To some degree, all current models of visual motion-perception mechanisms depend on the power of the visual signal in various spatiotemporal-frequency bands. Here we show how to construct counterexamples: visual stimuli that are consistently perceived as obviously moving in a fixed direction yet for which Fourier-domain power analysis yields no systematic motion components in any given direction. We provide a general theoretical framework for investigating non-Fourier motion-perception mechanisms; central are the concepts of drift-balanced and microbalanced random stimuli. A random stimulus S is drift balanced if its expected power in the frequency domain is symmetric with respect to temporal frequency, that is, if the expected power in S of every drifting sinusoidal component is equal to the expected power of the sinusoid of the same spatial frequency, drifting at the same rate in the opposite direction. Additionally, S is microbalanced if the result WS of windowing S by any space-time-separable function W is drift balanced. We prove that (i) any space-time-separable random (or nonrandom) stimulus is microbalanced; (ii) any linear combination of pairwise independent microbalanced (respectively, drift-balanced) random stimuli is microbalanced and drift balanced if the expectation of each component is uniformly zero; (iii) the convolution of independent microbalanced and drift-balanced random stimuli is microbalanced and drift balanced; (iv) the product of independent microbalanced random stimuli is microbalanced; and (v) the expected response of any Reichardt detector to any microbalanced random stimulus is zero at every instant in time. Examples are provided of classes of microbalanced random stimuli that display consistent and compelling motion in one direction. All the results and examples from the domain of motion perception are transposable to the space-domain problem of detecting orientation in a texture pattern.

Purpose. We studied the perceptual segregation of texture pairs consisting of a grid of uniform, square texture elements, differing only in the distribution of intensities across those elements. If two textures differ in mean intensity ("lightness") or in the variance of intensity ("contrast"), then they are easily segregated. Chubb, Econopouly & Landy (JOSA A, 11, 2350, 1994) demonstrated the existence of a 3rd mechanism B (in addition to mechanisms L and C, coding lightness and contrast respectively) that was solely responsible for the segregation of textures equated for mean and contrast. Here, we determine the sensitivity of B to textures differing in mean or variance, thus fully specifying its (highly nonlinear) contrast response function. Method. Two textures have gray-level histograms H1 and H2 chosen so as to equate both mean and variance so that B alone discriminates them. The magnitude of the difference D=H1-H2 was varied to find a segregation threshold t*D. Then, D was perturbed (e.g., D'=t*D+P) so that the two textures differed in mean or variance, and segregation performance was assessed. Results. Differences in texture mean and in texture variance traded off linearly with changes in D. This implies that even when the textures differ slightly in mean or variance, it is still B alone that discriminates between them. The slopes of the lines relating changes in mean and variance to changes in the amplitude of D reflect the sensitivity of B to texture mean and variance. Combining these findings with our previous results reveals that B is (i) highly sensitive to texture elements of the lowest contrast (near -1), and (ii) has a response that saturates by a contrast of -3/4 (dark gray). That is, this "blackshot mechanism" discriminates textures by comparing the number of the blackest pixels in each.

The finding that an item of type A pops out from an array of distractors of type B typically is taken to support the inference that human vision contains a neural mechanism that is activated by items of type A but not by items of type B. Such a mechanism might be expected to yield a neural image in which items of type A produce high activation and items of type B low (or zero) activation. Access to such a neural image might further be expected to enable accurate estimation of the centroid of an ensemble of items of type A intermixed with to-be-ignored items of type B. Here, it is shown that as the number of items in stimulus displays is increased, performance in estimating the centroids of horizontal (vertical) items amid vertical (horizontal) distractors degrades much more quickly and dramatically than does performance in estimating the centroids of white (black) items among black (white) distractors. Together with previous findings, these results suggest that, although human vision does possess bottom-up neural mechanisms sensitive to abrupt local changes in bar-orientation, and although human vision does possess and utilize top-down global attention filters capable of selecting multiple items of one brightness or of one color from among others, it cannot use a top-down global attention filter capable of selecting multiple bars of a given absolute orientation and filtering bars of the opposite orientation in a centroid task.

Purpose: Milner & Goodale, 1995, propose that visual processing splits into a ventral, "what" stream devoted to conscious recognition processes and a dorsal, "how" stream mediating motor control. Whereas the ventral stream receives input from both the magnocellular and parvocellular pathways, the dorsal stream receives only magnocellular input, suggesting that the "how" system may lack chromatic sensitivity. Here we assess the relative sensitivities to luminance versus chromatic variations in distinct tasks designed to selectively engage these two processing streams. Method: Taskwhat required the subject to register with a buttonpress the orientation (right, left, up or down) of a foveal isoceles triangle, briefly flashed, then masked. Taskhow required the subject to touch with a stylus the tip of the most acute angle of an isoceles triangle presented randomly in one of 4 orientations at one of 4 parafoveal locations. Each task used (i) equiluminant green targets varying in saturation, and (ii) isochromatic gray targets varying in luminance. Psychometric functions of green-target saturation and of gray-target luminance were obtained in Taskwhat. In Taskhow we measured movement duration and endpoint accuracy. Results: For saturation Green(p) and luminance Gray(p) yielding identical success rate p in Taskwhat, Green(p) targets yielded movement trajectories in Taskhow that were significantly slower and/or less accurate than Gray(p) targets. Conclusions: The process mediating performance in Taskwhat is relatively more sensitive to chromatic (vs. luminance) variations than the process mediating performance of Taskhow, supporting the dual claims that (i) Taskwhat and Taskhow selectively activate the "what" and "how" systems, and (ii) the "what" system is relatively more sensitive to chromatic variations than the " how" system.