Literature DB >> 22438467

Crowding follows the binding of relative position and orientation.

John A Greenwood1, Peter J Bex, Steven C Dakin.   

Abstract

Crowding--the deleterious influence of clutter on object recognition--disrupts the identification of visual features as diverse as orientation, motion, and color. It is unclear whether this occurs via independent feature-specific crowding processes (preceding the feature binding process) or via a singular (late) mechanism tuned for combined features. To examine the relationship between feature binding and crowding, we measured interactions between the crowding of relative position and orientation. Stimuli were a target cross and two flanker crosses (each composed of two near-orthogonal lines), 15 degrees in the periphery. Observers judged either the orientation (clockwise/counterclockwise) of the near-horizontal target line, its position (up/down relative to the stimulus center), or both. For single-feature judgments, crowding affected position and orientation similarly: thresholds were elevated and responses biased in a manner suggesting that the target appeared more like the flankers. These effects were tuned for orientation, with near-orthogonal elements producing little crowding. This tuning allowed us to separate the predictions of independent (feature specific) and combined (singular) models: for an independent model, reduced crowding for one feature has no effect on crowding for other features, whereas a combined process affects either all features or none. When observers made conjoint judgments, a reduction of orientation crowding (by increasing target-flanker orientation differences) increased the rate of correct responses for both position and orientation, as predicted by our combined model. In contrast, our independent model incorrectly predicted a high rate of position errors, since the probability of positional crowding would be unaffected by changes in orientation. Thus, at least for these features, crowding is a singular process that affects bound position and orientation values in an all-or-none fashion.

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Mesh:

Year:  2012        PMID: 22438467      PMCID: PMC3624616          DOI: 10.1167/12.3.18

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


  75 in total

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5.  Crowding with conjunctions of simple features.

Authors:  Endel Põder; Johan Wagemans
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Review 6.  Distributed hierarchical processing in the primate cerebral cortex.

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Review 7.  The binding problem.

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10.  Probabilistic, positional averaging predicts object-level crowding effects with letter-like stimuli.

Authors:  Steven C Dakin; John Cass; John A Greenwood; Peter J Bex
Journal:  J Vis       Date:  2010-08-01       Impact factor: 2.240

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  11 in total

1.  Dissociable effects of visual crowding on the perception of color and motion.

Authors:  John A Greenwood; Michael J Parsons
Journal:  Proc Natl Acad Sci U S A       Date:  2020-03-19       Impact factor: 11.205

2.  Crowding and Binding: Not All Feature Dimensions Behave in the Same Way.

Authors:  Amit Yashar; Xiuyun Wu; Jiageng Chen; Marisa Carrasco
Journal:  Psychol Sci       Date:  2019-09-18

3.  A Unifying Model of Orientation Crowding in Peripheral Vision.

Authors:  William J Harrison; Peter J Bex
Journal:  Curr Biol       Date:  2015-11-25       Impact factor: 10.834

4.  Crowding during restricted and free viewing.

Authors:  Julian M Wallace; Michael K Chiu; Anirvan S Nandy; Bosco S Tjan
Journal:  Vision Res       Date:  2013-04-04       Impact factor: 1.886

5.  Pooling of continuous features provides a unifying account of crowding.

Authors:  Shaiyan Keshvari; Ruth Rosenholtz
Journal:  J Vis       Date:  2016       Impact factor: 2.240

6.  Can (should) theories of crowding be unified?

Authors:  Mehmet N Agaoglu; Susana T L Chung
Journal:  J Vis       Date:  2016-12-01       Impact factor: 2.240

7.  Visual crowding is a combination of an increase of positional uncertainty, source confusion, and featural averaging.

Authors:  William J Harrison; Peter J Bex
Journal:  Sci Rep       Date:  2017-04-05       Impact factor: 4.379

8.  Challenges to pooling models of crowding: Implications for visual mechanisms.

Authors:  Ruth Rosenholtz; Dian Yu; Shaiyan Keshvari
Journal:  J Vis       Date:  2019-07-01       Impact factor: 2.240

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Journal:  Sci Rep       Date:  2018-08-22       Impact factor: 4.379

Review 10.  Seven Myths on Crowding and Peripheral Vision.

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Journal:  Iperception       Date:  2020-05-19
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