Literature DB >> 26024452

Crowding, grouping, and object recognition: A matter of appearance.

Michael H Herzog, Bilge Sayim, Vitaly Chicherov, Mauro Manassi.   

Abstract

In crowding, the perception of a target strongly deteriorates when neighboring elements are presented. Crowding is usually assumed to have the following characteristics. (a) Crowding is determined only by nearby elements within a restricted region around the target (Bouma's law). (b) Increasing the number of flankers can only deteriorate performance. (c) Target-flanker interference is feature-specific. These characteristics are usually explained by pooling models, which are well in the spirit of classic models of object recognition. In this review, we summarize recent findings showing that crowding is not determined by the above characteristics, thus, challenging most models of crowding. We propose that the spatial configuration across the entire visual field determines crowding. Only when one understands how all elements of a visual scene group with each other, can one determine crowding strength. We put forward the hypothesis that appearance (i.e., how stimuli look) is a good predictor for crowding, because both crowding and appearance reflect the output of recurrent processing rather than interactions during the initial phase of visual processing.

Mesh:

Year:  2015        PMID: 26024452      PMCID: PMC4429926          DOI: 10.1167/15.6.5

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  120 in total

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Authors:  G Westheimer; G Hauske
Journal:  Vision Res       Date:  1975-10       Impact factor: 1.886

2.  Effects of lateral masking and spatial precueing on gap-resolution in central and peripheral vision.

Authors:  T A Nazir
Journal:  Vision Res       Date:  1992-04       Impact factor: 1.886

3.  Whole report uncovers correctly identified but incorrectly placed target information under visual crowding.

Authors:  Jun-Yun Zhang; Gong-Liang Zhang; Lei Liu; Cong Yu
Journal:  J Vis       Date:  2012-07-10       Impact factor: 2.240

4.  Crowding, feature integration, and two kinds of "attention".

Authors:  Endel Põder
Journal:  J Vis       Date:  2006-02-21       Impact factor: 2.240

5.  When crowding of crowding leads to uncrowding.

Authors:  Mauro Manassi; Bilge Sayim; Michael H Herzog
Journal:  J Vis       Date:  2013-11-08       Impact factor: 2.240

6.  The Bouma law of crowding, revised: critical spacing is equal across parts, not objects.

Authors:  Sarah Rosen; Ramakrishna Chakravarthi; Denis G Pelli
Journal:  J Vis       Date:  2014-12-10       Impact factor: 2.240

7.  Vernier acuity, crowding and cortical magnification.

Authors:  D M Levi; S A Klein; A P Aitsebaomo
Journal:  Vision Res       Date:  1985       Impact factor: 1.886

8.  Asymmetry of visual interference.

Authors:  W P Banks; D W Larson; W Prinzmetal
Journal:  Percept Psychophys       Date:  1979-06

9.  Foveal target repetitions reduce crowding.

Authors:  Bilge Sayim; John A Greenwood; Patrick Cavanagh
Journal:  J Vis       Date:  2014-10-07       Impact factor: 2.240

10.  Facilitating recognition of crowded faces with presaccadic attention.

Authors:  Benjamin A Wolfe; David Whitney
Journal:  Front Hum Neurosci       Date:  2014-02-28       Impact factor: 3.169

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  35 in total

1.  Rapid and long-lasting reduction of crowding through training.

Authors:  Amit Yashar; Jiageng Chen; Marisa Carrasco
Journal:  J Vis       Date:  2015       Impact factor: 2.240

2.  Image content is more important than Bouma's Law for scene metamers.

Authors:  Thomas Sa Wallis; Christina M Funke; Alexander S Ecker; Leon A Gatys; Felix A Wichmann; Matthias Bethge
Journal:  Elife       Date:  2019-04-30       Impact factor: 8.140

3.  Masking, crowding, and grouping: Connecting low and mid-level vision.

Authors:  Josephine Reuther; Ramakrishna Chakravarthi; Jasna Martinovic
Journal:  J Vis       Date:  2022-02-01       Impact factor: 2.240

4.  Broad attention uncovers benefits of stimulus uniformity in visual crowding.

Authors:  Koen Rummens; Bilge Sayim
Journal:  Sci Rep       Date:  2021-12-14       Impact factor: 4.379

5.  Spatial Attention Enhances Crowded Stimulus Encoding Across Modeled Receptive Fields by Increasing Redundancy of Feature Representations.

Authors:  Justin D Theiss; Joel D Bowen; Michael A Silver
Journal:  Neural Comput       Date:  2021-12-15       Impact factor: 2.026

6.  Multidimensional feature interactions in visual crowding: When  configural  cues  eliminate the polarity advantage.

Authors:  Koen Rummens; Bilge Sayim
Journal:  J Vis       Date:  2022-05-03       Impact factor: 2.004

7.  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

8.  Perceptual integration and attention in human extrastriate cortex.

Authors:  Francesca Strappini; Gaspare Galati; Marialuisa Martelli; Enrico Di Pace; Sabrina Pitzalis
Journal:  Sci Rep       Date:  2017-11-01       Impact factor: 4.379

9.  The Honeycomb illusion: Uniform textures not perceived as such.

Authors:  Marco Bertamini; Michael H Herzog; Nicola Bruno
Journal:  Iperception       Date:  2016-07-25

10.  Contextual Interactions in Grating Plaid Configurations Are Explained by Natural Image Statistics and Neural Modeling.

Authors:  Udo A Ernst; Alina Schiffer; Malte Persike; Günter Meinhardt
Journal:  Front Syst Neurosci       Date:  2016-10-04
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