Abstract
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.
Highlights
Our immersion in a seamless and coherent perceptual experience veils the reality that it must be assembled from a sea of noisy and ambiguous sensory information.[1,2,3] It was Helmholtz who first proposed that perception does not obediently reflect the sensory inputs that undergird it, as exposed in myriad perceptual illusions,[4,5,6] but arises from an inferential process in which stimuli are interpreted in light of our past experiences
BNote that there are other biologically plausible accounts of predictive processing, which many take to be synonymous with active inference, they would not necessarily involve prediction error minimization, but here we focus on predictive coding because it has been by far the dominant account considered by the extant neurophysiological literature
The explanatory power of this framework, exemplified in simulations of predictive processing (PP) that readily account for phenomena ranging from V1 neuron response properties[207] to bistable perception[29] to perceptual illusions,[30,31] has led some to argue that everything the brain does can be explained in terms of prediction error minimization.[9,17,32]
Summary
Our immersion in a seamless and coherent perceptual experience veils the reality that it must be assembled from a sea of noisy and ambiguous sensory information.[1,2,3] It was Helmholtz who first proposed that perception does not obediently reflect the sensory inputs that undergird it, as exposed in myriad perceptual illusions,[4,5,6] but arises from an inferential process in which stimuli are interpreted in light of our past experiences.
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