Publication 5387

Kiefer A. & Hohwy J. (2018) Content and misrepresentation in hierarchical generative models. Synthese 195(6): 2387–2415.
In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization (PEM) framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski (Synthese 193(2):559–582, 2016) and Gładziejewski and Miłkowski (Biol Philos, 2017), to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing that it is consistent with approaches implicit in theories of unsupervised learning in neural networks. In the course of this discussion, we argue that the structural representation proposal, properly understood, has more in common with functional-role than with causal/informational or teleosemantic theories. In the remainder of the paper, we describe the PEM framework for approximate Bayesian inference in some detail, and discuss how structural representations might arise within the proposed Bayesian hierarchies. After explicating the notion of variational inference, we define a subjectively accessible measure of misrepresentation for hierarchical Bayesian networks by appeal to the Kullbach–Leibler divergence between posterior generative and approximate recognition densities, and discuss a related measure of objective misrepresentation in terms of correspondence with the facts.
We will upload a full textversion shortly.

The publication has not yet bookmarked in any reading list

You cannot bookmark this publication into a reading list because you are not member of any
Log in to create one.

There are currently no annotations

To add an annotation you need to log in first

Download statistics

Log in to view the download statistics for this publication
Export bibliographic details as: CF Format · APA · BibTex · EndNote · Harvard · MLA · Nature · RIS · Science