The efficient coding hypothesis posits that sensory circuits and animal behavior should be adapted to the statistical structure of natural signals . Here,  we show that the perception of visual textures is adapted to the spatial distribution of light signals at a striking level of detail . We first identify a parametrized 66-dimensional space of grayscale textures defined by local spatial correlationsbetween discrete light intensities. We then devise a method of measuring the contribution of each of these textures to the spatial structure of scenes. Efficient coding predicts that the perceptual salienceof a complex sensory signal should be related to its variability in natural settings. Based on an analysis of contrast-equalized natural scenes, this theory predicts that textures involving two-point correlations will be most salient, and it further predicts the relative salienceamong second-order correlations of different types. We test our predictions by asking observers to locate a briefly-flashed texture strip in a background of white noise. The behavior of individual subjects is highly consistent: correlations beyond the second order are hard to distinguish from random noise, and detection thresholds within this salient second-order subspace are quantitatively predicted across 26 two-dimensional texture planes. We additionally find non-trivial symmetries of natural images that leave psychophysical thresholds unchanged, further supporting our hypothesis. These findings significantly extend previous results obtained for binary images, and they provide deeper insights into the relationship between natural signal statistics and human perception .
Previously, in (Hermundstad et al., 2014), we showed that when sampling is limiting, the efficient 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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The efficient coding hypothesis posits that sensory circuits and animal behavior should be adapted to the statistical structure of natural signals
Previously, in (Hermundstad et al., 2014), we showed that when sampling is limiting, the efficient coding principle leads to a "variance is salience" hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics
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using extensive new psychophysical data and image analysis,
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the perception of visual textures is adapted to the spatial distribution of light signals
this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics
R
. We first identify a parametrized
, and also identify the limits of the prediction. We define a
R
grayscale textures defined by local spatial correlationsbetween discrete light intensities. We then devise a method of measuring the contribution of each of these textures to the spatial structure of scenes. Efficient coding predicts that the perceptual salienceof a complex sensory signal should be related to its variability in natural settings. Based on an analysis of contrast-equalized natural scenes, this theory predicts that textures involving
local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The "variance is salience" hypothesis predicts that
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will be
are
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it further predicts the relative salienceamong second-order correlations of different types. We test our predictions by asking observers to locate a briefly-flashed texture strip in a background of white noise. The behavior of individual subjects is highly consistent: correlations beyond the
predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond
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hard to distinguish from random noise, and detection thresholds within this salient
not salient, and predicted thresholds for over 300
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subspace are quantitatively predicted across 26 two-dimensional texture planes. We additionally find non-trivial symmetries of natural images that leave psychophysical thresholds unchanged, further supporting our hypothesis. These findings significantly extend previous results obtained for binary images, and they provide deeper insights into the relationship between natural signal statistics and human perception
correlations match psychophysical thresholds closely (median fractional error <0.13)