Literature DB >> 18217829

On the generality of crowding: visual crowding in size, saturation, and hue compared to orientation.

Ronald van den Berg1, Jos B T M Roerdink, Frans W Cornelissen.   

Abstract

Perception of peripherally viewed shapes is impaired when surrounded by similar shapes. This phenomenon is commonly referred to as "crowding". Although studied extensively for perception of characters (mainly letters) and, to a lesser extent, for orientation, little is known about whether and how crowding affects perception of other features. Nevertheless, current crowding models suggest that the effect should be rather general and thus not restricted to letters and orientation. Here, we report on a series of experiments investigating crowding in the following elementary feature dimensions: size, hue, and saturation. Crowding effects in these dimensions were benchmarked against those in the orientation domain. Our primary finding is that all features studied show clear signs of crowding. First, identification thresholds increase with decreasing mask spacing. Second, for all tested features, critical spacing appears to be roughly half the viewing eccentricity and independent of stimulus size, a property previously proposed as the hallmark of crowding. Interestingly, although critical spacings are highly comparable, crowding magnitude differs across features: Size crowding is almost as strong as orientation crowding, whereas the effect is much weaker for saturation and hue. We suggest that future theories and models of crowding should be able to accommodate these differences in crowding effects.

Mesh:

Year:  2007        PMID: 18217829     DOI: 10.1167/7.2.14

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


  28 in total

1.  Efficiencies for the statistics of size discrimination.

Authors:  Joshua A Solomon; Michael Morgan; Charles Chubb
Journal:  J Vis       Date:  2011-10-19       Impact factor: 2.240

2.  Crowding is tuned for perceived (not physical) location.

Authors:  Steven C Dakin; John A Greenwood; Thomas A Carlson; Peter J Bex
Journal:  J Vis       Date:  2011-08-08       Impact factor: 2.240

3.  Image correlates of crowding in natural scenes.

Authors:  Thomas S A Wallis; Peter J Bex
Journal:  J Vis       Date:  2012-07-13       Impact factor: 2.240

4.  Modeling visual clutter perception using proto-object segmentation.

Authors:  Chen-Ping Yu; Dimitris Samaras; Gregory J Zelinsky
Journal:  J Vis       Date:  2014-06-05       Impact factor: 2.240

5.  Face features and face configurations both contribute to visual crowding.

Authors:  Hsin-Mei Sun; Benjamin Balas
Journal:  Atten Percept Psychophys       Date:  2015-02       Impact factor: 2.199

6.  A summary-statistic representation in peripheral vision explains visual crowding.

Authors:  Benjamin Balas; Lisa Nakano; Ruth Rosenholtz
Journal:  J Vis       Date:  2009-11-19       Impact factor: 2.240

Review 7.  The uncrowded window of object recognition.

Authors:  Denis G Pelli; Katharine A Tillman
Journal:  Nat Neurosci       Date:  2008-10       Impact factor: 24.884

8.  Crowding changes appearance.

Authors:  John A Greenwood; Peter J Bex; Steven C Dakin
Journal:  Curr Biol       Date:  2010-03-04       Impact factor: 10.834

9.  (C)overt attention and visual speller design in an ERP-based brain-computer interface.

Authors:  Matthias S Treder; Benjamin Blankertz
Journal:  Behav Brain Funct       Date:  2010-05-28       Impact factor: 3.759

10.  A neurophysiologically plausible population code model for feature integration explains visual crowding.

Authors:  Ronald van den Berg; Jos B T M Roerdink; Frans W Cornelissen
Journal:  PLoS Comput Biol       Date:  2010-01-22       Impact factor: 4.475

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