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Title
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Minimizing prediction errors in predictive processing : from inconsistency to non-representationalism
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Author
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Abstract
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| Predictive processing is an increasingly popular approach to cognition, perception and action. It says that the brain is essentially a hierarchical prediction machine. It is typically construed in a representationalist and inferentialist fashion so that the brain makes contentful inferences on the basis of representational models. In this paper, I argue that the predictive processing framework is inconsistent with this epistemic position. In particular, I argue that the combination of hierarchical modeling, contentful inferentialism and representationalism entail an internal inconsistency. Specifically, for a particular set of states, there will be both a representation requirement and not. Yet a system cannot both be required to represent a certain set of states and not be required to represent those states. Due to this contradiction, I propose to reject the standard view. I suggest that predictive processing is best interpreted in terms of reliable covariation instead, entailing an instrumentalist approach to the statistical machinery. |
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Language
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English
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Source (journal)
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Phenomenology and the cognitive sciences. - Dordrecht, 2002, currens
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Publication
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Dordrecht
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2020
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ISSN
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1568-7759
[print]
1572-8676
[online]
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DOI
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10.1007/S11097-019-09649-Y
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Volume/pages
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19
:5
(2020)
, p. 997-1017
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ISI
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000577412300008
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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