August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Second-order boundaries segment more easily when density-defined rather than feature-defined
Author Affiliations & Notes
  • Christopher DiMattina
    Department of Psychology, Florida Gulf Coast University
  • Footnotes
    Acknowledgements  Funded by NIH-R15-EY032732-01 to C.D.
Journal of Vision August 2023, Vol.23, 4778. doi:
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      Christopher DiMattina; Second-order boundaries segment more easily when density-defined rather than feature-defined. Journal of Vision 2023;23(9):4778.

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

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Segmentation of second-order texture boundaries defined by regional differences in the properties of their constituent micropatterns is typically explained using Filter-Rectify-Filter (FRF) style models. The FRF model assumes that the rectified outputs of first-order linear filters which analyze local micropatterns is subsequently analyzed by a second stage of filtering which detects global differences in micropattern properties. For a texture boundary defined by two varieties of micropatterns, in the present study ones varying in orientation and contrast polarity, a version of the FRF model in which different variety-specific channels are analyzed separately by different second-stage filters makes the prediction that segmentation thresholds should be identical in two cases: (1) Boundaries with an equal number of micropatterns on each side but different proportions of each variety (feature-defined boundaries) and (2) Boundaries with different numbers of micropatterns on each side, but with each side having an identical number of each variety (density-defined boundaries). We tested the segmentation thresholds for feature-defined and density-defined second-order boundaries for textures comprised of (1) horizontal and vertical odd-phase Gabor functions or (2) positive and negative polarity DC-balanced difference-of-Gaussian (DOG) functions. We find lower segmentation thresholds in both cases for density-defined boundaries than for feature-defined boundaries. This rules out, at least for these stimuli, the simplistic version of the FRF model in which the only information available to the second-stage filters comes from first-stage filters responsive exclusively to each micropattern variety. Control experiments controlling for RMS contrast demonstrated that density-based segmentation is not a simple artifact of contrast sensitivity. We suggest that density boundary segmentation can possibly be explained by the existence of first-stage mechanisms which generalize over micropattern varieties, so that the outputs of these first-stage mechanisms can be subsequently utilized by second-stage filters.


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