We measured motion-detection and motion-discrimination performance for different directions of motion, using stochastic motion sequences. Random-dot cinematograms containing 200 dots in a circular aperture were used as stimuli in a two-interval forced-choice procedure. In the motion-detection experiment, observers judged which of two intervals contained weak coherent motion, the other internal containing random motion only. In the direction-discrimination experiment, observers viewed a standard direction of motion followed by comparison motion in a slightly different direction. Observers indicated whether the comparison was clockwise or counterclockwise, relative to the standard. Twelve directions of motion were tested in the detection task and five standard directions (three cardinal directions and two oblique directions) in the discrimination task. Detection thresholds were invariant with direction of motion, but direction-discrimination thresholds were significantly higher for motion in oblique directions, even at low-coherence levels. Results from control conditions ruled out monitor artifacts and indicate that the oblique effect is relative to retinal coordinates. These results have broad implications for computational and physiological models of motion perception.

Observers briefly viewed random dots moving in a given direction and subsequently recalled that direction. When required to remember a single direction, observers performed accurately for memory intervals of up to 8 s; this high-fidelity memory for motion was maintained when observers executed a vigilance task during the memory interval. When observers tried to remember multiple directions of motion, performance deteriorated with increasing number of directions. Still, memory for multiple directions was unchanged over delays of up to 30 s. In a forced-choice experiment, observers viewed 2 successive animation sequences separated by a memory interval; for both sequences, dots moved in any direction within a limited bandwidth. Observers accurately judged which animation sequence was more coherent, even with memory intervals of 30 s. The findings are considered within the context of cognitive bias and memory for other aspects of perception.

A series of experiments investigated perceived direction of motion and depth segregation in motion transparency displays consisting of two planes of dots moving in different directions. Direction and depth judgments were obtained from human observers viewing these "bi-directional" animation sequences with and without explicit stereoscopic depth information. We found that (1) misperception of motion direction ("direction repulsion") occurs when two spatially intermingled directions of motion are within 60 deg of each other; (2) direction repulsion is minimal at cardinal directions; (3) perception of two directions of motion always results in separate motion planes segregated in depth; and (4) stereoscopic depth information has no effect on the magnitude of direction repulsion, but it does disambiguate the depth relations between motion directions. These results are developed within the context of a two-stage model of motion transparency. On this model, motion directions are registered within units subject to inhibitory interactions that cause direction repulsion, with the outputs of these units pooled within units selective for direction and disparity.

Often it is claimed that humans are particularly sensitive to biological motion. Here, sensitivity as a detection advantage for biological over nonbiological motion is examined. Previous studies comparing biological motion to nonbiological motion have not used appropriate masks or have not taken into account the underlying form present in biological motion. The studies reported here compare the detection of biological motion to nonbiological motion with and without form. Target animation sequences represented a walking human, an unstructured translation and rotation, and a structured translation and rotation. Both the number of mask dots and the size of the target varied across trials. The results show that biological motion is easier to detect than unstructured nonbiological motion but is not easier to detect than structured nonbiological motion. The results cannot be explained by learning over the course of data collection. Additional analyses show that mask density explains masking of different size target areas and is not specific to detection tasks. These data show that humans are not better at detecting biological motion compared to nonbiological motion in a mask. Any differences in detection performance between biological motion and nonbiological motion may be in part because biological motion always contains an underlying form.

Prolonged adaptation to motion in a given direction produces distinctly different visual motion aftereffects (MAEs) when viewing static vs. dynamic test displays. The dynamic MAE can be exactly simulated by real motion, whereas the static MAE cannot. In addition, the magnitude of the dynamic MAE depends on the bandwidth of motion directions experienced during adaptation, whereas the static MAE does not. Evidently a stationary pattern does not directly activate the neural mechanisms affected during motion adaptation, whereas a dynamic visual display does. These results imply that the traditional explanation of the MAE needs modification.Following inspection of motion in a given direction for a period of time, a stationary object appears temporarily to drift in the opposite direction (1); this is the well-known motion aftereffect (MAE). The MAE is a widely used inferential tool for studying the response properties of motionanalyzing mechanisms in human vision (2-4), and neurophysiologists have sought to uncover the neural concomitants of this compelling illusion (5-8). The MAE cannot be caused by transients or by retinal slip associated with eye movements, for it is observed even when the image of the test pattern is stabilized on the retina (9). Instead, the MAE is typically attributed to a temporary depression in activity within those neurons responsive to motion in the direction experienced during adaptation. When a stationary pattern is then viewed, this selective adaptation yields a shift in the balance of activity favoring neural mechanisms signaling motion in the opposite direction (10, 11). Based on two findings utilizing dynamic as well as static MAE displays, we find this explanation deficient. (i) A dynamic MAE can be simulated by real motion whereas a static MAE cannot and (ii) the magnitude of a dynamic MAE depends on the bandwidth of motion directions experienced during adaptation whereas a static MAE does not. We propose that a stationary pattern does not directly activate neural mechanisms affected during motion adaptation, whereas a dynamic visual display does. This proposal leads to a significant modification of the traditional explanation of the MAE.Can the MAE Be Simulated by Real Motion?Imagine viewing a cluster of black dots moving against a white background, with the directions of dot motions entirely random. Termed random dynamic visual noise (RDVN), this display has no net direction flow; the individual dots appear to be jittering about randomly (12). But now suppose this RDVN test display is viewed following prolonged inspection of dots all moving in the same direction, say upward. Following adaptation to upward motion, RDVN now appears temporarily to have a general downward direction of drift, even though statistically all directions are equally represented. This dynamic MAE is readily explained by the distribution-shift model (10, 11). Now, the unadapted DVN stimulus can be rendered perceptually identical to the dynamic MAE experienced during postadaptation viewing of RDVN simpl...