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Evidence of a predictive coding hierarchy in the human brain listening to speech

Author

Listed:
  • Charlotte Caucheteux

    (Meta AI
    Université Paris-Saclay, Inria, Commissariat à l’Énergie Atomique et aux Énergies Alternatives)

  • Alexandre Gramfort

    (Meta AI
    Université Paris-Saclay, Inria, Commissariat à l’Énergie Atomique et aux Énergies Alternatives)

  • Jean-Rémi King

    (Meta AI
    École normale supérieure, PSL University, CNRS)

Abstract

Considerable progress has recently been made in natural language processing: deep learning algorithms are increasingly able to generate, summarize, translate and classify texts. Yet, these language models still fail to match the language abilities of humans. Predictive coding theory offers a tentative explanation to this discrepancy: while language models are optimized to predict nearby words, the human brain would continuously predict a hierarchy of representations that spans multiple timescales. To test this hypothesis, we analysed the functional magnetic resonance imaging brain signals of 304 participants listening to short stories. First, we confirmed that the activations of modern language models linearly map onto the brain responses to speech. Second, we showed that enhancing these algorithms with predictions that span multiple timescales improves this brain mapping. Finally, we showed that these predictions are organized hierarchically: frontoparietal cortices predict higher-level, longer-range and more contextual representations than temporal cortices. Overall, these results strengthen the role of hierarchical predictive coding in language processing and illustrate how the synergy between neuroscience and artificial intelligence can unravel the computational bases of human cognition.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:nathum:v:7:y:2023:i:3:d:10.1038_s41562-022-01516-2
    DOI: 10.1038/s41562-022-01516-2
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    References listed on IDEAS

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    1. 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.
    2. Adam Kepecs & Naoshige Uchida & Hatim A. Zariwala & Zachary F. Mainen, 2008. "Neural correlates, computation and behavioural impact of decision confidence," Nature, Nature, vol. 455(7210), pages 227-231, September.
    3. K. J. Forseth & G. Hickok & P. S. Rollo & N. Tandon, 2020. "Language prediction mechanisms in human auditory cortex," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
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    Cited by:

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    2. 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.

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