Natural Selection and Shape Perception

Abstract

We present a formal framework that generalizes and subsumes the standard Bayesian framework for vision. While incorporating the fundamental role of probabilistic inference, our Computational Evolutionary Perception (CEP) framework also incorporates fitness in a fundamental way, and it allows us to consider different possible relationships between the objective world and perceptual representations (e.g., in evolving visual systems). In our framework, shape is not assumed to be a “reconstruction” of an objective world property. It is simply a representational format that has been tuned by natural selection to guide adaptive behavior. In brief, shape is an effective code for fitness. Because fitness depends crucially on the actions of an organism, shape representations are closely tied to actions. We model this connection formally using the Perception-Decision-Action (PDA) loop. Among other things, the PDA loop clarifies how, even though one cannot know the effects of one’s actions in the objective world itself, one can nevertheless know the results of those effects back in our perceptions. This, in turn, explains how organisms can interact effectively with a fundamentally unknown objective world.