Computational Evolutionary Perception

Abstract

Marr proposed that human vision constructs “a true description of what is there”. He argued that to understand human vision one must discover the features of the world it recovers and the constraints it uses in the process. Bayesian decision theory (BDT) is used in modern vision research as a probabilistic framework for understanding human vision along the lines laid out by Marr. Marr's contribution to vision research is substantial and justly influential. We propose, however, that evolution by natural selection does not, in general, favor perceptions that are true descriptions of the objective world. Instead, research with evolutionary games shows that perceptual systems tuned solely to fitness routinely outcompete those tuned to truth. Fitness functions depend not just on the true state of the world, but also on the organism, its state, and the type of action. Thus, fitness and truth are distinct. Natural selection depends only on expected fitness. It shapes perceptual systems to guide fitter behavior, not to estimate truth. To study perception in an evolutionary context, we introduce the framework of Computational Evolutionary Perception (CEP). We show that CEP subsumes BDT, and reinterprets BDT as evaluating expected fitness rather than estimating truth.