December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Neural correlates of visual crowding in macaque area V4
Author Affiliations
  • Taekjun Kim
    University of Washington
  • Anitha Pasupathy
    University of Washington
Journal of Vision December 2022, Vol.22, 3624. doi:https://doi.org/10.1167/jov.22.14.3624
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      Taekjun Kim, Anitha Pasupathy; Neural correlates of visual crowding in macaque area V4. Journal of Vision 2022;22(14):3624. https://doi.org/10.1167/jov.22.14.3624.

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

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Abstract

Visual crowding refers to the phenomenon where a target object that is easily recognized in isolation becomes difficult to recognize when surrounded by other stimuli (distractors). Results from many past psychophysical studies support the hypothesis that impaired recognition in the presence of distractors stems from how the primate brain integrates information across space to create a unified representation of a visual scene. However, due to a paucity of neurophysiological studies, the neuronal mechanisms of crowding are still poorly understood. In this study, we used a set of 2D shape stimuli to investigate how single neurons in macaque area V4, an intermediate stage of the ventral, object-processing pathway, contribute to visual crowding. We characterized responses and selectivity for a variety of target-distractor relationships and found that V4 shape selectivity gradually decays with increasing distractor number, decreasing target-distractor distance, or an increase in target-distractor similarity. Specifically, salient targets are resistant to crowding. Comparison of V4 responses to target-distractor combination and corresponding noise stimuli with matched texture statistics demonstrates that a texture pooling model does not explain the crowding effect in V4. Instead, our results are more consistent with a normalization model where target saliency titrates the relative gains for the target and distractor influences in a normalization pool.

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