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Gain, not concomitant changes in spatial receptive field properties, improves task performance in a neural network attention model

Kai J Fox, View ORCID ProfileDaniel Birman, View ORCID ProfileJustin L Gardner
doi: https://doi.org/10.1101/2022.03.04.483026
Kai J Fox
1 Stanford University;
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Daniel Birman
2 University of Washington
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  • For correspondence: dbirman@uw.edu
Justin L Gardner
1 Stanford University;
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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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Revision to clarify findings and add new analyses in response to public reviews.

  • https://osf.io/aghqk/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 26, 2023.
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Gain, not concomitant changes in spatial receptive field properties, improves task performance in a neural network attention model
Kai J Fox, Daniel Birman, Justin L Gardner
bioRxiv 2022.03.04.483026; doi: https://doi.org/10.1101/2022.03.04.483026
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Gain, not concomitant changes in spatial receptive field properties, improves task performance in a neural network attention model
Kai J Fox, Daniel Birman, Justin L Gardner
bioRxiv 2022.03.04.483026; doi: https://doi.org/10.1101/2022.03.04.483026

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