Larger language models better align with neural representations of natural language.

Overview

1. Department of Psychology and the Neuroscience Institute, Princeton University, Princeton, United States
2. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States
3. New York University Grossman School of Medicine, New York, United States
4. Business School, Data Science Department and Cognitive Science Department, Hebrew University, Jerusalem, Israel

Abstract

Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. Here, we utilized several families of transformer-based LLMs to investigate the relationship between model size and their ability to capture linguistic information in the human brain. Crucially, a subset of LLMs were trained on a fixed training set, enabling us to dissociate model size from architecture and training set size. We used electrocorticography (ECoG) to measure neural activity in epilepsy patients while they listened to a 30 min naturalistic audio story. We fit electrode-wise encoding models using contextual embeddings extracted from each hidden layer of the LLMs to predict word-level neural signals. In line with prior work, we found that larger LLMs better capture the structure of natural language and better predict neural activity. We also found a logarithmic relationship where the encoding performance peaks in relatively earlier layers as model size increases. We also observed variations in the best-performing layer across different brain regions, corresponding to an organized language processing hierarchy.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• hassonlab.github.io/podcast-ecog-tutorials — at hassonlab.github.io; found in “Data availability”

Data availability

We have recently made the data publicly available (Zada et al., 2025). We have also provided tutorials for preprocessing the data and training encoding models: https://hassonlab.github.io/podcast-ecog-tutorials. For this specific project, the analysis code is available at https://github.com/hassonlab/247-pickling/tree/scaling-paper-1 (copy archived at Kokaja et al., 2026) and https://github.com/hassonlab/247-encoding/tree/scaling-paper-2 (copy archived at Wang, 2026).

The following previously published dataset was used:

Zada Z, Nastase SA, Aubrey B, Jalon I, Goldstein A, Michelmann S, Wang H, Hasenfratz L, Doyle W, Friedman D, Dugan P, Melloni L, Devore S, Devinsky O, Flinker A, Hasson U. 2025. The "Podcast" ECoG dataset. OpenNeuro.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 16 authors, 1 keyword, 6 MeSH terms, 4 funders, 32 references.

Cite

This paper

Hong, Z., Wang, H., Zada, Z., Gazula, H., Turner, D., Aubrey, B., Niekerken, L., Doyle, W., Devore, S., Dugan, P., Friedman, D., Devinsky, O., Flinker, A., Hasson, U., Nastase, S., & Goldstein, A. Y. (2026). Larger language models better align with neural representations of natural language. eLife, 13, RP101204. https://doi.org/10.7554/elife.101204

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