Enactivism and predictive processing: A non-representational view

Philosophical Explorations 21 (2):264-281 (2018)
  Copy   BIBTEX

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.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 94,726

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Analytics

Added to PP
2018-07-02

Downloads
106 (#165,770)

6 months
27 (#135,613)

Historical graph of downloads
How can I increase my downloads?

Author Profiles