Taking the energy out of spatio-temporal energy models of human motion processing: the Component Level Feature Model

Abstract

Standard biologically inspired spatio-temporal energy models of how humans perceive moving two-dimensional patterns often have two critical stages. In the first stage, suitable filters are convolved with the pattern over time to extract information at the "component" level. Motion energy is then computed for each component. The second stage typically computes pattern velocity using the intersection of constraints rule (IOC). This paper describes a new implementation of the Component Level Feature Model (Bowns, 2002) that computes motion direction that is similar to these two stages except that it does not compute motion energy. Here the model computes direction for 200 randomly generated plaids. The output linearly matched that predicted by the IOC. The model was also able to predict the perceived direction even when it deviated from the IOC due to the following variables - speed ratio (Bowns, 1996); duration (Yo & Wilson, 1992); adaptation (Bowns & Alais, 2006). The model provides a novel explanation for each of the above and for why multiple directions can be represented for the same stimuli (Bowns & Alais, 2006); and why some second-order information attributed to non-linearities (Derrington, Badcock, & Holroyd, 1992) reverses perceived motion direction. Finally, CLFM is invariant to contrast and phase.