The model is derived as a Bayesian estimator, as in our ARVO-90 presentation, and is implemented in two stages. The first stage computes normalized spatio-temporal energy. The normalization, specified by the Bayesian estimator, is a form of automatic gain control in which each energy output is divided by an appropriate sum of energies plus a small offset (i.e., a semi-saturation constant). The second stage of the model computes a distributed representation of velocity via a linear summation of the STE outputs. The velocity estimate is given by the peak (or mean) location in the distribution. In accordance with the Bayesian approach the model incorporates a prior bias toward slower speeds, implemented by adding a small offset to two of the motion energies. Thus, the model has two parameters: the semi-saturation constant for the gain control, and the prior bias.

We show that this model is consistent with many recent psychophysical experiments on the perception of sine grating plaid velocities, including observed deviations from the intersection-of-constraints solution. For appropriate values of the two parameters, the model accounts for: 1) the data of Stone et. al. describing the effects of contrast on plaid direction; and 2) the data of Ferrera and Wilson describing the perceived speed and direction of plaids.