Mountain View, CA, USA

    Ariel Goldstein

  6. Business School, Data Science Department and Cognitive Department, Hebrew University, Jerusalem, Israel

    Ariel Goldstein

  7. Department of Psychology and Center for Computational Language Sciences, University of Southern California, Los Angeles, CA, USA

    Samuel A. Nastase

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Contributions

Conceptualization, A.B., S.A.N., A.G. and U.H.; data curation, B.A., W.D., D.F., P.D., A.F. and O.D.; formal analysis, A.B.; funding acquisition, P.J.R. and U.H.; methodology, A.B., S.A.N. and U.H.; project administration, A.B., S.A.N., P.J.R. and U.H.; software, A.B.; supervision, S.A.N. and U.H.; visualization, A.B., S.A.N. and U.H.; writing—original draft, A.B. and S.A.N.; writing—review and editing, A.B., S.A.N., Z.Z., H.W. and U.H.

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Correspondence to Arnab Bhattacharjee.

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Nature Computational Science thanks Mark Lescroart, Jeremy R. Manning and Alex Murphy for their contribution to the peer review of this work. Primary Handling Editor: Ananya Rastogi, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Source data

Source Data Fig. 1

Contains the .mat files for statistical source data for Fig. 1. It also contains a README.txt file that points to the GitHub notebook code on how to use these source data files to generate the figure.

Source Data Fig. 2

Contains the .mat files for statistical source data for Fig. 2. It also contains a README.txt file that points to the GitHub notebook code on how to use these source data files to generate the figure.

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Contains the .mat files for statistical source data for Fig. 4. It also contains a README.txt file that points to the GitHub notebook code on how to use these source data files to generate the figure.

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Bhattacharjee, A., Zada, Z., Wang, H. et al. Aligning brains into a shared space improves their alignment with large language models. Nat Comput Sci (2025). https://doi.org/10.1038/s43588-025-00900-y

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