ArticleNature communications2025
Temporal structure of natural language processing in the human brain corresponds to layered hierarchy of large language models.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Linguistics and human brain: a perspective of computational neuroscience.Cognitive neurodynamics · 2026Review
- Pretraining of Embodied Recurrent Networks Bridges the Gap Between Artificial and Cortical Neural Activities.Biomimetics (Basel, Switzerland) · 2026Article
- The human language processing system straightens natural speech.bioRxiv : the preprint server for biology · 2026Article
- Article
- Opportunities and challenges in automated coding of electronic health records: a pilot study for rare disease registries.Frontiers in digital health · 2026Article
- A conceptual analysis: generative artificial intelligence literacy in English as a foreign language writing.Frontiers in psychology · 2026Review
Corrections and comments
- Erratum issued
Authors and funding
22 authors.
Funding
Abstract
Large Language Models (LLMs) offer a framework for understanding language processing in the human brain. Unlike traditional models, LLMs represent words and context through layered numerical embeddings. Here, we demonstrate that LLMs' layer hierarchy aligns with the temporal dynamics of language comprehension in the brain. Using electrocorticography (ECoG) data from participants listening to a 30-minute narrative, we show that deeper LLM layers correspond to later brain activity, particularly in Broca's area and other language-related regions. We extract contextual embeddings from GPT-2 XL and Llama-2 and use linear models to predict neural responses across time. Our results reveal a strong correlation between model depth and the brain's temporal receptive window during comprehension. We also compare LLM-based predictions with symbolic approaches, highlighting the advantages of deep learning models in capturing brain dynamics. We release our aligned neural and linguistic dataset as a public benchmark to test competing theories of language processing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.