- Perception is an unconscious inference, inferring or estimating properties of the world from the stimulus (e.g., the light that reaches the eye). These inferences have evolved/developed to match the statistics of the environment (e.g., Bayesian estimation with priors that embody statistics of environment). Functional specialization and computational theory are the two balancing principles in the field, that guide our approach to understanding these unconscious inferences.

- Functional specialization. Each brain area (defined on the basis of physiology, architecture, connections, topography) performs a different function (e.g., visual motion perception and visual cortical area MT).

- Computational theory. Canonical computation (linear sum, threshold or sigmoid nonlinearity, gain control, adaptation) in each brain area. The same computations cascaded from one brain area to the next along a pathway estimate/infer properties of the world (e.g., velocity of visual motion). Increasing selectivity and invariance across a such a hierarchy of computations.