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Natural speech reveals the semantic maps that tile human cerebral cortex

Author

Listed:
  • Alexander G. Huth

    (Helen Wills Neuroscience Institute, University of California)

  • Wendy A. de Heer

    (University of California)

  • Thomas L. Griffiths

    (Helen Wills Neuroscience Institute, University of California
    University of California)

  • Frédéric E. Theunissen

    (Helen Wills Neuroscience Institute, University of California
    University of California)

  • Jack L. Gallant

    (Helen Wills Neuroscience Institute, University of California
    University of California)

Abstract

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.

Suggested Citation

  • Alexander G. Huth & Wendy A. de Heer & Thomas L. Griffiths & Frédéric E. Theunissen & Jack L. Gallant, 2016. "Natural speech reveals the semantic maps that tile human cerebral cortex," Nature, Nature, vol. 532(7600), pages 453-458, April.
    • Handle: RePEc:nat:nature:v:532:y:2016:i:7600:d:10.1038_nature17637
    DOI: 10.1038/nature17637
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    Citations

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    Cited by:

    1. Sam V Norman-Haignere & Josh H McDermott, 2018. "Neural responses to natural and model-matched stimuli reveal distinct computations in primary and nonprimary auditory cortex," PLOS Biology, Public Library of Science, vol. 16(12), pages 1-46, December.
    2. Desjardins, Christoph, 2021. "Don't be too SMART, but SAVE your goals: Proposal for a renewed goal-setting formula for Generation Y," Journal of Applied Leadership and Management, Hochschule Kempten - University of Applied Sciences, Professional School of Business & Technology, vol. 9, pages 73-87.
    3. Katherine Farrow & Gilles Grolleau & Naoufel Mzoughi, 2018. "What in the Word! The Scope for the Effect of Word Choice on Economic Behavior," Kyklos, Wiley Blackwell, vol. 71(4), pages 557-580, November.
    4. Charlotte Caucheteux & Alexandre Gramfort & Jean-Rémi King, 2023. "Evidence of a predictive coding hierarchy in the human brain listening to speech," Nature Human Behaviour, Nature, vol. 7(3), pages 430-441, March.
    5. Maryam Honari-Jahromi & Brea Chouinard & Esti Blanco-Elorrieta & Liina Pylkkänen & Alona Fyshe, 2021. "Neural representation of words within phrases: Temporal evolution of color-adjectives and object-nouns during simple composition," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-17, March.
    6. Lorenza Lucchi Basili & Pier Luigi Sacco, 2017. "Tie-Up Cycles in Long-Term Mating. Part II: Fictional Narratives and the Social Cognition of Mating," Challenges, MDPI, vol. 8(1), pages 1-60, February.
    7. Keiko Ohmae & Shogo Ohmae, 2024. "Emergence of syntax and word prediction in an artificial neural circuit of the cerebellum," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    8. Xue L. Gong & Alexander G. Huth & Fatma Deniz & Keith Johnson & Jack L. Gallant & Frédéric E. Theunissen, 2023. "Phonemic segmentation of narrative speech in human cerebral cortex," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    9. Jörn Diedrichsen & Nikolaus Kriegeskorte, 2017. "Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-33, April.
    10. Chandan Singh & Armin Askari & Rich Caruana & Jianfeng Gao, 2023. "Augmenting interpretable models with large language models during training," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    11. Zsuzsanna Kocsis & Rick L. Jenison & Peter N. Taylor & Ryan M. Calmus & Bob McMurray & Ariane E. Rhone & McCall E. Sarrett & Carolina Deifelt Streese & Yukiko Kikuchi & Phillip E. Gander & Joel I. Ber, 2023. "Immediate neural impact and incomplete compensation after semantic hub disconnection," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    12. Timothy N Rubin & Oluwasanmi Koyejo & Krzysztof J Gorgolewski & Michael N Jones & Russell A Poldrack & Tal Yarkoni, 2017. "Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-24, October.
    13. Francesca Setti & Giacomo Handjaras & Davide Bottari & Andrea Leo & Matteo Diano & Valentina Bruno & Carla Tinti & Luca Cecchetti & Francesca Garbarini & Pietro Pietrini & Emiliano Ricciardi, 2023. "A modality-independent proto-organization of human multisensory areas," Nature Human Behaviour, Nature, vol. 7(3), pages 397-410, March.
    14. David M Alexander & Tonio Ball & Andreas Schulze-Bonhage & Cees van Leeuwen, 2019. "Large-scale cortical travelling waves predict localized future cortical signals," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-34, November.

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