The discrimination of chromatic textures

Abstract

Color discrimination is influenced by chromatic distributions such as they appear on differently illuminated 3D surfaces (T. Hansen, M. Giesel, & K. R. Gegenfurtner, 2008). Here, we measured discrimination thresholds for chromatically variegated stimuli and modeled the data employing a model with multiple chromatic mechanisms. Each mechanism has a differently tuned half-wave-rectified cosine-shaped sensitivity profile centered at a different chromatic direction. To estimate thresholds, the model's responses to a test and a comparison stimulus are determined. A detection variable is calculated by taking the difference of the responses to the two stimuli and by a subsequent nonlinear combination of the responses. The model was fitted to the data presented in T. Hansen et al. (2008) and to data from two new experiments. In the first experiment, we measured discrimination thresholds for stimuli chromatically variegated along a direction orthogonal to the one used in the previous experiments. In the second experiment, we investigated the interplay between chromatic distributions and different mean contrast levels. We found that a model with eight mechanisms accounted for the effect of chromatic variation within the stimuli and provided a better fit to the discrimination thresholds than a four mechanisms model.

Reverse correlation analysis of chromatic contrast perception based on chromatic mechanism models

“…This might have appeared as the CP differences between the conditions, even if the same mechanisms process both the conditions. The idea that a common mechanisms model subserved the near and away conditions is consistent with the results of Giesel et al 22) Note that the CI experiment simulations based on our optimized models showed that the optimized model could not completely replicate the observer's responses. For example, about 0.2 distances in the isoluminant plane remained between the observer's CP and the model's CP.…”

“…Recently, the color discrimination sensitivities for colors away from an adaptation color for heterochromatic stimuli have also been investigated. 21,22) For example, Giesel et al 22) reported elongation of the discrimination threshold contours similar to those observed in Sankeralli and Mullen 20) for multicolored texture stimuli whose color distribution center was located at or away from an adaptation color. Moreover, a model with eight chromatic mechanisms provided a better fit to these elongations than a four-mechanism model or the cardinal mechanisms model.…”

“…We were not able to improve the model performance by simple manipulations of the model, such as changing the mechanism response summation to a squared sum, and inclusion of nonlinear responses of the models to chromatic saturations (data not shown). Thus, the chromatic mechanism models used in our analysis, which were similar to those employed in many previous studies, 15,16,21,22) were insufficient in explaining perceptual chromatic contrast. More sophisticated models -for example, those including complex nonlinear coding of chromatic information, chromatic mechanisms with adjustable sensitivities by attention, or chromatic mechanisms not based on adaptation colors -might better explain perceptual chromatic contrast, though we can present no strong suggestions from our experiments for such new aspects of chromatic mechanisms.…”

“…Thus, there was no significant difference between the optimization conditions, leading to the possibility that the mechanisms underlying the near and the away conditions were not different. This idea seems plausible for the following three reasons: (1) optimization based only on the away conditions were not desirable, because each signal color can be processed mainly by a single mechanism and the optimization might have been performed rather independently for each of the single colors but not for all of the signal colors; (2) the all optimization exhibited a performance similar to the other optimizations in spite of the mathematical drawback that the all optimization treated more conditions than the others; and (3) Giesel et al 22) showed that a similar single model explained threshold elevations due to noise masking for various signal chromaticities in an isoluminance plane.…”

Texture affects color emotion

“…Recent findings from neuroimaging studies suggest that the cerebral processing of form, texture, and color may be independent. 24,25 Yet, these studies provide no answer to the question how those object features interact when subjects have to respond on emotion scales.…”