Sensitivity to grayscale textures is adapted to natural scene statistics

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 correlations between 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 salience of 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 salience among 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.