Literature DB >> 31271931

A new framework for understanding vision from the perspective of the primary visual cortex.

Li Zhaoping1.   

Abstract

Visual attention selects only a tiny fraction of visual input information for further processing. Selection starts in the primary visual cortex (V1), which creates a bottom-up saliency map to guide the fovea to selected visual locations via gaze shifts. This motivates a new framework that views vision as consisting of encoding, selection, and decoding stages, placing selection on center stage. It suggests a massive loss of non-selected information from V1 downstream along the visual pathway. Hence, feedback from downstream visual cortical areas to V1 for better decoding (recognition), through analysis-by-synthesis, should query for additional information and be mainly directed at the foveal region. Accordingly, non-foveal vision is not only poorer in spatial resolution, but also more susceptible to many illusions.
Copyright © 2019 Elsevier Ltd. All rights reserved.

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Year:  2019        PMID: 31271931     DOI: 10.1016/j.conb.2019.06.001

Source DB:  PubMed          Journal:  Curr Opin Neurobiol        ISSN: 0959-4388            Impact factor:   6.627


  16 in total

1.  Parallel Advantage: Further Evidence for Bottom-up Saliency Computation by Human Primary Visual Cortex.

Authors:  Li Zhaoping
Journal:  Perception       Date:  2022-01       Impact factor: 1.490

2.  Central-peripheral dichotomy: color-motion and luminance-motion binding show stronger top-down feedback in central vision.

Authors:  Keyan Bi; Yifei Zhang; Yan-Yu Zhang
Journal:  Atten Percept Psychophys       Date:  2022-03-18       Impact factor: 2.199

Review 3.  Priority coding in the visual system.

Authors:  Nicole C Rust; Marlene R Cohen
Journal:  Nat Rev Neurosci       Date:  2022-04-11       Impact factor: 34.870

4.  V1-origin Bidirectional Plasticity in Visual Thalamo-ventral Pathway and Its Contribution to Saliency Detection of Dynamic Visual Inputs.

Authors:  Shang Feng; Zhichang Cui; Zhengqi Han; Hongjian Li; Hongbo Yu
Journal:  J Neurosci       Date:  2022-07-15       Impact factor: 6.709

5.  Correspondence between Monkey Visual Cortices and Layers of a Saliency Map Model Based on a Deep Convolutional Neural Network for Representations of Natural Images.

Authors:  Nobuhiko Wagatsuma; Akinori Hidaka; Hiroshi Tamura
Journal:  eNeuro       Date:  2021-02-09

6.  Redundancy between spectral and higher-order texture statistics for natural image segmentation.

Authors:  Daniel Herrera-Esposito; Leonel Gómez-Sena; Ruben Coen-Cagli
Journal:  Vision Res       Date:  2021-06-30       Impact factor: 1.984

7.  Late disruption of central visual field disrupts peripheral perception of form and color.

Authors:  Kimberly B Weldon; Alexandra Woolgar; Anina N Rich; Mark A Williams
Journal:  PLoS One       Date:  2020-01-30       Impact factor: 3.240

8.  The effect of target salience and size in visual search within naturalistic scenes under degraded vision.

Authors:  Antje Nuthmann; Adam C Clayden; Robert B Fisher
Journal:  J Vis       Date:  2021-04-01       Impact factor: 2.240

Review 9.  A review of interactions between peripheral and foveal vision.

Authors:  Emma E M Stewart; Matteo Valsecchi; Alexander C Schütz
Journal:  J Vis       Date:  2020-11-02       Impact factor: 2.240

10.  Evidence of cortical thickness reduction and disconnection in high myopia.

Authors:  Ya-Jun Wu; Na Wu; Xin Huang; Jie Rao; Li Yan; Ling Shi; Hui Huang; Si-Yu Li; Fu-Qing Zhou; Xiao-Rong Wu
Journal:  Sci Rep       Date:  2020-10-01       Impact factor: 4.379

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