IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v10y2019i1d10.1038_s41467-019-11440-1.html
   My bibliography  Save this article

Automatic and feature-specific prediction-related neural activity in the human auditory system

Author

Listed:
  • Gianpaolo Demarchi

    (University of Salzburg, Hellbrunnerstraße 34)

  • Gaëtan Sanchez

    (University of Salzburg, Hellbrunnerstraße 34
    Université Claude Bernard Lyon 1, Université de Lyon)

  • Nathan Weisz

    (University of Salzburg, Hellbrunnerstraße 34)

Abstract

Prior experience enables the formation of expectations of upcoming sensory events. However, in the auditory modality, it is not known whether prediction-related neural signals carry feature-specific information. Here, using magnetoencephalography (MEG), we examined whether predictions of future auditory stimuli carry tonotopic specific information. Participants passively listened to sound sequences of four carrier frequencies (tones) with a fixed presentation rate, ensuring strong temporal expectations of when the next stimulus would occur. Expectation of which frequency would occur was parametrically modulated across the sequences, and sounds were occasionally omitted. We show that increasing the regularity of the sequence boosts carrier-frequency-specific neural activity patterns during both the anticipatory and omission periods, indicating that prediction-related neural activity is indeed feature-specific. Our results illustrate that even without bottom-up input, auditory predictions can activate tonotopically specific templates.

Suggested Citation

  • Gianpaolo Demarchi & Gaëtan Sanchez & Nathan Weisz, 2019. "Automatic and feature-specific prediction-related neural activity in the human auditory system," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11440-1
    DOI: 10.1038/s41467-019-11440-1
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-019-11440-1
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-019-11440-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Antonino Greco & Julia Moser & Hubert Preissl & Markus Siegel, 2024. "Predictive learning shapes the representational geometry of the human brain," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11440-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.