Evaluating the neurophysiological evidence for predictive processing as a model of perception

Ann N Y Acad Sci. 2020 Mar;1464(1):242-268. doi: 10.1111/nyas.14321. Epub 2020 Mar 8.


For many years, the dominant theoretical framework guiding research into the neural origins of perceptual experience has been provided by hierarchical feedforward models, in which sensory inputs are passed through a series of increasingly complex feature detectors. However, the long-standing orthodoxy of these accounts has recently been challenged by a radically different set of theories that contend that perception arises from a purely inferential process supported by two distinct classes of neurons: those that transmit predictions about sensory states and those that signal sensory information that deviates from those predictions. Although these predictive processing (PP) models have become increasingly influential in cognitive neuroscience, they are also criticized for lacking the empirical support to justify their status. This limited evidence base partly reflects the considerable methodological challenges that are presented when trying to test the unique predictions of these models. However, a confluence of technological and theoretical advances has prompted a recent surge in human and nonhuman neurophysiological research seeking to fill this empirical gap. Here, we will review this new research and evaluate the degree to which its findings support the key claims of PP.

Keywords: neurophysiology; perception; perceptual inference; predictive coding; predictive processing.

Publication types

  • Review

MeSH terms

  • Humans
  • Models, Neurological
  • Neurons / physiology*
  • Neurophysiology*
  • Perception / physiology*