IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v402y1999i6758d10.1038_46027.html
   My bibliography  Save this article

Signal but not noise changes with perceptual learning

Author

Listed:
  • J. Gold

    (University of Toronto)

  • P. J. Bennett

    (University of Toronto)

  • A. B. Sekuler

    (University of Toronto)

Abstract

Perceptual discrimination improves with practice. This ‘perceptual learning’ is often specific to the stimuli presented during training1,2,3,4,5, indicating that practice may alter the response characteristics of cortical sensory neurons6,7. Although much is known about how learning modifies cortical circuits8, it remains unclear how these changes relate to behaviour. Different theories assume that practice improves discrimination by enhancing the signal1,9,10, diminishing internal noise11,12 or both13. Here, to distinguish among these alternatives, we fashioned sets of faces and textures whose signal strength could be varied, and we trained observers to identify these patterns embedded in noise. Performance increased by up to 400% across several sessions over several days. Comparisons of human performance to that of an ideal discriminator showed that learning increased the efficiency with which observers encoded task-relevant information. Observer response consistency, measured by a double-pass technique in which identical stimuli are shown twice in each experimental session14,15, did not change during training, showing that learning had no effect on internal noise. These results indicate that perceptual learning may enhance signal strength, and provide important constraints for theories of learning.

Suggested Citation

  • J. Gold & P. J. Bennett & A. B. Sekuler, 1999. "Signal but not noise changes with perceptual learning," Nature, Nature, vol. 402(6758), pages 176-178, November.
  • Handle: RePEc:nat:nature:v:402:y:1999:i:6758:d:10.1038_46027
    DOI: 10.1038/46027
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/46027
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/46027?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Richard F Murray & Khushbu Patel & Alan Yee, 2015. "Posterior Probability Matching and Human Perceptual Decision Making," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-16, June.
    2. Hojin Jang & Frank Tong, 2024. "Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Ari S. Benjamin & Ling-Qi Zhang & Cheng Qiu & Alan A. Stocker & Konrad P. Kording, 2022. "Efficient neural codes naturally emerge through gradient descent learning," Nature Communications, Nature, vol. 13(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:nature:v:402:y:1999:i:6758:d:10.1038_46027. 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.