Perception and perceptual decision-making are strongly facilitated by prior knowledge about the probabilistic structure of the world. While the computational benefits of using prior expectation in perception are clear, there are myriad ways in which this computation can be realized. We review here recent advances in our understanding of the neural sources and targets of expectations in perception. Furthermore, we discuss Bayesian theories of perception that prescribe how an agent should integrate prior knowledge and sensory information, and investigate how current and future empirical data can inform and constrain computational frameworks that implement such probabilistic integration in perception.

Key words: prediction, perception, sensory processing, bayesian inference, predictive coding, perceptual inference.

Phenomenology and physiology become commensurable through a self-organizational physiology and an “enactive” view of consciousness. Selforganizing processes appropriate and replace their own needed substrata, rather than merely being caused by interacting components. Biochemists apply this notion to the living/ nonliving distinction. An enactive approach sees consciousness as actively executed by an agent rather than passively reacting to stimuli. Perception does not result from mere stimulation of brain areas by sensory impulses; unless motivated organismic purposes first anticipate and “look for” emotionally relevant stimuli, brain-sensory processing is not accompanied by perceptual consciousness. To see a soccer ball requires looking for it in the right place. The self-organizing, emotionally motivated agent instigates this looking for activity.