Bayesian Workflow for Generative Modeling in Computational Psychiatry
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 Translat...
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| Format: | Article |
| Language: | English |
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Ubiquity Press
2025-03-01
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| Series: | Computational Psychiatry |
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| Online Access: | https://account.cpsyjournal.org/index.php/up-j-cp/article/view/116 |
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| author | Alexander J. Hess Sandra Iglesias Laura Köchli Stephanie Marino Matthias Müller-Schrader Lionel Rigoux Christoph Mathys Olivia K. Harrison Jakob Heinzle Stefan Frässle Klaas Enno Stephan |
| author_facet | Alexander J. Hess Sandra Iglesias Laura Köchli Stephanie Marino Matthias Müller-Schrader Lionel Rigoux Christoph Mathys Olivia K. Harrison Jakob Heinzle Stefan Frässle Klaas Enno Stephan |
| author_sort | Alexander J. Hess |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-168787b3493449ea80e3af6d59d1a452 |
| institution | OA Journals |
| issn | 2379-6227 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Ubiquity Press |
| record_format | Article |
| series | Computational Psychiatry |
| spelling | doaj-art-168787b3493449ea80e3af6d59d1a4522025-08-20T02:13:14ZengUbiquity PressComputational Psychiatry2379-62272025-03-019176–9976–9910.5334/cpsy.11693Bayesian Workflow for Generative Modeling in Computational PsychiatryAlexander J. Hess0https://orcid.org/0000-0002-2991-7387Sandra Iglesias1https://orcid.org/0000-0002-1778-7239Laura Köchli2https://orcid.org/0009-0001-0038-9592Stephanie Marino3https://orcid.org/0000-0002-6559-9072Matthias Müller-Schrader4https://orcid.org/0000-0001-6672-9230Lionel Rigoux5https://orcid.org/0000-0003-3761-8931Christoph Mathys6https://orcid.org/0000-0003-4079-5453Olivia K. Harrison7https://orcid.org/0000-0003-0897-7142Jakob Heinzle8https://orcid.org/0000-0001-5228-041XStefan Frässle9https://orcid.org/0000-0002-8011-2226Klaas Enno Stephan10https://orcid.org/0000-0002-8594-9092Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, ZurichTranslational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, ZurichTranslational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, ZurichTranslational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, ZurichTranslational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, ZurichMax Planck Institute for Metabolism Research, CologneInteracting Minds Centre, Aarhus University, AarhusTranslational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, CH; Department of Psychology, University of Otago, DunedinTranslational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, ZurichTranslational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, ZurichTranslational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, CH; Max Planck Institute for Metabolism Research, CologneComputational (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.https://account.cpsyjournal.org/index.php/up-j-cp/article/view/116translational neuromodelingcomputational psychiatrybayesian workflowhierarchical gaussian filter (hgf)multimodal response modelsrobust inference |
| spellingShingle | Alexander J. Hess Sandra Iglesias Laura Köchli Stephanie Marino Matthias Müller-Schrader Lionel Rigoux Christoph Mathys Olivia K. Harrison Jakob Heinzle Stefan Frässle Klaas Enno Stephan Bayesian Workflow for Generative Modeling in Computational Psychiatry Computational Psychiatry translational neuromodeling computational psychiatry bayesian workflow hierarchical gaussian filter (hgf) multimodal response models robust inference |
| title | Bayesian Workflow for Generative Modeling in Computational Psychiatry |
| title_full | Bayesian Workflow for Generative Modeling in Computational Psychiatry |
| title_fullStr | Bayesian Workflow for Generative Modeling in Computational Psychiatry |
| title_full_unstemmed | Bayesian Workflow for Generative Modeling in Computational Psychiatry |
| title_short | Bayesian Workflow for Generative Modeling in Computational Psychiatry |
| title_sort | bayesian workflow for generative modeling in computational psychiatry |
| topic | translational neuromodeling computational psychiatry bayesian workflow hierarchical gaussian filter (hgf) multimodal response models robust inference |
| url | https://account.cpsyjournal.org/index.php/up-j-cp/article/view/116 |
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