How Do Expectations Shape Perception?

Expectations play a strong role in determining the way we perceive the world. Prior expectations can originate from multiple sources of information, and correspondingly have different neural sources, depending on where in the brain the relevant prior knowledge is stored. Recent findings from both human neuroimaging and animal electrophysiology have revealed that prior expectations can modulate sensory processing at both early and late stages, and both before and after stimulus onset. The response modulation can take the form of either dampening the sensory representation or enhancing it via a process of sharpening. Theoretical computational frameworks of neural sensory processing aim to explain how the probabilistic integration of prior expectations and sensory inputs results in perception. 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. 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.