The Integration of Color-Selective Mechanisms in Symmetry Detection.

We studied how the visual system detects multicolor symmetric patterns by manipulating the number of colors in an image in both isoluminance and luminance conditions. With a two-interval forced choice noise masking paradigm, we presented a noise mask in both intervals of each trial. A vertically symmetric target was randomly presented in one interval while a noise control was presented in the other. The task of the observers was to determine which interval contained the target. The target detection threshold was measured at various noise mask densities, which was found to decrease 1.4- to 2.5-fold as the number of colors in the image went up at median to high noise densities across different conditions. In addition, this color facilitation effect was greater in luminance conditions than in isoluminance conditions. Our data cannot be explained by the probability summation theory or simple signal-to-noise ratio. We therefore propose a computational model that incorporates a linear chromatic symmetry register, a nonlinear transducer response, noise manipulation and a multiple channel decision making process. This model suggests that the increment of the number of colors reduces the interference to the symmetry channels produced by noise, and in turn facilitates symmetry detection.