Blakeslee and McCourt ((1997) Vision Research, 37, 2849-2869) demonstrated that a multiscale array of two-dimensional difference-of-Gaussian (DOG) filters provided a simple but powerful model for explaining a number of seemingly complex features of grating induction (GI), while simultaneously encompassing salient features of brightness induction in simultaneous brightness contrast (SBC), brightness assimilation and Hermann Grid stimuli. The DOG model (and isotropic contrast models in general) cannot, however, account for another important group of brightness effects which includes the White effect (White (1979) Perception, 8, 413-416) and the demonstrations of Todorovic ((1997) Perception, 26, 379-395). This paper introduces an oriented DOG (ODOG) model which differs from the DOG model in that the filters are anisotropic and their outputs are pooled nonlinearly. The ODOG model qualitatively predicts the appearance of the test patches in the White effect, the Todorovic demonstration, GI and SBC, while quantitatively predicting the relative magnitudes of these brightness effects as measured psychophysically using brightness matching. The model also accounts for both the smooth transition in test patch brightness seen in the White effect (White & White (1985) Vision Research, 25, 1331-1335) when the relative phase of the test patch is varied relative to the inducing grating, and for the spatial variation of brightness across the test patch as measured using point-by-point brightness matching. Finally, the model predicts intensive aspects of brightness induction measured in a series of Todorovic stimuli as the arms of the test crosses are lengthened (Pessoa, Baratoff, Neumann & Todorokov (1998) Investigative Ophthalmology and Visual Science, Supplement, 39, S159), but fails in one condition. Although it is concluded that higher-level perceptual grouping factors may play a role in determining brightness in this instance, in general the psychophysical results and ODOG modeling argue strongly that the induced brightness phenomena of SBC, GI, the White effect and the Todorovic demonstration, primarily reflect early-stage cortical filtering operations in the visual system.

Brightness induction includes both contrast and assimilations effects. Brightness contrast occurs when the brightness of a test region shifts away from the brightness of adjacent regions. Brightness assimilation refers to the opposite situation in which the brightness of the test region shifts toward that of the surrounding regions. Interestingly, in the White effect [Perception 8 (1979) 413] the direction of the induced brightness change does not correlate with the amount of black or white border in contact with the gray test patch. This has led some investigators to reject spatial filtering explanations not only for the White effect but for brightness perception in general. Instead, these investigators have offered explanations based on a variety of junction analyses and/or perceptual organization schemes. Here, these approaches are challenged with a critical set of new psychophysical measurements that determined the magnitude of the White effect, the shifted White effect [Perception 10 (1981) 215] and the checkerboard illusion [R.L. DeValois, K.K. DeValois, Spatial Vision, Oxford University Press, NY, 1988] as a function of inducing pattern spatial frequency and test patch height. The oriented difference-of-Gaussians (ODOG) computational model of Blakeslee and McCourt [Vision Res. 39 (1999) 4361] parsimoniously accounts for the psychophysical data, and illustrates that mechanisms based on junction analysis or perceptual inference are not required to explain them. According to the ODOG model, brightness induction results from linear spatial filtering with an incomplete basis set (the finite array of spatial filters in the human visual system). In addition, orientation selectivity of the filters and contrast normalization across orientation channels are critical for explaining some brightness effects, such as the White effect.

The experiments explore whether the mechanism(s) underlying grating induction (GI) can also account for simultaneous brightness contrast (SBC). At each of three test field heights (1, 3 and 6 deg), point-by-point brightness matches were obtained from two subjects for test field widths of 32 deg (GI condition), 14, 12, 8, 6, 3 and 1 deg. The point-by-point brightness matches were quantitatively compared, using GI condition matches as a standard, to assess systematic alterations in the structure and average magnitude of brightness and darkness induction within the test fields as a function of changing test field height and width. In the wider test fields induction structure was present and was generally well-accounted for by the GI condition sinewave predictions. As test field width decreased the sinewave amplitude of the induced structure in the test field decreased (i.e., flattened), and eventually became negative (i.e., showed a reverse cusping) at the narrower test field widths. As expected, both subjects showed a decrease in overall levels of brightness and darkness induction with increasing test field height. For any particular test field height, however, relative brightness increased with decreasing test field width. This brightness increase began at larger test field widths as test field height increased. The results are parsimoniously accounted for by the output of a weighted, octave-interval array of seven difference-of-gaussian filters. This array of filters differs from those previously employed to model various aspects of spatial vision in that it includes filters tuned to much lower spatial frequencies. The two-dimensional output of this same array of filters also accounts for the GI demonstrations of Zaidi [(1989) Vision Research, 29, 691-697], Shapley and Reid's [(1985) Proceedings of the National Academy of Sciences USA, 82, 5983-5986] contrast and assimilation demonstration, and the induced spots seen at the street intersections of the Hermann Grid. The physiological plausibility of the filter array explanation of brightness induction is discussed, along with a consideration of its relationship to other models of brightness perception.

The function of the intracellular pupil mechanism is examined by comparing the responses of photoreceptors in normal flies with those from white-eyed flies that lack the pupil. In white-eyed flies the response to an intensity increment of fixed contrast decreases at high background intensities. There is a smaller decrease in noise amplitude so that the signal: noise ratio falls. The intensity dependence of the photoreceptor signal: noise ratio fits a simple model in which activated photopigment molecules compete for 3 x 10 4 tranduction units. The signal: noise ratio decreases at high intensities because the transduction units are saturated. This model is supported by a noise analysis, which provides three estimates of the number of events generating photoreceptor responses. In white-eyed flies the event number saturates at high background intensities, suggesting that a maximum of 2 x 10 4 events can be simultaneously active. Wild-type flies do not exhibit saturation effects over the range of intensities studied. The signal: noise ratio rises with intensity to reach a stable asymptote, close to the maximum observed for white-eyed flies. Pupil attenuation is calculated from measurements of signal: noise ratio in white-eyed and wild-type flies. The pupil is progressively activated over a two log unit intensity range and when fully closed attenuates the effective intensity by 99%. The threshold of this pupil effect coincides with the threshold of pupil activation measured optically. We conclude that the intracellular pupil attenuates the light flux to prevent receptor saturation and to extend the range of intensities at which fly photoreceptors operate close to their maximum signal: noise ratio. This upper limit is determined by the number of transduction units generating a cell’s response.

Neurons in area 17 of the cat visual cortex adapt when stimulated by drifting patterns of optimal orientation, spatial frequency and temporal frequency (Ohzawa et al. 1982; Albrecht et al. 1984; Ohzawa et al. 1985). A component of this adaptation has been attributed to a contrast gain-control mechanism, rather than to neural fatigue, and results in enhanced differential sensitivity around the adapting contrast level (Ohzawa et al. 1982; Albrecht et al. 1984; Ohzawa et al. 1985). Experiments described here suggest that neural response rate, the directional selectivity of the cell, and the temporal frequency of the stimulus, are the principal determinants of adaptation, irrespective of other stimulus parameters such as contrast, velocity, or spatial frequency. The present results can nevertheless accommodate the results of previous studies of adaptation, and additionally provide scope for the resolution of apparent contradictions between results from psychophysical and neurophysiological studies of adaptation.