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.

Key words: evolution, fitness, natural selection, bayesian decision theory, vision, evolutionary game theory.

We propose that selection favors nonveridical perceptions that are tuned to fitness. Current textbooks assert, to the contrary, that perception is useful because, in the normal case, it is veridical. Intuition, both lay and expert, clearly sides with the textbooks. We thus expected that some commentators would reject our proposal and provide counterarguments that could stimulate a productive debate. We are pleased that several commentators did indeed rise to the occasion and have argued against our proposal. We are also pleased that several others found our proposal worth exploring and have offered ways to test it, develop it, and link it more deeply to the history of ideas in the science and philosophy of perception. To both groups of commentators: thank you. Point and counterpoint, backed by data and theory, is the essence of science. We hope that the exchange recorded here will advance the scientific understanding of perception and its evolution. In what follows, we respond to the commentaries in alphabetical order.

Key words: bayesian inference, categorization, parameter estimation, perceptual categorization and identification, visual perception.

Perception is a product of evolution. Our perceptual systems, like our limbs and livers, have been shaped by natural selection. The effects of selection on perception can be studied using evolutionary games and genetic algorithms. To this end, we define and classify perceptual strategies and allow them to compete in evolutionary games in a variety of worlds with a variety of fitness functions. We find that veridical perceptions – strategies tuned to the true structure of the world – are routinely dominated by nonveridical strategies tuned to fitness. Veridical perceptions escape extinction only if fitness varies monotonically with truth. Thus, a perceptual strategy favored by selection is best thought of not as a window on truth but as akin to a windows interface of a PC. Just as the color and shape of an icon for a text file do not entail that the text file itself has a color or shape, so also our perceptions of space-time and objects do not entail (by the Invention of Space-Time Theorem) that objective reality has the structure of space-time and objects. An interface serves to guide useful actions, not to resemble truth. Indeed, an interface hides the truth; for someone editing a paper or photo, seeing transistors and firmware is an irrelevant hindrance. For the perceptions of H. sapiens, space-time is the desktop and physical objects are the icons. Our perceptions of space-time and objects have been shaped by natural selection to hide the truth and guide adaptive behaviors. Perception is an adaptive interface.

Key words: bayesian inference and parameter estimation, perceptual learning, perceptual categorization and identification, spatial cognition, evolution.