October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Models for discriminating blur from loss of contrast
Author Affiliations
  • Joshua Solomon
    City, University of London
  • Michael Morgan
Journal of Vision October 2020, Vol.20, 521. doi:https://doi.org/10.1167/jov.20.11.521
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      Joshua Solomon, Michael Morgan; Models for discriminating blur from loss of contrast. Journal of Vision 2020;20(11):521. https://doi.org/10.1167/jov.20.11.521.

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

  • Supplements

Using chessboard-like stimuli, Morgan (2017) found that human observers aren't merely capable of discriminating between different levels of stimulus contrast and stimulus blur (i.e. a selective loss of high spatial frequencies), they can also discriminate between these two image manipulations. How they do it isn't yet clear. Wang and Simoncelli (2004) demonstrated that a code for blur is inherent to the phase relationships between localized pattern detectors of different scale. To test whether human observers actually use local phase coherence when discriminating between blur and loss of contrast, we compared phase-scrambled chessboards with unscrambled chessboards. Although both stimuli had identical amplitude spectra, local phase coherence was disrupted by phase-scrambling. Human observers were required to concurrently detect and identify (as contrast or blur) image manipulations in the 2x2 forced-choice paradigm (Nachmias & Weber, 1975; Watson & Robson, 1981) traditionally considered to be a litmus test for "labelled lines" (i.e. detection mechanisms that can be distinguished on the basis of their preferred stimuli). Phase scrambling produced a marked reduction in the ability to discriminate between blur and a reduction in contrast. Nonetheless, none of our results (including those with unscrambled chessboards) passed Watson & Robson’s most stringent test for labelled lines. Models of performance fit significantly better when either a) the blur detector also responded to contrast modulations, b) the contrast detector also responded to blur modulations, or c) noise in the two detectors was anticorrelated.


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