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Vanilla PP for Philosophers: A Primer on Predictive Processing

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Wiese, Wanja, Metzinger, Thomas K.·2017·Theoretical Philosophy/MIND Group – JGU Mainz
DOI 10.15502/9783958573024
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Abstract

The goal of this short chapter, aimed at philosophers, is to provide an overview and brief explanation of some central concepts involved in predictive processing (PP). Even those who consider themselves experts on the topic may find it helpful to see how the central terms are used in this collection. To keep things simple, we will first informally define a set of features important to predictive processing, supplemented by some short explanations and an alphabetic glossary. The features described here are not shared in all PP accounts. Some may not be necessary for an individual model; others may be contested. Indeed, not even all authors of this collection will accept all of them. To make this transparent, we have encouraged contributors to indicate briefly which of the features are necessary to support the arguments they provide, and which (if any) are incompatible with their account. For the sake of clarity, we provide the complete list here, very roughly ordered by how central we take them to be for “vanilla PP” (i.e., a formulation of predictive processing that will probably be accepted by most researchers working on this topic). More detailed explanations will be given below. Note that these features do not specify individually necessary and jointly sufficient conditions for the application of the concept of “predictive processing”. All we currently have is a semantic cluster, with perhaps some overlapping sets of jointly sufficient criteria. The framework is still developing, and it is difficult, maybe impossible, to provide theory-neutral explanations of all PP ideas without already introducing strong background assumptions. Nevertheless, at least features 1-7 can be regarded as necessary properties of what is called PP in this volume:

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  • What the PCM adds to predictive coding, active inference and Bayesian Brain paradigms is a crucial geometrical component, yielding a psycho-functional model that is independent of the specific brain implementation.

    “What the PCM adds to all the approaches in the predictive coding, active inference and Bayesian Brain paradigms (see, e.g., Wiese and Metzinger, 2017) is this crucial geometrical component, yielding a psycho-functional model that is independent of the specific brain implementation.”

    From The Projective Consciousness Model and Phenomenal Selfhood (2018) · cites this paper as Wiese and Metzinger, 2017

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Authors
Wiese, W., & Metzinger, T. K.
ORCID
Venue
Theoretical Philosophy/MIND Group – JGU Mainz
Publisher
Theoretical Philosophy/MIND Group – JGU Mainz
Published
2017
DOI
10.15502/9783958573024
OpenAlex
W2603171575
Cited by
130
References
1
Citations by year
2026: 1 · 2025: 12 · 2024: 12 · 2023: 29 · 2022: 20 · 2021: 20 · 2020: 15 · 2019: 8 · 2018: 9 · 2017: 4
FWCI
5.65
Type
journal article
Language
en
Open access
Locations
PhilPapers (PhilPapers Foundation) (submittedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
  • Social Sciences › Arts and Humanities › Philosophy › Philosophy and Theoretical Science
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
Keywords
predictive processing, philosophy of mind, cognitive science, semantic clusters, conceptual analysis, perceptual inference, active inference

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