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From Language to Cognition: How LLMs Outgrow the Human Language Network

Paper · 2025 · Badr AlKhamissi, Greta Tuckute, Yingtian Tang et al.

Summary

The paper examines why large language models resemble activity in the human language network (LN), and how this resemblance develops during pretraining. The authors track eight models (14M–6.7B parameters, including Pythia models) across 34 training checkpoints covering about 300B tokens. They score brain alignment on five neuroimaging datasets restricted to language-selective units. The score comes from ridge-regression Linear Predictivity, normalized by cross-subject consistency. In parallel, they measure formal competence (grammar and syntax) and functional competence (world knowledge and reasoning).

Alignment closely follows formal competence, and both level off early in training. Functional abilities keep improving long after that plateau and relate only weakly to alignment. Once models exceed human-level next-word prediction, the links between alignment, perplexity and human reading-time behavior fade.

This suggests the LN mainly encodes structural aspects of language, while higher cognition likely depends on other brain systems. Because the benchmarks are not saturated, there is still room to build more brain-like models.

Key points

  • Scale of the study: 34 checkpoints, 8 model sizes and five neural benchmarks (e.g., PEREIRA 2018, TUCKUTE 2024, NARRATIVES), plus reading times from Futrell et al. (2018). The authors describe this as the largest rigorous set of language brain benchmarks to date.
  • Formal competence: measured with 2 formal benchmarks, it saturates around ~4B tokens, alongside the plateau in brain alignment.
  • Functional competence: measured with 6 functional benchmarks, it continues to grow throughout training.
  • Fading correlations: alignment correlates with perplexity and behavioral alignment only up to ~2B tokens. In larger models, alignment and behavioral alignment even become negatively correlated late in training.
  • Other findings:
    • Alignment in untrained models stems from context integration.
    • Model size does not reliably predict alignment once feature count is controlled.
    • CKA and RSA are argued to be unsuitable metrics for these datasets.
    • All brain data use English stimuli only.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksNeuroscience · 165 worksCognitive Neuroscience · 164 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Badr AlKhamissi, Greta Tuckute, Yingtian Tang, Taha Osama A Binhuraib, Antoine Bosselut and Martin Schrimpf. 2025. From Language to Cognition: How LLMs Outgrow the Human Language Network. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.emnlp-main.1237

@inproceedings{alkhamissi2025language,
  title = {{From Language to Cognition: How LLMs Outgrow the Human Language Network}},
  author = {Badr AlKhamissi, Greta Tuckute, Yingtian Tang, Taha Osama A Binhuraib, Antoine Bosselut and Martin Schrimpf},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.1237},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.1237}
}

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