Literature DB >> 17997637

Ladder contours are undetectable in the periphery: a crowding effect?

Keith A May1, Robert F Hess.   

Abstract

We studied the perceptual integration of contours consisting of Gabor elements positioned along a smooth path, embedded among distractor elements. Contour elements either formed tangents to the path ("snakes") or were perpendicular to it ("ladders"). Perfectly straight snakes and ladders were easily detected in the fovea but, at an eccentricity of 6 degrees , only the snakes were detectable. The disproportionate impairment of peripheral ladder detection remained when we brought foveal performance away from ceiling by jittering the orientations of the elements. We propose that the failure to detect peripheral ladders is a form of crowding, the phenomenon observed when identification of peripherally located letters is disrupted by flanking letters. D. G. Pelli, M. Palomares, and N. J. Majaj (2004) outlined a model in which simple feature detectors are followed by integration fields, which are involved in tasks, such as letter identification, that require the outputs of several detectors. They proposed that crowding occurs because small integration fields are absent from the periphery, leading to inappropriate feature integration by large peripheral integration fields. We argue that the "association field," which has been proposed to mediate contour integration (D. J. Field, A. Hayes, & R. F. Hess, 1993), is a type of integration field. Our data are explained by an elaboration of Pelli et al.'s model, in which weak ladder integration competes with strong snake integration. In the fovea, the association fields were small, and the model integrated snakes and ladders with little interference. In the periphery, the association fields were large, and integration of ladders was severely disrupted by interference from spurious snake contours. In contrast, the model easily detected snake contours in the periphery. In a further demonstration of the possible link between contour integration and crowding, we ran our contour integration model on groups of three-letter stimuli made from short line segments. Our model showed several key properties of crowding: The critical spacing for crowding to occur was independent of the size of the target letter, scaled with eccentricity, and was greater on the peripheral side of the target.

Entities:  

Mesh:

Year:  2007        PMID: 17997637     DOI: 10.1167/7.13.9

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


  20 in total

1.  The same binding in contour integration and crowding.

Authors:  Ramakrishna Chakravarthi; Denis G Pelli
Journal:  J Vis       Date:  2011-07-14       Impact factor: 2.240

2.  Positional averaging explains crowding with letter-like stimuli.

Authors:  John A Greenwood; Peter J Bex; Steven C Dakin
Journal:  Proc Natl Acad Sci U S A       Date:  2009-07-16       Impact factor: 11.205

3.  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

4.  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

5.  Dissociable effects of attention and crowding on orientation averaging.

Authors:  Steven C Dakin; Peter J Bex; John R Cass; Roger J Watt
Journal:  J Vis       Date:  2009-10-29       Impact factor: 2.240

Review 6.  Crowding--an essential bottleneck for object recognition: a mini-review.

Authors:  Dennis M Levi
Journal:  Vision Res       Date:  2008-01-28       Impact factor: 1.886

7.  On the rules of integration of crowded orientation signals.

Authors:  Endel Põder
Journal:  Iperception       Date:  2012-07-09

8.  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

9.  Reduced crowding and poor contour detection in schizophrenia are consistent with weak surround inhibition.

Authors:  Valentina Robol; Marc S Tibber; Elaine J Anderson; Tracy Bobin; Patricia Carlin; Sukhwinder S Shergill; Steven C Dakin
Journal:  PLoS One       Date:  2013-04-09       Impact factor: 3.240

10.  Crowding deficits in the visual periphery of schizophrenia patients.

Authors:  Rainer Kraehenmann; Franz X Vollenweider; Erich Seifritz; Michael Kometer
Journal:  PLoS One       Date:  2012-09-26       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.