Natural Scene Statistics and Perception of Grayscale Textures
Effcient coding of natural scene
statistics predicts discrimination
thresholds for grayscale textures
Tiberiu Tesileanu, Mary M. Conte, John J. Briguglio, Ann M. Hermundstad,
Jonathan D. Victor and Vijay Balasubramanian
eLife 2020;9:e54347 (2020)
Abstract
Previously, in Hermundstad et al. (2014), we showed that when sampling is limiting,
the effcient coding principle leads to a “variance is salience” hypothesis, and that this hypothesis
accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical
data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set
of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction.
We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the
relevance of each direction to natural scenes. The “variance is salience” hypothesis predicts that
two-point correlations are most salient, and predicts their relative salience. We tested these
predictions in a texture-segregation task using un-natural, synthetic textures. As predicted,
correlations beyond second order are not salient, and predicted thresholds for over 300
second-order correlations match psychophysical thresholds closely (median fractional error < 0.13).
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