Motion Trajectories

Humans are very good at detecting motion along a smooth path. Vilayanur Ramachandran and Stuart Anstis showed that the direction of motion of an ambiguous motion stimulus (e.g., one that is equally likely to be perceived to move in two different directions) is resolved when it is embedded in a motion sequence that moves in a consistent direction. This phenomenon, called motion inertia, refers to the tendency to see motion along a consistent path, despite a brief motion ambiguity. Andrea van Doorn, Jan Koenderink, and coworkers showed that it is much easier to detect coherent motion embedded in incoherent motion when the stimulus is elongated in the direction of motion rather than perpendicular to it. Similarly, Scott Watamaniuk and coworkers showed that a single signal dot moving along a straight path is easily detected among noise dots moving in random directions, despite the fact that in single frames the signal dot and all the noise dots move an equal distance. A quantitative investigation of this effect showed that for extended trajectories (lasting 200 msec), human performance far exceeds the combined outputs of local motion units acting independently. This discrepancy occurs only for long trajectories. For brief trajectories (lasting 100 msec), human performance is predicted by local units with circular profiles. One explanation for this finding is that sensitivity to extended motion trajectories is due to specialized motion units that are elongated in the direction of motion. This explanation has been ruled out by experiments that show that motion signals along any smooth motion path are easily detected, whether that path is linear, circular, or a triangular wave. It has been suggested that the detection of extended trajectories is mediated by a flexible network of local units that propagate activation among units tuned to similar directions of motion. A unit propagates activation to another unit if its preferred direction roughly points to that unit, thus facilitating the detection of motion signals along a smooth path. This interaction would have to be nonlinear to be consistent with the observed data. We have preliminary evidence that the signal that is propagated along the motion path is a measure of the probability of future motion in a particular direction, given the history of past motion.

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