Proceedings of the Annual Meeting of the Cognitive Science Society

ISOR is a neural network system for object recognition and scene analysis that leeims visual schemas from examples. Processing in VISOR is based on cooperation, competition, and parallel bottom-up and top-down activation of schema representations. Similar principles appear to imderlie much of human visual processing, and VISOR can therefore be used to model vairious perceptucd phenomena. This paper focuses on anedyzing three phenomena through simulation with VISOR: (1) priming and mental imagery, (2) perceptual reversal, and (3) circular reaction. The results illustrate similarity and subtle differences between the mechanisms mediating priming and mental imagery, show how the two opposing accounts of perceptual reversal (neural satiation and cognitive factors) may both contribute to the phenomenon, and demonstrate how intentional actions can be graduaJly learned from reflex cictions. Successful simulation of such effects suggests that similar mechanisms may govern human visual perception eind learning of visual schemas.