Enactivism and predictive processing: A non-representational view
Philosophical Explorations 21 (2):264-281 (2018)
Abstract
This paper starts by considering an argument for thinking that predictive processing (PP) is representational. This argument suggests that the Kullback–Leibler (KL)-divergence provides an accessible measure of misrepresentation, and therefore, a measure of representational content in hierarchical Bayesian inference. The paper then argues that while the KL-divergence is a measure of information, it does not establish a sufficient measure of representational content. We argue that this follows from the fact that the KL-divergence is a measure of relative entropy, which can be shown to be the same as covariance (through a set of additional steps). It is well known that facts about covariance do not entail facts about representational content. So there is no reason to think that the KL-divergence is a measure of (mis-)representational content. This paper thus provides an enactive, non-representational account of Bayesian belief optimisation in hierarchical PP.Author Profiles
DOI
10.1080/13869795.2018.1477983
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References found in this work
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Radicalizing Enactivism: Basic Minds Without Content.Daniel D. Hutto & Erik Myin - 2013 - Cambridge, MA: MIT Press.
Surfing Uncertainty: Prediction, Action, and the Embodied Mind.Andy Clark - 2015 - New York: Oxford University Press USA.