Nonlinear cortical encoding of color predicts enhanced McCollough effects in anomalous trichromats.

Nonlinear encoding of chromatic contrast by the early visual cortex predicts that anomalous trichromats will show a larger McCollough effect than normal trichromats. In Experiment 1 we employed the McCollough effect to probe the cortical representation of saturation in normal trichromats, and used the results to predict enhanced McCollough effects for anomalous trichromats, which we measured in Experiment 2. In Experiment 1 three participants adapted to red and green orthogonal gratings of four different saturations. Using nulling to measure aftereffect strength, we found that halving the saturation of the inducing gratings decreased aftereffect strength only slightly, consistent with a compressive coding of saturation in early visual cortex. In anomalous trichromats, cone contrasts between red and green are greatly decreased from those of normal trichromats, but induced aftereffects are only slightly decreased, because of the non-linearity in the cortical encoding of saturation. To null the aftereffect, however, the retinal color deficiency must be overcome by adding more color to the null than required by normal trichromats. We confirmed this prediction in Experiment 2 where four anomalous trichromats required nulling stimuli approximately four times more saturated than did normal trichromats. We consider two competing models to explain our results: in a 'pigment swap' model anomalous trichromats have an altered photopigment but process color postreceptorally in the same way as normal trichromats; in a 'postreceptoral compensation' model the cortical representation of red-green contrasts is amplified to compensate for reduced cone contrasts. The latter provided a better fit to our data.

Modeling, Prediction, and Reduction of 3D Crosstalk in Circular Polarized Stereoscopic LCDs.

Crosstalk, which is the incomplete separation between the left and right views in 3D displays, induces ghosting and causes difficulty of the eyes to fuse the stereo image for depth perception. Circularly polarized (CP) liquid crystal display (LCD) is one of the main-stream consumer 3D displays with the prospering of 3D movies and gamings. The polarizing system including the patterned retarder is one of the major causes of crosstalk in CP LCD. The contributions of this paper are the modeling of the polarizing system of CP LCD, and a crosstalk reduction method that efficiently cancels crosstalk and preserves image contrast. For the modeling, the practical orientation of the polarized glasses (PG) is considered. In addition, this paper calculates the rotation of the light-propagation coordinate for the Stokes vector as light propagates from LCD to PG, and this calculation is missing in the previous works when applying Mueller calculus. The proposed crosstalk reduction method is formulated as a linear programming problem, which can be easily solved. In addition, we propose excluding the highly textured areas in the input images to further preserve image contrast in crosstalk reduction.

Depth and luminance edges attract.

The spatial resolution of disparity perception is poor compared to luminance perception, yet we do not notice that depth edges are more blurry than luminance edges. Is this because the two cues are combined by the visual system? Subjects judged the locations of depth-defined or luminance-defined edges, which were separated by up to 5.6 min of arc. The perceived edge location was a function of the depth-defined edge and the luminance-defined edge, with the luminance edge tending to play a larger role. Our data are compatible with but not completely explained by an optimal cue-combination model that gives more reliable cues a heavier weight. Both edge cues (depth and luminance) contribute to the final percept, with an adaptive weighting depending on the task and the acuity with which each cue is perceived.

Dynamic brightness induction causes flicker adaptation, but only along the edges: evidence against the neural filling-in of brightness.

Is brightness represented in a point-for-point neural map that is filled in from the response of small, contrast-sensitive edge detector cells? We tested for the presence of this filled-in map by adapting to illusory flicker caused by a dynamic brightness-induction stimulus. Thereafter flicker sensitivity was reduced when our test region was the same size as the induced region, but not for smaller, inset regions. This suggests induced brightness is represented by either small edge-selective cells with no filling-in stage, or by contrast-sensitive spatial filters at many different scales, but not by a population of filled-in neurons arranged in a point-for-point map.

Spatial properties of flicker adaptation.

Prolonged viewing of a flickering region reduces sensitivity to a subsequently flickered test patch of identical extent, but the spatial properties of this adaptation are unknown. What happens to the sensitivity to a smaller flickered test patch completely contained in, but inset from, the adapted region? We show that sensitivity to the inset test patch is only slightly affected by adaptation of the larger region. This suggests that neurons that respond to the edges of the smaller test patch are not adapted by the larger flickering region. We then show that an annulus adapter designed specifically to adapt only those edges only slightly reduces sensitivity, demonstrating that neurons that do not adapt to the flickered edges are also involved in detecting flicker. This gives further evidence that flicker detection depends on at least two mechanisms - one sensitive to flickering edges and one sensitive to local flicker, and shows that these mechanisms can operate in isolation.

Brief presentations reveal the temporal dynamics of brightness induction and White's illusion.

We measured the timecourse of brightness processing by briefly presenting brightness illusions and then masking them. Brightness induction (brightness contrast) was visible when presented for only 58 ms, was stronger at short presentation times, and its visibility did not depend on spatial frequency. We also found that White's illusion was visible at 82 ms. Together, these results suggest that (1) brightness perception depends on the surrounding context, even at very short presentation times, (2) the initial brightness percept is generated very quickly, but additional exposure can modulate it, and (3) the temporal dynamics are not dependent on a slow filling-in process.

Explaining brightness illusions using spatial filtering and local response normalization.

We introduce two new low-level computational models of brightness perception that account for a wide range of brightness illusions, including many variations on White's Effect [Perception, 8, 1979, 413]. Our models extend Blakeslee and McCourt's ODOG model [Vision Research, 39, 1999, 4361], which combines multiscale oriented difference-of-Gaussian filters and response normalization. We extend the response normalization to be more neurally plausible by constraining normalization to nearby receptive fields (models 1 and 2) and spatial frequencies (model 2), and show that both of these changes increase the effectiveness of the models at predicting brightness illusions.