ArticleNature neuroscience2022
Shared computational principles for language processing in humans and deep language models.
Article in Nature neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 179 papers.
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Who cites it
179 citing papers in PubMed, 423 citations in OpenAlex.
- Linguistics and human brain: a perspective of computational neuroscience.Cognitive neurodynamics · 2026Review
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- A population code for semantics in human hippocampus.Nature neuroscience · 2026Article
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- Neural tracking of surprisal and semantic distance in naturalistic movie viewing.bioRxiv : the preprint server for biology · 2026Article
- The compositionality continuum as a principle for studying the neural basis of intelligence.Nature neuroscience · 2026Review
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- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- Hybrid spatial organization and evidence for magnitude-independent neural coding of linguistic information during sentence production.Science advances · 2026Article
- Unified Brain Mechanisms Underlying the Processing of Word Order Permutations of Arguments and Adjuncts in Sentences.Human brain mapping · 2026Article
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- The human language processing system straightens natural speech.bioRxiv : the preprint server for biology · 2026Article
- Competing speech streams are simultaneously represented in the human cortex during attention switching.PLoS biology · 2026Article
- Frontal cortex organization supporting audiovisual processing during naturalistic viewing.Nature communications · 2026Article
- Brain states recur across diverse narrative contexts during longitudinal viewing.bioRxiv : the preprint server for biology · 2026Article
- Constituent-constrained word prediction during language comprehension.Nature neuroscience · 2026Article
- Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?The European journal of neuroscience · 2026Review
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- How AI can advance psychological science.British journal of psychology (London, England : 1953) · 2026Article
119 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
32 authors at 6 institutions in 3 countries.
Funding
Abstract
Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.
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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.