A model of motion adaptation and motion after-effects based upon principalcomponent regression

A computational model to help explain effects of adaptation to moving signa ls is compared with established energy (linear regression) models of motion detection. The proposed model assumes that processed image signals are sub ject to error in both dimensions of space and time. This assumption constra ins models of motion perception to be based upon principal component regres sion rather than linear regression. It is shown that response suppression o f model complex cell neurons that input into the model may account for (1) increases in perceived speed after adaptation to static patterns and testin g with slowly moving patterns, (2) significant increases in perceived speed after adaptation to patterns moving at a medium speed and testing at high speed, and (3) decreases in perceived speed in the opponent direction to a quickly moving adapting signal. Neither of predictions (2) or (3) are gener al features of established accounts of motion detection by visual processes based upon linear regression. Comparisons of the proposed model's speed tr ansfer function with existing psychophysical data suggests that the visual system processes motion signals with the tacit assumption that image measur ements are subject to error in both space and time.