Search

A central puzzle in vision science is how perceptions that are routinely at odds with physical measurements of real world properties nonetheless elicit neural responses that lead to effective behaviors. Here we argue that the solution depends on: (1) rejecting the assumption that the goal of vision is to recover, however imperfectly, properties of the world; and (2) replacing it with a paradigm in which perceptions reflect biological utility based on past experience rather than objective features of the environment. Present evidence is consistent with the conclusion that conceiving vision in wholly empirical terms provides a plausible way to understand what we see and why.

Based on electrophysiological and anatomical studies, a prevalent conception is that the visual system recovers features of the world from retinal images to generate perceptions and guide behavior. This paradigm, however, is unable to explain why visual perceptions differ from physical measurements, or how behavior could routinely succeed on this basis. An alternative is that vision does not recover features of the world, but assigns perceptual qualities empirically by associating frequently occurring stimulus patterns with useful responses on the basis of survival and reproductive success. The purpose of the present article is to briefly describe this strategy of vision and the evidence for it.

The responses of visual neurons in experimental animals have been extensively characterized. To ask whether these responses are consistent with a wholly empirical concept of visual perception, we optimized simple neural networks that respond according to the cumulative frequency of occurrence of local luminance patterns in retinal images. Based on this estimation of accumulated experience, the neuron responses showed classical center-surround receptive fields, luminance gain control and contrast gain control, the key properties of early level visual neurons determined in animal experiments. These results imply that a major purpose of pre-cortical neuronal circuitry is to contend with the inherently uncertain significance of luminance values in natural stimuli.