August 2010
Volume 10, Issue 7
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2010
Efficiencies for estimating mean orientation, mean size, orientation variance and size variance
Author Affiliations
  • Josh Solomon
    City University London
  • Michael J. Morgan
    City University London
  • Charles Chubb
    University of California, Irvine
Journal of Vision August 2010, Vol.10, 24. doi:10.1167/10.7.24
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      Josh Solomon, Michael J. Morgan, Charles Chubb; Efficiencies for estimating mean orientation, mean size, orientation variance and size variance. Journal of Vision 2010;10(7):24. doi: 10.1167/10.7.24.

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

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Abstract

The merest glance is usually sufficient for an observer to get the gist of a scene. That is because the visual system statistically summarizes its input. We are currently exploring the precision and efficiency with which orientation and size statistics can be calculated. Previous work has established that orientation discrimination is limited by an intrinsic source of orientation-dependent noise, which is approximately Gaussian. New results indicate that size discrimination is also limited by approximately Gaussian noise, which is added to logarithmically transduced circle diameters. More preliminary results include: 1a) JAS can discriminate between two successively displayed, differently oriented Gabors, at 7 deg eccentricity, without interference from 7 iso-eccentric, randomly oriented distractors. 1b) He and another observer can discriminate between two successively displayed, differently sized circles, at 7 deg eccentricity, without much interference from 7 iso-eccentric distractors. 2a) JAS effectively uses just two of the eight uncrowded Gabors when computing their mean orientation. 2b) He and another observer use at most four of the eight uncrowded circles when computing their mean size. 3a) Mean-orientation discriminations suggest a lot more Gaussian noise than orientation-variance discriminations. This surprising result suggests that cyclic quantities like orientation may be harder to remember than non-cyclic quantities like variance. 3b) Consistent with this hypothesis is the greater similarity between noise estimates from discriminations of mean size and size variance.

Solomon, J. Morgan, M. J. Chubb, C. (2010). Efficiencies for estimating mean orientation, mean size, orientation variance and size variance [Abstract]. Journal of Vision, 10(7):24, 24a, http://www.journalofvision.org/content/10/7/24, doi:10.1167/10.7.24. [CrossRef]
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