A ‘complexity level’ analysis of immediate vision

摘要

This paper demonstrates how serious consideration of the deep complexity issues inherent in the design of a visual system can constrain the development of a theory of vision. We first show how the seemingly intractable problem of visual perception can be converted into a much simpler problem by the application of several physical and biological constraints. For this transformation, two guiding principles are used that are claimed to be critical in the development of any theory of perception. The first is that analysis at the ‘complexity level’ is necessary to ensure that the basic space and performance constraints observed in human vision are satisfied by a proposed system architecture. Second, the ‘maximum power/minimum cost principle’ ranks the many architectures that satisfy the complexity level and allows the choice of the best one. The best architecture chosen using this principle is completely compatible with the known architecture of the human visual system, and in addition, leads to several predictions. The analysis provides an argument for the computational necessity of attentive visual processes by exposing the computational limits of bottom-up early vision schemes. Further, this argues strongly for the validity of the computational approach to modeling the human visual system. Finally, a new explanation for the pop-out phenomenon so readily observed in visual search experiments, is proposed.