A model for motion coherence and transparency.

Abstract

A recent model for two-dimensional motion processing in MT has demonstrated that perceived direction can be accurately predicted by combining Fourier and non-Fourier component motion signals using a vector sum computation. The vector sum direction is computed by a neural network that weights Fourier and non-Fourier components by the cosine of the component direction relative to that of each pattern unit, after which competitive inhibition extracts the signals of the most active units. It is shown here that a minor modification of the connectivity in this network suffices to predict transitions from motion coherence to transparency under a wide range of circumstances. It is only necessary that the cosine weighting function and competitive inhibition be limited to directions within +/- 120 deg of each pattern unit's preferred direction. This network responds by signaling one pattern direction for coherent motion but two distinct directions for transparent motion. Based on this, neural networks with properties of MT and MST neurons can automatically signal motion coherence or transparency. In addition, the model accurately predicts motion repulsion under transparency conditions.