December 2023
Volume 23, Issue 15
Open Access
Optica Fall Vision Meeting Abstract  |   December 2023
Poster Session I: Gain, not changes in spatial receptive field properties, improves task performance in a neural network attention model
Author Affiliations
  • Daniel Birman
    Department of Biological Structure, University of Washington
  • Kai J Fox
    Department of Psychology, Stanford University
  • Justin L Gardner
    Department of Psychology, Stanford University
Journal of Vision December 2023, Vol.23, 26. doi:https://doi.org/10.1167/jov.23.15.26
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      Daniel Birman, Kai J Fox, Justin L Gardner; Poster Session I: Gain, not changes in spatial receptive field properties, improves task performance in a neural network attention model. Journal of Vision 2023;23(15):26. https://doi.org/10.1167/jov.23.15.26.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Attention allows us to focus sensory processing on behaviorally relevant aspects of the visual world. One potential mechanism of attention is a change in the gain of sensory responses. However, changing gain at early stages could have multiple downstream consequences for visual processing. Which, if any, of these effects can account for the benefits of attention for detection and discrimination? Using a model of primate visual cortex we document how a Gaussian-shaped gain modulation results in changes to spatial tuning properties. Forcing the model to use only these changes failed to produce any benefit in task performance. Instead, we found that gain alone was both necessary and sufficient to explain category detection and discrimination during attention. Our results show how gain can give rise to changes in receptive fields which are not necessary for enhancing task performance.

Footnotes
 Funding: Funding: Washington Research Foundation, NEI T32EY07031, Research to Prevent Blindness, Lions Clubs International Foundation, Hellman Fellows Fund
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