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Enactivism and predictive processing: a non-representational view

Journal Article


Abstract


  • This paper starts by considering an argument for thinking that predictive processing (PP) is representational. This argument suggests that the KullbackÔÇôLeibler (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.

Publication Date


  • 2018

Citation


  • Kirchhoff, M. D. & Robertson, I. (2018). Enactivism and predictive processing: a non-representational view. Philosophical Explorations: an international journal for the philosophy of mind and action, 21 (2), 264-281.

Scopus Eid


  • 2-s2.0-85049311119

Ro Metadata Url


  • http://ro.uow.edu.au/lhapapers/3547

Number Of Pages


  • 17

Start Page


  • 264

End Page


  • 281

Volume


  • 21

Issue


  • 2

Place Of Publication


  • United Kingdom

Abstract


  • This paper starts by considering an argument for thinking that predictive processing (PP) is representational. This argument suggests that the KullbackÔÇôLeibler (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.

Publication Date


  • 2018

Citation


  • Kirchhoff, M. D. & Robertson, I. (2018). Enactivism and predictive processing: a non-representational view. Philosophical Explorations: an international journal for the philosophy of mind and action, 21 (2), 264-281.

Scopus Eid


  • 2-s2.0-85049311119

Ro Metadata Url


  • http://ro.uow.edu.au/lhapapers/3547

Number Of Pages


  • 17

Start Page


  • 264

End Page


  • 281

Volume


  • 21

Issue


  • 2

Place Of Publication


  • United Kingdom