, Volume 195, Issue 6, pp 2387–2415 | Cite as

Content and misrepresentation in hierarchical generative models

  • Alex Kiefer
  • Jakob Hohwy
S.I. : Predictive Brains


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.


Problem of content Misrepresentation Functional role semantics Structural resemblance Prediction error minimization Generative model Recognition model Kullbach–Leibler divergence Variational Bayesian inference Unsupervised learning 



We wish to thank Michael Kirchhoff and two anonymous reviewers for comments. JH is supported by Australian Research Council Grants FT100100322 and DP160102770, and by the Research School Bochum and the Center for Mind, Brain and Cognitive Evolution, Ruhr-University Bochum.


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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.City University of New York Graduate CenterNew YorkUSA
  2. 2.Cognition & Philosophy LabMonash UniversityMelbourneAustralia

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