August 2010
Volume 10, Issue 7
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2010
Overt and Covert attention interact with curvature-based perceptual singularities
Author Affiliations
  • Liana Diesendruck
    Department of Computer Science, Ben Gurion University, Beer Sheva, Israel
    Zlotowski Center for Neuroscience, Ben Gurion University, Beer Sheva, Israel
  • Ohad Ben-Shahar
    Department of Computer Science, Ben Gurion University, Beer Sheva, Israel
    Zlotowski Center for Neuroscience, Ben Gurion University, Beer Sheva, Israel
Journal of Vision August 2010, Vol.10, 134. doi:10.1167/10.7.134
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      Liana Diesendruck, Ohad Ben-Shahar; Overt and Covert attention interact with curvature-based perceptual singularities. Journal of Vision 2010;10(7):134. doi: 10.1167/10.7.134.

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Abstract

The presence of significant contrast in a texture's dominant feature (i.e., orientation) is the foundation of many texture segregation models. Nevertheless, texture patterns consisting of slowly varying orientations were shown to preattentively segregate into perceptually coherent regions. Without an abrupt feature gradient, the striking perceptual singularities of these orientation-defined textures (ODTs) are not detected by the standard texture segregation models, and instead were recently predicted by a new theory based on multiple texture curvatures. To further investigate the role of perceptual singularities in vision, here we explore their interaction with attentional processes. Indeed, we show that perceptual singularities interact and affect both covert and overt attention similar to classical findings whereby attentional selection is influenced by strong feature gradients or higher level visual objects. A series of experiments using divided attention and saccadic programming are described and analyzed.

Diesendruck, L. Ben-Shahar, O. (2010). Overt and Covert attention interact with curvature-based perceptual singularities [Abstract]. Journal of Vision, 10(7):134, 134a, http://www.journalofvision.org/content/10/7/134, doi:10.1167/10.7.134. [CrossRef]
Footnotes
 This research was funded in part by the Psychobiology Institute and the DFG. We also thank the generous support of the Paul Ivanier center for Robotics Research, the Zlotowski Center for Neuroscience, and the Lynne and William Frankel Center for Computer Science at Ben-Gurion University.
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