The structure of our model is shown in
Figure 8. First, motion detectors are activated by the rightward and leftward moving dots. The selectivity of the motion detectors results in a “common fate” separation of the dots: Dots moving in one direction are processed by one population of MT neurons while dots moving in another direction are processed by a different population of MT neurons (
DeYoe & Van Essen, 1988;
Poggio, Gonzalez, & Krause, 1988;
Livingstone & Hubel, 1987;
Zeki, 1974). In the next stage, the sinusoidal speed profiles lead to a depth curvature assignment (i.e., convex or concave half cylinders) (
Fernandez, Watson, & Qian, 2002). Because the depth curvature assignment occurs for the leftward and rightward moving dots independently, all four permutations of the pairs (left, right) (convex, concave) are equally possible (the effect of differences in the prior probabilities of convex and concave depth curvature assignment will be addressed in the “Discussion”). For most natural stimuli, depth cues, such as disparity, perspective, and so on, determine the polarity of the depth curvature (convex or concave). Because none of these cues is present in a standard SFM stimulus, the depth curvature polarity is ambiguous. Under these conditions, the assigned depth curvature will be the result of signal noise and therefore change stochastically (
Merk & Schnakenberg, 2002).
Figure 9a shows the temporal dynamics of this initial depth curvature assignment stage. If we assume that the highest frequency of percept changes gives us the rate at which the depth curvature assignment is updated, then the addition of a temporal integration over an appropriate time window is needed so the model can produce the longest percept durations (which were found to go up to 5 s). The output of the temporal integration stage simply corresponds to the curvature that, during the period inside the integration window, has been assigned more often to the leftward/rightward moving dots. For stimuli with unambiguous depth cues, the temporal integration ensures shape constancy by filtering out signal noise.
Figure 9b illustrates the temporal integration by showing the difference in the number of times that convex or concave curvatures have been assigned (# convex - # concave) as function of time.
Figure 9c shows the resulting output, and
Figure 9d shows the temporal dynamics of the corresponding percepts.