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Andy Clark and His Critics$
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Matteo Colombo, Elizabeth Irvine, and Mog Stapleton

Print publication date: 2019

Print ISBN-13: 9780190662813

Published to Oxford Scholarship Online: May 2019

DOI: 10.1093/oso/9780190662813.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2019. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 06 December 2019

How Radical Is Predictive Processing?

How Radical Is Predictive Processing?

Chapter:
(p.206) 15 How Radical Is Predictive Processing?
Source:
Andy Clark and His Critics
Author(s):

Nico Orlandi

Geoff Lee

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190662813.003.0016

This chapter discusses Andy Clark’s recent explorations of Bayesian perceptual models and predictive processing. In the first part, the chapter discusses the predictive processing (PP) framework, explicating its relationship with hierarchical Bayesian models in theories of perception. In the second part, it examines the relationship between perception and action in the PP model. The overarching goal is twofold: first, to get clearer on the picture of mental activity that Clark is presenting, including what exactly is represented at the levels of the perception/action hierarchy, and the nature of the information processing it postulates; second, although the framework presented by Clark certainly has interesting novel features, some of his glosses on it are misleading. In particular, Clark’s interpretation of predictive processing as essentially a top-down, expectation-driven process, on which perception is aptly thought of as “controlled hallucination,” exaggerates the contrast with the traditional picture of perception as bottom-up and stimulus-driven. Additionally, despite the rhetoric, Clark’s PP model substantially preserves the traditional distinction between perception and action.

Keywords:   predictive processing, hierarchical Bayes, Bayesian inference, perception, bottom-up, top-down, representation, forward model, active inference, optimal feedback control

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