September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
The texture centroid paradigm: A new method for isolating preattentive visual mechanisms
Author Affiliations
  • Charles Chubb
    Department of Cognitive Sciences & Institute for Mathematical Behavioral Sciences, UC Irvine
  • Michael Landy
    Department of Psychology, New York University Center for Neural Science, New York University
  • Zack Westrick
    Department of Psychology, New York University
  • Eero Simoncelli
    Howard Hughes Medical Institute Center for Neural Science, New York University
Journal of Vision September 2015, Vol.15, 775. doi:10.1167/15.12.775
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      Charles Chubb, Michael Landy, Zack Westrick, Eero Simoncelli; The texture centroid paradigm: A new method for isolating preattentive visual mechanisms. Journal of Vision 2015;15(12):775. doi: 10.1167/15.12.775.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

What are the basic image attributes sensed by human vision? This fundamental question has proved difficult to answer experimentally. We introduce a novel psychophysical method that provides leverage for addressing this question in the context of visual texture perception. On each trial, the participant sees a brief display comprising a randomly positioned set of circular apertures, each filled with texture. Some apertures contain a “distractor texture” D; others contain “target texture” T. The task of the participant is to mouse-click the centroid of the set of T-apertures, while ignoring the D-apertures. Suppose the participant performs this task using a separable linear computation: (1) computing a set of neural images corresponding to preattentive mechanisms, Mk; (2) combining these images into a weighted average image S (with nonnegative weights wk); and (3) extracting the centroid of the resulting image. An ideal observer, that aims to minimize the Euclidean distance of the response from the target centroid, should choose the wk to maximize ST/SD, where ST and SD are the weighted averages of mechanism responses to textures T and D, respectively. This ratio is maximized by assigning all the weight to the single mechanism Mk for which Mk(T)/Mk(D) is largest. Thus, if a participant performs as well as possible in the centroid task, the resulting behavior reflects use of a single mechanism. We apply this method to white noise textures, varying the distributions of grayscale pixel values characterizing D and T in different conditions. Results implicate (1) a ‘‘blackshot’’ mechanism, sharply tuned to the blackest pixels; (2) a ‘‘dark-gray” mechanism with maximal sensitivity for pixels between black and mid-gray, (3) a ‘‘down-ramped’’ mechanism whose sensitivity is maximal for black and decreases quasi-linearly with luminance, and (4) a complementary “up-ramped’’ mechanism whose sensitivity increases linearly with luminance, with maximum sensitivity to white.

Meeting abstract presented at VSS 2015

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