To be precise, the details don't matter: On predictive processing, precision, and level of detail of predictions
Abstract
Many theoretical and empirical contributions to the Predictive Processing account emphasize the important role of precision modulation of prediction errors. Recently it has been proposed that the causal models used in human predictive processing are best formally modeled by categorical probability distributions. Crucially, such distributions assume a well-defined, discrete state space. In this paper we explore the consequences of this formalization. In particular we argue that the level of detail of generative models and predictions modulates prediction error. We show that both increasing the level of detail of the generative models and decreasing the level of detail of the predictions can be suitable mechanisms for lowering prediction errors. Both increase precision, yet come at the price of lowering the amount of information that can be gained by correct predictions. Our theoretical result establishes a key open empirical question to address: How does the brain optimize the trade-off between high precision and information gain when making its predictions?
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Differential weightings of specific hierarchical levels in different psychotic symptoms could explain the heterogeneity in psychotic phenomenology.
“This raises a hitherto underappreciated mechanism through which the heterogeneity in psychotic phenomenology could be explained, namely differential weightings of specific hierarchical levels in different psychotic symptoms (70).”
From The Predictive Coding Account of Psychosis (2018) · cites this paper as [70]
Many core quantities invoked by predictive coding (such as prediction errors, value updating, and precision) also arise in alternative computational frameworks including reinforcement learning, Bayesian decision theory, and control-theoretic models.
“Many of the core quantities invoked by predictive coding (such as prediction errors, value updating, and precision) also arise in alternative computational frameworks including reinforcement learning, Bayesian decision theory, and control-theoretic models (79).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [79]
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“If one instead assumes a higher-level prediction, such as ‘either a vertical or horizontal stimulus will appear’, then all S1 stimuli would be reclassed as expected stimuli (see Kwisthout et al. 2017).”
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Asai, T., Imaizumi, S., & Imamizu, H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/519934 · Cited by 3 · see citing works
Causal learning in the Crib: A predictive processing formalization and babybot simulation
Otworowska, M., Zaadnoordijk, L., de Wolff, E., Kwisthout, J., & van Rooij, I. (2016) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2016.7846786 · Cited by 2 · see citing works
“Causal Learning in the Crib: A predictive processing formalization and babybot simulation Maria Otworowska, Lorijn Zaadnoordijk, Erwin de Wolff, Johan Kwisthout, Iris van Rooij Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour I. INTRODUCTION Predictive Processing (PP) [1], [2], [3] is…”
MethodEnvironmentalism and the Minds of Gods
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López‐Silva, P., & Álvaro Cavieres, F. (2023) · Oxford University Press eBooks · doi:10.1093/med/9780192896162.003.0008 · Cited by 2 · see citing works
Schmid, F. R., & Kriegleder, M. (2023) · Consciousness and Cognition · doi:10.1016/j.concog.2023.103620 · Cited by 1 · see citing works
“This results in a cascade of increasingly abstract information exchange that hierarchically runs between low-level sensory input to high-level cognitive mechanisms in a feedforward-feedback manner (Kwisthout et al., 2017; Piekarski, 2021).”
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White, D. J. (2021) · The Atrium (University of Guelph)
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THE UNDERLYING BIOMECHANICS OF OUR BRAIN
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MINDING THE PORTFOLIO OF BELIEFS II
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Young, J. H. (2023) · Wiley eBooks · doi:10.1002/9781119767398.ch6
INTRODUCTION TO THE MBSAT PROGRAM: ESSENTIAL FOUNDATIONS TO MAKE YOUR MBSAT TRAINING EFFECTIVE
Young, J. H. (2023) · Wiley eBooks · doi:10.1002/9781119767398.ch4
MINDING BETA IV ‐ ACTION IMPULSES
Young, J. H. (2023) · Wiley eBooks · doi:10.1002/9781119767398.ch8
Young, J. H. (2023) · Wiley eBooks · doi:10.1002/9781119767398.ch7
Young, J. H. (2023) · Wiley eBooks · doi:10.1002/9781119767398.ref
MINDING THE PORTFOLIO OF BELIEFS III
Young, J. H. (2023) · Wiley eBooks · doi:10.1002/9781119767398.ch11
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Bayesian surprise tracks the strength of perceptual insightPreprint
Völler, J., Domingo, J. L., & González‐García, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.26.708200
“1 2 3 Visual ambiguity was operationalised as Shannon’s entropy calculated over all participant-provided labels for a specific image at pre-disambiguation (Kwisthout et al., 2017; Shannon, 1948; see Methods).”
- Authors
- Kwisthout, J., Bekkering, H., & van Rooij, I.
- Affiliations
- Radboud University Nijmegen (NL)
- Corresponding author
- Kwisthout, Johan
- ORCID
- Kwisthout, Johan
- Venue
- Brain and Cognition
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 112
- Pages
- 84-91
- ISSN
- 0278-2626 (ISSN-L)
- OpenAlex
- W2343597701
- Cited by
- 73
- References
- 50
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 16 · 2022: 6 · 2021: 10 · 2020: 10 · 2019: 6 · 2018: 8 · 2017: 4 · 2016: 1
- FWCI
- 4.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Brain and Cognition (publishedVersion)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Keywords
- predictive processing, prediction error, level of detail, generative models, information gain, Bayesian inference, discrete state space
- MeSH terms
- Brain, Humans, Models, Neurological, Probability, Uncertainty
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