Enactivism and predictive processing: a non-representational view

• Published in: Philosophical explorations Vol. 21; no. 2; pp. 264 - 281
• Main Authors: Kirchhoff, Michael D., Robertson, Ian
• Format: Journal Article
• Language: English
• Published: Abingdon Routledge 04.05.2018; Taylor & Francis Ltd
• Subjects: (mis-)representation; Bayesian analysis; Cognition & reasoning; covariance; enactivism; Entropy; generalised synchrony; Kullback-Leibler-divergence; mutual information; predictive processing; relative entropy; Shannon information
• ISSN: 1386-9795; 1741-5918
• DOI: 10.1080/13869795.2018.1477983

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.