Priming, Perceptual Reversal, and Circular Reaction in a Neural Network Model of Schema-Based Vision

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 as a platform for testing hypotheses about various perceptual phenomena. This paper focuses on analyzing three phenomena through simulation with VISOR: (1) the effects of 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.

[1]  Thomas C. Toppino,et al.  Prime time: Fatigue and set effects in the perception of reversible figures , 1992, Perception & psychophysics.

[2]  Stephen Grossberg,et al.  Neural dynamics of adaptive sensory-motor control , 1986 .

[3]  Representing Visual Schemas in Neural Networks for Object Recognition , 1993 .

[4]  Wee Kheng Leow and Risto Miikkulainen.  Representing And Learning Visual Schemas In Neural Networks For Scene Analysis , 1994 .

[5]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[6]  R. Finke,et al.  Principles of mental imagery , 1989 .

[7]  P. Rabbitt,et al.  Memory and data-driven control of selective attention continuous tasks. , 1979 .

[8]  J. Barnden.  Michael A. Arbib, The metaphorical brain 2: Neural networks and beyond , 1998 .

[9]  Risto Miikkulainen,et al.  Visual Schemas in Neural Networks for Object Recognition and Scene Analysis , 1997, Connect. Sci..

[10]  D. V. van Essen,et al.  A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[11]  M. Arbib.  CHAPTER 5 – Schemas and Perception: Perspectives from Brain Theory and Artificial Intelligence* , 1986 .

[12]  H. K. Beller.  Priming: effects of advance information on matching. , 1971, Journal of experimental psychology.

[13]  F. Attneave.  Multistability in perception. , 1971, Scientific American.

[14]  P. L. Adams.  THE ORIGINS OF INTELLIGENCE IN CHILDREN , 1976 .

[15]  Bruce A. Draper,et al.  The schema system , 1988, International Journal of Computer Vision.

[16]  M. Mozer,et al.  On the Interaction of Selective Attention and Lexical Knowledge: A Connectionist Account of Neglect Dyslexia , 1990, Journal of Cognitive Neuroscience.

[17]  M. Farah,et al.  Psychophysical evidence for a shared representational medium for mental images and percepts. , 1985, Journal of experimental psychology. General.

[18]  S. Grossberg,et al.  The Adaptive Brain , 1990 .

[19]  T. Carr,et al.  Words, pictures, and priming: on semantic activation, conscious identification, and the automaticity of information processing. , 1982, Journal of experimental psychology. Human perception and performance.

[20]  B. R. Bugelski,et al.  The role of frequency in developing perceptual sets. , 1961, Canadian journal of psychology.

[21]  J. Wilder.  The Origins of Intelligence in Children , 1954 .