Advances in our understanding of natural image statistics and of gain control within the retinal circuitry are leading to new insights into the classic problem of retinal light adaptation. Here we review what we know about how rapid adaptation occurs during active exploration of the visual scene. Adaptational mechanisms must balance the competing demands of adapting quickly, locally, and reliably, and this balance must be maintained as lighting conditions change. Multiple adaptational mechanisms in different locations within the retina act in concert to accomplish this task, with lighting conditions dictating which mechanisms dominate.

Recent theories of lightness perception assume that lightness (perceived reflectance) is computed by a process that contrasts the target's luminance with that of one or more regions in its spatial surround. A challenge for any such theory is the phenomenon of lightness assimilation, which occurs when increasing the luminance of a surround region increases the target lightness: the opposite of contrast. Here contrast and assimilation are studied quantitatively in lightness matching experiments utilizing concentric disk-and-ring displays. Whether contrast or assimilation is seen depends on a number of factors including: the luminance relations of the target, surround, and background; surround size; and matching instructions. When assimilation occurs, it is always part of a larger pattern in which assimilation and contrast both occur over different ranges of surround luminance. These findings are quantitatively modeled by a theory that assumes lightness is computed from a weighted sum of responses of edge detector neurons in visual cortex. The magnitude of the neural response to an edge is regulated by a combination of contrast gain control acting between neighboring edge detectors and a top-down attentional gain control that selectively weights the response to stimulus edges according to their task relevance.

Edge integration refers to a hypothetical process by which the visual system combines information about the local contrast, or luminance ratios, at luminance borders within an image to compute a scale of relative reflectances for the regions between the borders. The results of three achromatic color matching experiments, in which a test and matching ring were surrounded by one or more rings of varying luminance, were analyzed in terms of three alternative quantitative edge integration models: (1) a generalized Retinex algorithm, in which achromatic color is computed from a weighted sum of log luminance ratios, with weights free to vary as a function of distance from the test (Weighted Log Luminance Ratio model); (2) an elaboration of the first model, in which the weights given to distant edges are reduced by a percentage that depends on the log luminance ratios of borders lying between the distant edges and the target (Weighted Log Luminance Ratio model with Blockage); and (3) an alternative modification of the first model, in which Michelson contrasts are substituted for log luminance ratios in the achromatic color computation (Weighted Michelson Contrast model). The experimental results support the Weighted Log Luminance Ratio model over the other two edge integration models. The Weighted Log Luminance Ratio model is also shown to provide a better fit to the achromatic color matching data than does Wallach's Ratio Rule, which states that the two disks will match in achromatic color when their respective disk/ring luminance ratios are equal.