Computational Implications of Biological Vision: A Gestalt Model of Spatial Perception

Abstract

A serious crisis is identified in theories of neurocomputation, which has implications for computer models of visual processing inspired by biological vision. The problem is reflected in a persistent disparity between the phenomenological or experiential account of visual perception and the neurophysiological or computational level of description. In particular, conventional concepts of neural processing as well as image processing algorithms offer no explanation for the holistic global aspects of perception identified by Gestalt theory. The problem is paradigmatic, and can be traced to atomistic concepts of computation embodied in the biological notion of neurocomputation, as well as in the paradigm of digital computation. I propose a perceptual modeling approach, i.e. to model the percept as experienced subjectively, rather than the objective neurophysiological state of the visual system that supposedly subserves that experience. A Gestalt Bubble model is presented to demonstrate how the elusive Gestalt principles of emergence, reification, and invariance, can be expressed in a quantitative model of the subjective experience of visual consciousness. That model in turn reveals a unique computational strategy underlying visual processing, which is unlike any algorithm devised by man, and certainly unlike the atomistic feed-forward model of visual processing characteristic of many biological and computer models. The perceptual modeling approach reveals the primary function of perception as that of generating a fully spatial virtual-reality replica of the external world in an internal representation.