In what ways is mammalian vision—and in particular, human vision—efficiently adapted to its ecology? We suggest that human visual artwork, which is made for the human eye, holds clues that could help answer this question. Paintings are readily perceived as representations of natural objects and scenes, yet statistical relationships between natural images and paintings are nontrivial. Although spatial frequency content is generally similar for art and natural images, paintings cannot reproduce the dynamic range of luminance in scenes. Through a variety of image manipulations designed to alter image intensity distributions and spatial contrast, we here investigate the notion that artists’ representational strategies can efficiently capture salient features of natural images, and in particular, of faces. We report that humans perform near flawless discrimination of faces and nonfaces in both paintings and natural images, even for stimulus presentation durations of 12 ms. In addition, contrast negation and up-down inversion have minimal to no effect on performance for both image types, whereas 1/f noise addition significantly affects discrimination performance for art more than for natural images. Together, these results suggest artists create representations that are highly efficient for transmitting perceptual information to the human brain.

Form vision is traditionally regarded as processing primarily achromatic information. Previous investigations into the statistics of color and luminance in natural scenes have claimed that luminance and chromatic edges are not independent of each other and that any chromatic edge most likely occurs together with a luminance edge of similar strength. Here we computed the joint statistics of luminance and chromatic edges in over 700 calibrated color images from natural scenes. We found that isoluminant edges exist in natural scenes and were not rarer than pure luminance edges. Most edges combined luminance and chromatic information but to varying degrees such that luminance and chromatic edges were statistically independent of each other. Independence increased along successive stages of visual processing from cones via postreceptoral color-opponent channels to edges. The results show that chromatic edge contrast is an independent source of information that can be linearly combined with other cues for the proper segmentation of objects in natural and artificial vision systems. Color vision may have evolved in response to the natural scene statistics to gain access to this independent information.