Literature DB >> 17292938

Bayesian contour extrapolation: geometric determinants of good continuation.

Manish Singh1, Jacqueline M Fulvio.   

Abstract

We investigated whether observers use rate of change of curvature in visually extrapolating contour shape. Arcs of Euler spirals with positive or negative rate of change of curvature gamma (hence linearly increasing or decreasing curvature) disappeared behind the straight-edge of a half-disk occluder. Observers adjusted the position and the orientation of a line probe around the curved portion of the occluder to optimize the percept of extrapolation. These paired measurements were obtained at multiple distances from the point of occlusion in order to map out the extended shape of visually extrapolated contours. An Euler-spiral model was fit to the extrapolation data corresponding to each inducing contour. Maximum-likelihood estimates of extrapolation rate of change of curvature gamma/\ were consistently found to be negative, indicating that visually extrapolated contours are characterized by decaying curvature, irrespective of whether inducer curvature is increasing or decreasing as it approaches the occluder. Moreover, extrapolation gamma/\ was found to exhibit no systematic dependence on inducer gamma. The results indicate that the visual system does not extrapolate rate of change of contour curvature. They support a Bayesian model of contour extrapolation, in which the decay in extrapolation curvature derives from an interaction between a likelihood bias to continue estimated contour curvature, and a prior bias to minimize contour curvature. Rate of change of curvature does not play a role.

Mesh:

Year:  2007        PMID: 17292938     DOI: 10.1016/j.visres.2006.11.022

Source DB:  PubMed          Journal:  Vision Res        ISSN: 0042-6989            Impact factor:   1.886


  13 in total

1.  Principles of contour information: Reply to Lim and Leek (2012).

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Journal:  Psychol Rev       Date:  2012-07       Impact factor: 8.934

2.  Analysis of the studies of the perception of fragmented images: global description and perception using local features.

Authors:  Yu E Shelepin; V N Chikhman; N Foreman
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3.  Studies of the perception of incomplete outline images of different sizes.

Authors:  O A Vakhrameeva; Yu E Shelepin; A Yu Mezentsev; S V Pronin
Journal:  Neurosci Behav Physiol       Date:  2009-10-15

4.  Contour complexity and contour detection.

Authors:  John Wilder; Jacob Feldman; Manish Singh
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Review 5.  A century of Gestalt psychology in visual perception: I. Perceptual grouping and figure-ground organization.

Authors:  Johan Wagemans; James H Elder; Michael Kubovy; Stephen E Palmer; Mary A Peterson; Manish Singh; Rüdiger von der Heydt
Journal:  Psychol Bull       Date:  2012-07-30       Impact factor: 17.737

6.  Decoding information about dynamically occluded objects in visual cortex.

Authors:  Gennady Erlikhman; Gideon P Caplovitz
Journal:  Neuroimage       Date:  2016-09-20       Impact factor: 6.556

Review 7.  A century of Gestalt psychology in visual perception: II. Conceptual and theoretical foundations.

Authors:  Johan Wagemans; Jacob Feldman; Sergei Gepshtein; Ruth Kimchi; James R Pomerantz; Peter A van der Helm; Cees van Leeuwen
Journal:  Psychol Bull       Date:  2012-07-30       Impact factor: 17.737

8.  Contour statistics in natural images: grouping across occlusions.

Authors:  Wilson S Geisler; Jeffrey S Perry
Journal:  Vis Neurosci       Date:  2009-02-16       Impact factor: 3.241

9.  The neural correlates of visuospatial perceptual and oculomotor extrapolation.

Authors:  Marc Tibber; Ayse Pinar Saygin; Simon Grant; Dean Melmoth; Geraint Rees; Michael Morgan
Journal:  PLoS One       Date:  2010-03-15       Impact factor: 3.240

10.  Bayesian hierarchical grouping: Perceptual grouping as mixture estimation.

Authors:  Vicky Froyen; Jacob Feldman; Manish Singh
Journal:  Psychol Rev       Date:  2015-08-31       Impact factor: 8.934

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