A serious crisis is identified in theories of neurocomputation, marked by a persistent disparity between the phenomenological or experiential account of visual perception and the neurophysiological level of description of the visual system. In particular, conventional concepts of neural processing offer no explanation for the holistic global aspects of perception identified by Gestalt theory. The problem is paradigmatic and can be traced to contemporary concepts of the functional role of the neural cell, known as the Neuron Doctrine. In the absence (...) of an alternative neurophysiologically plausible model, I propose a perceptual modeling approach, to model the percept as experienced subjectively, rather than modeling 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 neurocomputation offered by the Neuron Doctrine paradigm. 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. The common objections to this picture-in-the-head concept of perceptual representation are shown to be ill founded. Key Words: brain-anchored; Cartesian theatre; consciousness; emergence; extrinsic constraints; filling-in; Gestalt; homunculus; indirect realism; intrinsic constraints; invariance; isomorphism; multistability; objective phenomenology; perceptual modeling; perspective; phenomenology; psychophysical parallelism; psychophysical postulate; qualia; reification; representationalism; structural coherence. (shrink)

The Gestalt principle of isomorphism reveals the primacy of subjective experience as a valid source of evidence for the information encoded neurophysiologically. This theory invalidates the abstractionist view that the neurophysiological representation can be of lower dimensionality than the percept to which it gives rise.

Computer vision systems are, on most counts, poor performers, when compared to their biological counterparts. The reason for this may be that computer vision is handicapped by an unreasonable assumption regarding what it means to see, which became prevalent as the notions of intrinsic images and of representation by reconstruction took over the field in the late 1970’s. Learning from biological vision may help us to overcome this handicap.