Motion transparency: making models of motion perception transparent

In daily life our visual system is bombarded with motion information. We see cars driving by, flocks of birds flying in the sky, clouds passing behind trees that are dancing in the wind. Vision science has a good understanding of the first stage of visual motion processing, that is, the mechanism underlying the detection of local motions. Currently, research is focused on the processes that occur beyond the first stage. At this level, local motions have to be integrated to form objects, define the boundaries between them, construct surfaces and so on. An interesting, if complicated case is known as motion transparency: the situation in which two overlapping surfaces move transparently over each other. In that case two motions have to be assigned to the same retinal location. Several researchers have tried to solve this problem from a computational point of view, using physiological and psychophysical results as a guideline. We will discuss two models: one uses the traditional idea known as ‘filter selection’ and the other a relatively new approach based on Bayesian inference. Predictions from these models are compared with our own visual behaviour and that of the neural substrates that are presumed to underlie these perceptions.