Representation in the Prediction Error Minimization Framework

In Sarah K. Robins, John Symons & Paco Calvo (eds.), The Routledge Companion to Philosophy of Psychology: 2nd Edition. London, UK: pp. 384-409 (2019)

Authors
Alex Kiefer
Monash University
Jakob Hohwy
Monash University
Abstract
This chapter focuses on what’s novel in the perspective that the prediction error minimization (PEM) framework affords on the cognitive-scientific project of explaining intelligence by appeal to internal representations. It shows how truth-conditional and resemblance-based approaches to representation in generative models may be integrated. The PEM framework in cognitive science is an approach to cognition and perception centered on a simple idea: organisms represent the world by constantly predicting their own internal states. PEM theories often stress the hierarchical structure of the generative models they posit. The novel explanatory power of the PEM account derives largely from the way in which pairs of generative and recognition models interact. “Predictive coding” refers to an encoding strategy in which predicted portions of an input signal are subtracted from the actual signal received, so that only the difference between the two is passed as output to the next stage of information processing.
Keywords Generative models  Bayesian inference  Free energy minimization  Structural representation  Predictive coding
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Predictive Coding and Thought.Daniel Williams - 2020 - Synthese 197 (4):1749-1775.
Are Generative Models Structural Representations?Marco Facchin - 2021 - Minds and Machines 31 (2):277-303.

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