What is noise for the motion system?

Motion coherence thresholds in random-dot patterns have been widely adopted as a measure of performance in visual motion processing. However, there has been diversity in the type of "noise" in which a coherent motion signal has to be detected. Here we compare coherence thresholds for three ways of creating motion noise: dots replotted in random positions in each new frame; dots with a set displacement but following a random walk from frame to frame; or dots moving in random directions which remain constant for a given dot over a sequence of displacements. In each case, the signal dots may either remain the same throughout the display sequence, or the signal dots may be re-selected afresh on each frame ("different"). With our display (3 deg square, 120 msec exposure, velocity = 5 or 10 deg sec-1), all these different noise conditions yielded similar thresholds around 5-8%. There were some small but systematic differences between conditions. Thresholds in random-direction displays were consistently higher than those in random-walk or random-position displays, especially at the lower velocity. However, this effect is much smaller than would be expected from the increased standard error of the noise mean in random direction, perhaps because the motion system integrates information most effectively over a local region of space and/or time. Subjects" performance could not be explained by a strategy of identifying individual signal dots with extended trajectories. The similarity between random-walk and random-position thresholds implies that subjects do not exploit the marked differences in speed distribution between signal and noise dots in the latter case. The practical message for the design and interpretation of experiments using coherence thresholds is that the results are not much affected by the choice of noise, at least within the range of stimuli tested here

Integration across directions in dynamic random dot displays: vector summation or winner take all?

Recent studies have clearly demonstrated that the activity of directionally selective neuronal populations in the middle temporal (MT) and medial superior temporal (MST) cortical areas plays a direct role in the judgment of the direction of visual motion. However, the way in which the information is derived from a population of neurons remains unknown. Two principal models have been suggested in the past: the vector summation model suggests that the responses of neurons encoding all directions of motion are weighted and pooled to obtained an accurate estimate of the mean direction of motion; the winner-take-all model is based on a competition between different direction-specific channels, so that decisions are cast in favor of the channel generating the strongest directional signal. To discriminate between these two models we generated random dot stimuli that contained an asymmetric distribution of directions of motion. Human subjects were asked to adjust the global direction of motion to the upward vertical direction. When the directional signals were of similar strength, subjects tended to perceive global motion in the mean direction of motion (corresponding to vector summation), but as one directional signal became more prominent, most subjects' settings diverged from the mean towards the modal direction of motion. Some subjects could either match the mean or the modal direction of motion in the display, depending on the task instructions. These results suggest that the perceptual judgment of direction of motion is not based on any rigid algorithm generating a single valued output. Rather, human observers are able to judge different aspects of the distribution of activity in a cortical area depending on the task requirements