Useful misrepresentation: perception as embodied proactive inference. Issue 8 (August 2021)

• Record Type: Journal Article
• Authors: Martin, Joshua M.; Solms, Mark; Sterzer, Philipp
• Is Part Of: Trends in neurosciences. Volume 44:Issue 8(2021)
• Page Start: 619
• Page End: 628
• Publication Date: 2021-08
• Subjects: predictive processing -- allostasis -- perceptual inference -- expectations -- utility -- error management theory; Neurology -- Periodicals; Neurophysiology -- Periodicals; Neurobiology -- Periodicals; 612.8
• DOI: 10.1016/j.tins.2021.04.007
• Languages: English
• ISSNs: 0166-2236

Abstract

Abstract : According to the predictive processing framework, perception is geared to represent the environment in terms of embodied action opportunities as opposed to objective truth. Here, we argue that such an optimisation is reflected by biases in expectations (i.e., prior predictive information) that facilitate 'useful' inferences of external sensory causes. To support this, we highlight a body of literature suggesting that perception is systematically biased away from accurate estimates under conditions where utility and accuracy conflict with one another. We interpret this to reflect the brain's attempt to adjudicate between conflicting sources of prediction error, as external accuracy is sacrificed to facilitate actions that proactively avoid physiologically surprising outcomes. This carries important theoretical implications and offers new insights into psychopathology. Highlights: Converging lines of evidence place allostatic physiological regulation as a core imperative of perception. From the perspective of predictive processing, we evaluate how such an optimisation is reflected by biases in expectations underlying perception. Within perceptual inference, expectations and attention are often assigned functionally orthogonal roles, whereby attention alters the gain on sensory signals based upon their estimated utility, while expectations bias perception according to regularities in the environment. We argue that expectations are utility driven, biasing perceptual inference towards useful outcomes that may be inaccurate but, importantly, facilitate actions that preemptively avoid biologically unfavourable regulatory conditions. Internal models are thus structured, tuned, and updated in ways that usefully misrepresent the world.