Perceived Depth in Natural Images Reflects Encoding of Low-Level Luminance Statistics

Sighted animals must survive in an environment that is diverse yet highly structured. Neural-coding models predict that the visual system should allocate its computational resources to exploit regularities in the environment, and that this allocation should facilitate perceptual judgments. Here we use three approaches (natural scenes statistical analysis, a reanalysis of single-unit data from alert behaving macaque, and a behavioral experiment in humans) to address the question of how the visual system maximizes behavioral success by taking advantage of low-level regularities in the environment. An analysis of natural scene statistics reveals that the probability distributions for light increments and decrements are biased in a way that could be exploited by the visual system to estimate depth from relative luminance. A reanalysis of neurophysiology data from Samonds et al. (2012) shows that the previously reported joint tuning of V1 cells for relative luminance and binocular disparity is well matched to a predicted distribution of binocular disparities produced by natural scenes. Finally, we show that a percept of added depth can be elicited in images by exaggerating the correlation between luminance and depth. Together, the results from these three approaches provide further evidence that the visual system allocates its processing resources in a way that is driven by the statistics of the natural environment