October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Equivalent noise characterization of human lightness constancy
Author Affiliations & Notes
  • Vijay Singh
    North Carolina Agricultural and Technical State University
  • Johannes Burge
    University of Pennsylvania
  • David Brainard
    University of Pennsylvania
  • Footnotes
    Acknowledgements  NIH EY10016 (DHB), NIH R01-EY028571 (JB)
Journal of Vision October 2020, Vol.20, 610. doi:https://doi.org/10.1167/jov.20.11.610
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      Vijay Singh, Johannes Burge, David Brainard; Equivalent noise characterization of human lightness constancy. Journal of Vision 2020;20(11):610. https://doi.org/10.1167/jov.20.11.610.

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

  • Supplements

An important goal for vision is to provide stable perceptual representations of task-relevant scene properties (e.g. target object size, shape, reflectance) despite variation in task-irrelevant scene properties (e.g. illumination, reflectance of other nearby objects). To study such stability, we measured how variation in a task-irrelevant scene property affects threshold for discriminating changes in a task-relevant property. Four subjects viewed computer-rendered images of a 1-degree sphere, within a 2-degree scene containing naturalistic background objects. The sphere’s reflectance was spectrally flat but varied in albedo. On each trial, two images of the scene were presented in sequence and subjects indicated which 0.25s interval contained the sphere with higher albedo. Across intervals, the reflectances of the background objects were randomized by sampling from a probabilistic model of naturally occurring surface reflectances. This reflectance distribution was varied systematically by applying a scalar to its covariance matrix. Discrimination thresholds were measured as a function of the scalar. When plotted as a function of log covariance scalar, log squared thresholds were initially constant, and then rose approximately linearly with a slope of 0.20 +/- 0.03. The equivalent noise, the log covariance scalar value at which threshold elevation began, was -2.08 +/- 0.21. We compared the data to predictions of a recently published computational model of lightness constancy. Model thresholds were aligned with human thresholds at covariance scalar equal to zero by adding noise to the computational observer estimates. The model predicted human equivalent noise to reasonable approximation (model value, -2.44), but model thresholds increased more rapidly than those of the subjects (model slope, 0.38). Our experiment characterizes the intrusion that background variability has on perceived object lightness. Our computational model accounts reasonably for the equivalent noise, but is challenged by the low slope of threshold rise shown by human subjects.


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