Understanding Predictive Processing. A Review

Avant, Vol. XII, No. 1, https://doi.org/10.26913/avant.2021.01.04
published under license CC BY-NC-ND 3.0

Michał Piekarski orcid-id
Institute of Philosophy
Cardinal Stefan Wyszyński University in Warsaw

Received 4 August 2020; accepted 31 August 2021; published 5 September 2021.
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Abstract: The purpose of this paper is to provide a systematic review of the Predictive Processing framework (hereinafter PP) and to identify its basic theoretical difficulties. For this reason, it is, primarily, polemic-critical and, secondarily, historical. I discuss the main concepts, positions and research issues present within this framework (§1-2). Next, I present the Bayesian-brain thesis (§3) and the difficulty associated with it (§4). In §5, I compare the conservative and radical approach to PP and discuss the internalist nature of the generative model in the context of Markov blankets. The possibility of linking PP with the free energy principle (hereinafter FEP) and the homeostatic nature of predictive mechanisms is discussed in §6. This is followed by the presentation of PP’s difficulties with solving the dark room problem and the exploration-exploitation trade-off (§7). I emphasize the need to integrate PP with other models and research frameworks within cognitive science. Thus, this review not only discusses PP, but also provides an assessment of the condition of this research framework in the light of the hopes placed on it by many researchers. The Conclusions section discuss further research challenges and the epistemological significance of PP.

Keywords: predictive processing; Bayesian brain; Bayesian inference; Bayesian models; prediction; prediction error; generative model; hierarchical inference; top-down processing; free energy principle; active inference; Markov blanket; perceptual inference; precision; perception; mechanisms; philosophy of mind; philosophy of cognitive science; epistemology


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