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Perceptual restoration of masked speech in human cortex

Author

Listed:
  • Matthew K. Leonard

    (University of California, San Francisco
    Center for Integrative Neuroscience, University of California, San Francisco)

  • Maxime O. Baud

    (University of California, San Francisco)

  • Matthias J. Sjerps

    (University of California, Berkeley
    Donders Institute for Brain, Cognition and Behavior, Centre for Cognitive Neuroimaging, Radboud University)

  • Edward F. Chang

    (University of California, San Francisco
    Center for Integrative Neuroscience, University of California, San Francisco
    University of California, San Francisco)

Abstract

Humans are adept at understanding speech despite the fact that our natural listening environment is often filled with interference. An example of this capacity is phoneme restoration, in which part of a word is completely replaced by noise, yet listeners report hearing the whole word. The neurological basis for this unconscious fill-in phenomenon is unknown, despite being a fundamental characteristic of human hearing. Here, using direct cortical recordings in humans, we demonstrate that missing speech is restored at the acoustic-phonetic level in bilateral auditory cortex, in real-time. This restoration is preceded by specific neural activity patterns in a separate language area, left frontal cortex, which predicts the word that participants later report hearing. These results demonstrate that during speech perception, missing acoustic content is synthesized online from the integration of incoming sensory cues and the internal neural dynamics that bias word-level expectation and prediction.

Suggested Citation

  • Matthew K. Leonard & Maxime O. Baud & Matthias J. Sjerps & Edward F. Chang, 2016. "Perceptual restoration of masked speech in human cortex," Nature Communications, Nature, vol. 7(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13619
    DOI: 10.1038/ncomms13619
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    Cited by:

    1. Cheng‐Han Yu & Meng Li & Colin Noe & Simon Fischer‐Baum & Marina Vannucci, 2023. "Bayesian inference for stationary points in Gaussian process regression models for event‐related potentials analysis," Biometrics, The International Biometric Society, vol. 79(2), pages 629-641, June.
    2. Laura Gwilliams & Jean-Remi King & Alec Marantz & David Poeppel, 2022. "Neural dynamics of phoneme sequences reveal position-invariant code for content and order," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Giovanni M. Di Liberto & Adam Attaheri & Giorgia Cantisani & Richard B. Reilly & Áine Ní Choisdealbha & Sinead Rocha & Perrine Brusini & Usha Goswami, 2023. "Emergence of the cortical encoding of phonetic features in the first year of life," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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