ArticleNature human behaviour2024
Driving and suppressing the human language network using large language models.
Article in Nature human behaviour, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- The cerebellum specializes for language even in the absence of contralateral neocortical inputs.bioRxiv : the preprint server for biology · 2026Article
- Neural tracking of surprisal and semantic distance in naturalistic movie viewing.bioRxiv : the preprint server for biology · 2026Article
- Unified Brain Mechanisms Underlying the Processing of Word Order Permutations of Arguments and Adjuncts in Sentences.Human brain mapping · 2026Article
- Large Language Models Estimate Fine-Grained Human Color-Concept Associations.Cognitive science · 2026Article
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- Deep Sound Synthesis Matched to Brain Activity Recapitulates Preferential Responses to Speech and Music.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Aligning brains into a shared space improves their alignment with large language models.Nature computational science · 2026Article
- Empowering liver cancer diagnosis and treatment with foundation models: technological innovation and clinical practice.Clinical and experimental medicine · 2026Review
- Sensory encoding of emotion conveyed by the face and visual context.Social cognitive and affective neuroscience · 2026Article
- Do Language Models Know Who Did What to Whom?Open mind : discoveries in cognitive science · 2026Article
- The neurobiology of human sequential signal prediction: Insights from language, music, and mathematics.Neuroscience research · 2026Review
- Neural encoding of auditory processing in schizophrenia.Frontiers in neuroscience · 2026Article
- Evaluating large language models' performance in answering common questions on drug-induced liver injury.JHEP reports : innovation in hepatology · 2025Article
- Multilingual Computational Models Capture a Shared Meaning Component in Brain Responses across 21 Languages.bioRxiv : the preprint server for biology · 2025Article
- The cerebellar components of the human language network.bioRxiv : the preprint server for biology · 2025Article
- Review
- SIGNAL: Dataset for Semantic and Inferred Grammar Neurological Analysis of Language.Scientific data · 2025Article
- In silico discovery of representational relationships across visual cortex.Nature human behaviour · 2025Article
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8 authors.
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Abstract
Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict the magnitude of the brain response associated with each sentence. We then use the model to identify new sentences that are predicted to drive or suppress responses in the human language network. We show that these model-selected novel sentences indeed strongly drive and suppress the activity of human language areas in new individuals. A systematic analysis of the model-selected sentences reveals that surprisal and well-formedness of linguistic input are key determinants of response strength in the language network. These results establish the ability of neural network models to not only mimic human language but also non-invasively control neural activity in higher-level cortical areas, such as the language network.
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38172630What OpenQuestion holds
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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.