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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
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    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. Alan Consorti & Gabriele Sansevero & Irene Marco & Silvia Floridia & Elena Novelli & Nicoletta Berardi & Alessandro Sale, 2024. "An essential role for the latero-medial secondary visual cortex in the acquisition and retention of visual perceptual learning in mice," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    4. 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.

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