Bayesian Workflow for Generative Modeling in Computational Psychiatry
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Abstract
Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been suggested which, however, has rarely been applied in Translational Neuromodeling and Computational Psychiatry (TN/CP) so far. Here, we present a worked example of Bayesian workflow in the context of a typical application scenario for TN/CP. This application example uses Hierarchical Gaussian Filter (HGF) models, a family of computational models for hierarchical Bayesian belief updating. When equipped with a suitable response model, HGF models can be fit to behavioural data from cognitive tasks; these data frequently consist of binary responses and are typically univariate. This poses challenges for statistical inference due to the limited information contained in such data. We present a novel set of response models that allow for simultaneous inference from multivariate (here: two) behavioural data types. Using both simulations and empirical data from a speed-incentivised associative reward learning (SPIRL) task, we show that models harnessing information from two different data streams (binary responses and continuous response times) ensure robust inference (specifically, identifiability of parameters and models). Moreover, we find a linear relationship between log-transformed response times in the SPIRL task and participants' uncertainty about the outcome. Our analysis illustrates the benefits of Bayesian workflow for a typical use case in TN/CP. We argue that adopting Bayesian workflow for generative modelling helps increase the transparency and robustness of results, which in turn is of fundamental importance for the long-term success of TN/CP.
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The study followed a Bayesian workflow for both computational models and conventional analyses.
“We followed a Bayesian workflow for both computational models and conventional analyses [19, 20].”
From Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework (2026) · cites this paper as [19]
The Methods > Analysis section says the paper followed a Bayesian workflow for its model-based analysis. It describes model-agnostic analyses separately, including descriptive visualisations and a one-way ANOVA, but does not say that conventional analyses followed the Bayesian workflow. The cited paper therefore supports the computational-model part of the sentence, not the conventional-analyses part. (LLM, conf=0.84)
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- Authors
- Hess, A. J., Iglesias, S., Köchli, L., Marino, S., Müller-Schrader, M., Rigoux, L., Mathys, C., Harrison, O. K., Heinzle, J., Frässle, S., & Stephan, K. E.
- Affiliations
- University of Zurich (CH)
- Max Planck Institute for Metabolism Research (DE)
- Aarhus University (DK)
- Corresponding author
- Hess, Alexander J.
- ORCID
- Venue
- Computational Psychiatry
- Publisher
- Ubiquity Press, Ltd.
- Published
- 2025
- Volume
- 9
- Issue
- 1
- Pages
- 76-99
- ISSN
- 2379-6227 (ISSN-L)
- PMC
- PMC11951975
- OpenAlex
- W4408807552
- Cited by
- 10
- References
- 54
- Citations by year
- 2026: 5 · 2025: 5
- FWCI
- 8.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Computational Psychiatry (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- generative modelling, response model, binary outcomes, reaction time (psychology), parameter identifiability, statistical inference
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