VISOR is a neural network system for object recognition and scene analysis that learns 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 underlie much of human visual processing, and VISOR can therefore be used to model various perceptual phenomena. This paper focuses on analyzing 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 gradually learned from reflex actions. Successful simulation of such effects suggests that similar mechanisms may govern human visual perception and learning of visual schemas.

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In Proceedings of the 16th Annual Conference of the Cognitive Science Society, Ashwin Ram and Kurt Eiselt (Eds.), pp. 560-565 1994. Hillsdale, NJ: Erlbaum.

@InProceedings{leow:priming, title={Priming, Perceptual Reversal, And Circular Reaction In A Neural Network Model Of Schema-Based Vision}, author={Wee Kheng Leow and Risto Miikkulainen}, booktitle={Proceedings of the 16th Annual Conference of the Cognitive Science Society}, editor={Ashwin Ram and Kurt Eiselt}, publisher={Hillsdale, NJ: Erlbaum}, key={COGSCI}, pages={560-565}, url="http://www.cs.utexas.edu/users/ai-lab?leow:cogsci94", year={1994} }