Natural speech reveals the semantic maps that tile human cerebral cortex
Open accessAbstract
The meaning of language is represented in regions of the cerebral cortex collectively known as the 'semantic system'. However, little of the semantic system has been mapped comprehensively, and the semantic selectivity of most regions is unknown. Here we systematically map semantic selectivity across the cortex using voxel-wise modelling of functional MRI (fMRI) data collected while subjects listened to hours of narrative stories. We show that the semantic system is organized into intricate patterns that seem to be consistent across individuals. We then use a novel generative model to create a detailed semantic atlas. Our results suggest that most areas within the semantic system represent information about specific semantic domains, or groups of related concepts, and our atlas shows which domains are represented in each area. This study demonstrates that data-driven methods--commonplace in studies of human neuroanatomy and functional connectivity--provide a powerful and efficient means for mapping functional representations in the brain.
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Semantics appear to be encoded in a distributed manner across many cortical regions including prefrontal, speech and language association cortices.
“Semantics appear to be encoded in a distributed manner, across many cortical regions including prefrontal, speech and language association cortices67,68 (Fig. 1; Tang et al. 2023).”
From The speech neuroprosthesis (2024) · cites this paper as [67]
Experimental — accuracy not yet measuredPrevious studies in the neural encoding literature all used LLMs that were only aware of the short-term context, with context window size as large as several tens of words.
“Previous studies in the neural encoding literature all used LLMs that were only aware of the short-term context, i.e., where the context window size was as large as several tens of words1–10.”
From Incremental accumulation of linguistic context in artificial and biological neural networks (2025) · cites this paper as [7]
LLMs have shown success in predicting neural signals associated with narrative processing.
“Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing”
From Incremental accumulation of linguistic context in artificial and biological neural networks (2025) · cites this paper as [7]
In previous neural encoding studies, participants were exposed to language stimuli that encompassed hundreds and even thousands of words.
“This was the case even though the participants in these studies were exposed to language stimuli that encompassed hundreds and even thousands of words.”
From Incremental accumulation of linguistic context in artificial and biological neural networks (2025) · cites this paper as [7]
Current word embedding-based language encoding approaches treat each stimulus word independently and ignore context.
“Current word embedding-based approaches treat each stimulus word independently and thus ignore the influence of context on language understanding.”
From Incorporating Context into Language Encoding Models for fMRI (2018) · cites this paper as [9]
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- Authors
- Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Gallant, Jack L.
- Venue
- Nature
- Published
- 2016-04
- Volume
- 532
- Issue
- 7600
- Pages
- 453-458
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4852309
- OpenAlex
- W2344975321
- Cited by
- 1,504
- References
- 37
- Citations by year
- 2026: 115 · 2025: 169 · 2024: 152 · 2023: 150 · 2022: 130 · 2021: 170 · 2020: 160 · 2019: 169 · 2018: 121 · 2017: 121 · 2016: 43 · 2015: 1 · 2012: 1
- FWCI
- 96.31
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- National Institutes of Health (5r01ey019684-08)
- National Eye Institute (R01-EY019684, EY019684)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Keywords
- semantic system, semantic maps, word meaning, language processing, semantic field, inter-individual consistency, human cerebral cortex
- MeSH terms
- Adult, Auditory Perception, Brain Mapping, Cerebral Cortex, Female, Humans, Magnetic Resonance Imaging, Male, Semantics, Speech, Reproducibility of Results, Principal Component Analysis, Narration
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