A set of high-order spatiotemporal stimuli that elicit motion and reverse-phi percepts.

Detection of motion is a crucial component of visual processing. To probe the computations underlying motion perception, we created a new class of non-Fourier motion stimuli, characterized by their third- and fourth-order spatiotemporal correlations. As with other non-Fourier stimuli, they lack second-order correlations, and therefore their motion cannot be detected by standard Fourier mechanisms. Additionally, these stimuli lack pairwise spatiotemporal correlation of edges or flicker-and thus, also cannot be detected by extraction of one of these features, followed by standard motion analysis. Nevertheless, many of these stimuli produced apparent motion in human observers. The pattern of responses-i.e., which specific spatiotemporal correlations led to a percept of motion-was highly consistent across subjects. For many of these stimuli, inverting the overall contrast of the stimulus reversed the direction of apparent motion. This "reverse-phi" phenomenon challenges existing models, including models that correlate low-level features and gradient models. Our findings indicate that current knowledge of the computations underlying motion processing is as yet incomplete, and that understanding how high-order spatiotemporal correlations lead to motion percepts will illuminate the computations underlying early motion processing.

[1]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[2]  Peter W. McOwan,et al.  Robust velocity computation from a biologically motivated model of motion perception , 1999, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[3]  S. Anstis,et al.  Effects of Luminance and Contrast on Direction of Ambiguous Apparent Motion , 1985, Perception.

[4]  S. M. Axstis PHI MOVEMENT AS A SUBTRACTION PROCESS , 1970 .

[5]  Truong Q. Nguyen,et al.  LCD Motion Blur Reduction: A Signal Processing Approach , 2008, IEEE Transactions on Image Processing.

[6]  Mary M. Conte,et al.  Nonlinear Preprocessing in Short-range Motion , 1997, Vision Research.

[7]  W. Reichardt,et al.  Autocorrelation, a principle for the evaluation of sensory information by the central nervous system , 1961 .

[8]  B. Julesz,et al.  Visual discrimination of textures with identical third-order statistics , 1978, Biological Cybernetics.

[9]  R. Tennant Algebra , 1941, Nature.

[10]  S. Anstis,et al.  Illusory reversal of visual depth and movement during changes of contrast , 1975, Vision Research.

[11]  Mary M. Conte,et al.  Spatial organization of nonlinear interactions in form perception , 1991, Vision Research.

[12]  P. McOwan,et al.  A computational model of the analysis of some first-order and second-order motion patterns by simple and complex cells , 1992, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[13]  Z L Lu,et al.  Three-systems theory of human visual motion perception: review and update. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[14]  Edgar N. Gilbert Random Colorings of a Lattice of Squares in the Plane , 1980, SIAM J. Matrix Anal. Appl..