Significance: Our eyes sense bright light using cones (L, M, and S) and recently discovered melanopsin-containing ganglion cells. Both S cones and melanopsin respond to blueish (short-wavelength) light. How does melanopsin interact with the cones in visual function? We measured the response of the human pupil to isolated stimulation of the different photoreceptors. Our work reveals a curious, opponent response to blue light in the otherwise familiar pupillary light response. Increased stimulation of S cones can cause the pupil to dilate, but this effect is usually masked by a stronger and opposite response from melanopsin-containing cells. Our results have clinical importance because the sensing of blue light is known to be related to seasonal depression, sleep, and pain from bright light.

The illumination of the environment undergoes both intensity and spectral changes during the 24 h cycle of a day. Daylight spectral power distributions are well described by low-dimensional models such as the CIE (Commission Internationale de l’Éclairage) daylight model, but the performance of this model in non-daylight regimes is not characterised. We measured downwelling spectral irradiance across multiple days in two locations in North America: One rural location (Cherry Springs State Park, PA) with minimal anthropogenic light sources, and one city location (Philadelphia, PA). We characterise the spectral, intensity and colour changes and extend the existing CIE model for daylight to capture twilight components and the spectrum of the night sky.

Vision is difficult because images are ambiguous about the structure of the world. For object color, the ambiguity arises because the same object reflects a different spectrum to the eye under different illuminations. Human vision typically does a good job of resolving this ambiguity—an ability known as color constancy. The past 20 years have seen an explosion of work on color constancy, with advances in both experimental methods and computational algorithms. Here, we connect these two lines of research by developing a quantitative model of human color constancy. The model includes an explicit link between psychophysical data and illuminant estimates obtained via a Bayesian algorithm. The model is fit to the data through a parameterization of the prior distribution of illuminant spectral properties. The fit to the data is good, and the derived prior provides a succinct description of human performance.

Color, lightness, and glossiness are perceptual attributes associated with object reflectance. For these perceptual representations to be useful, they must correlate with physical reflectance properties of objects and not be overly affected by changes in illumination or viewing context. We employed a matching paradigm to investigate the perception of lightness and glossiness under geometric changes in illumination. Stimuli were computer simulations of spheres presented on a high-dynamic-range display. Observers adjusted the diffuse and specular reflectance components of a test sphere so that its appearance matched that of a reference sphere simulated under a different light field. Diffuse component matches were close to veridical across geometric changes in light field. In contrast, specular component matches were affected by geometric changes in light field. We tested several independence principles and found (i) that the effect of changing light field geometry on the diffuse component matches was independent of the reference sphere specular component; (ii) that the effect of changing light field geometry on the specular component matches was independent of the reference sphere diffuse component; and (iii) that diffuse and specular components of the match depended only slightly on the roughness of the specular component. Finally, we found that equating simple statistics (i.e., standard deviation, skewness, and kurtosis) computed from the luminance histograms of the spheres did not predict the matches: these statistics differed substantially between spheres that matched in appearance across geometric changes in the light field.

Vision provides information about the properties and identity of objects. The ease with which we perceive object properties belies the difficulty of the underlying information-processing task. In the case of object color, retinal information about object reflectance is confounded with information about the illumination as well as about the object's shape and pose. There is no obvious rule that allows transformation of the retinal image to a color representation that depends primarily on object surface reflectance. Under many circumstances, however, object color appearance is remarkably stable across scenes in which the object is viewed. Here, we review a line of experiments and theory that aim to understand how the visual system stabilizes object color appearance. Our emphasis is on models derived from explicit analysis of the computational problem of estimating the physical properties of illuminants and surfaces from the retinal image, and experiments that test these models. We argue that this approach has considerable promise for allowing generalization from simplified laboratory experiments to richer scenes that more closely approximate natural viewing. We discuss the relation between the work we review and other theoretical approaches available in the literature.