期刊
PHILOSOPHICAL EXPLORATIONS
卷 21, 期 2, 页码 264-281出版社
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/13869795.2018.1477983
关键词
predictive processing; enactivism; (mis-)representation; Kullback-Leibler-divergence; relative entropy; mutual information; generalised synchrony; covariance; Shannon information
类别
资金
- Australian Research Council Discovery Project Minds in Skilled Performance [DP 170102987]
- Australian Research Council PhD scholarship, as part of the Discovery Project Minds in Skilled Performance [DP 170102987]
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.
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