Rudd and Zemach analyzed brightness/lightness matches performed with disk/annulus stimuli under four contrast polarity conditions, in which the disk was either a luminance increment or decrement with respect to the annulus, and the annulus was either an increment or decrement with respect to the background. In all four cases, the disk brightness—measured by the luminance of a matching disk—exhibited a parabolic dependence on the annulus luminance when plotted on a log-log scale. Rudd further showed that the shape of this parabolic relationship can be influenced by instructions to match the disk’s brightness (perceived luminance), brightness contrast (perceived disk/annulus luminance ratio), or lightness (perceived reflectance) under different assumptions about the illumination. Here, I compare the results of those experiments to results of other, recent, experiments in which the match disk used to measure the target disk appearance was not surrounded by an annulus. I model the entire body of data with a neural model involving edge integration and contrast gain control in which top-down influences controlling the weights given to edges in the edge integration process act either before or after the contrast gain control stage of the model, depending on the stimulus configuration and the observer’s assumptions about the nature of the illumination.
Computer simulations of an extended version of a neural model of lightness perception [1,2] are presented. The model provides a unitary account of several key aspects of spatial lightness phenomenology, including contrast and assimilation, and asymmetries in the strengths of lightness and darkness induction. It does this by invoking mechanisms that have also been shown to account for the overall magnitude of dynamic range compression in experiments involving lightness matches made to real-world surfaces [2]. The model assumptions are derived partly from parametric measurements of visual responses of ON and OFF cells responses in the lateral geniculate nucleus of the macaque monkey [3,4] and partly from human quantitative psychophysical measurements. The model’s computations and architecture are consistent with the properties of human visual neurophysiology as they are currently understood. The neural model's predictions and behavior are contrasted though the simulations with those of other lightness models, including Retinex theory [5] and the lightness filling-in models of Grossberg and his colleagues [6].