Content and misrepresentation in hierarchical generative models

Synthese 195 (6):2387-2415 (2018)
Authors
Jakob Hohwy
Monash University
Alex Kiefer
City University of New York
Abstract
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 framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski :559–582, 2016) and Gładziejewski and Miłkowski, 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.
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DOI 10.1007/s11229-017-1435-7
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Pragmatism and the Predictive Mind.Daniel Williams - forthcoming - Phenomenology and the Cognitive Sciences:1-25.
Neural Representations Observed.Eric Thomson & Gualtiero Piccinini - 2018 - Minds and Machines 28 (1):191-235.

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