Neural Networks2022,Vol.15121.DOI:10.1016/j.neunet.2022.03.029

Cortical circuits for top-down control of perceptual grouping

Kon, Maria Francis, Gregory
Neural Networks2022,Vol.15121.DOI:10.1016/j.neunet.2022.03.029

Cortical circuits for top-down control of perceptual grouping

Kon, Maria 1Francis, Gregory1
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作者信息

  • 1. Dept Psychol Sci,Purdue Univ
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Abstract

A fundamental characteristic of human visual perception is the ability to group together disparate elements in a scene and treat them as a single unit. The mechanisms by which humans create such groupings remain unknown, but grouping seems to play an important role in a wide variety of visual phenomena, and a good understanding of these mechanisms might provide guidance for how to improve machine vision algorithms. Here, we build on a proposal that some groupings are the result of connections in cortical area V2 that join disparate elements, thereby allowing them to be selected and segmented together. In previous instantiations of this proposal, connection formation was based on the anatomy (e.g., extent) of receptive fields, which made connection formation obligatory when the stimulus conditions stimulate the corresponding receptive fields. We now propose dynamic circuits that provide greater flexibility in the formation of connections and that allow for top-down control of perceptual grouping. With computer simulations we explain how the circuits work and show how they can account for a wide variety of Gestalt principles of perceptual grouping, texture segmentation tasks, amodal illusory contours, and ratings of perceived groupings. We propose that human observers use such top-down control to implement task-dependent connection strategies that encourage particular groupings of stimulus elements in order to promote performance on various visual tasks.(C) 2022 Elsevier Ltd. All rights reserved.

Key words

Grouping/Gestalt/Segmentation/Strategy/Emergent segmentation/NEURAL DYNAMICS/LAMINAR CIRCUITS/ORIENTATION/ATTENTION/SYMMETRY/BINDING/VISION

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出版年

2022
Neural Networks

Neural Networks

EISCI
ISSN:0893-6080
被引量2
参考文献量34
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