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  1. Home
  3. Bayesian Workflow For Generative Modeling In Computational Psychiatry.
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  3. Bayesian Workflow For Generative Modeling In Computational Psychiatry.

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Bayesian Workflow for Generative Modeling in Computational Psychiatry.

Alexander J Hess1, Sandra Iglesias1, Laura Köchli1

  • 1Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.

Computational Psychiatry (Cambridge, Mass.)
|March 31, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Bayesian workflow enhances generative models for clinical applications by improving statistical inference. This approach, using Hierarchical Gaussian Filter models, ensures robust results in Translational Neuromodeling and Computational Psychiatry.

Keywords:
Bayesian WorkflowComputational PsychiatryHierarchical Gaussian Filter (HGF)Translational Neuromodelingmultimodal response modelsrobust inference

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Area of Science:

  • Computational Neuroscience
  • Cognitive Science
  • Psychiatry

Background:

  • Generative models hold significant clinical potential but require reliable statistical inference.
  • Bayesian workflow is a suggested but underutilized approach in Translational Neuromodeling and Computational Psychiatry (TN/CP).
  • Hierarchical Gaussian Filter (HGF) models are used for hierarchical Bayesian belief updating in computational modeling.

Purpose of the Study:

  • To demonstrate a practical application of Bayesian workflow in TN/CP.
  • To address challenges in statistical inference with univariate behavioral data.
  • To introduce novel response models for simultaneous inference from multivariate data.

Main Methods:

  • Applied Bayesian workflow to Hierarchical Gaussian Filter (HGF) models.
  • Developed and utilized novel response models for multivariate data (binary responses and response times).
  • Validated methods using simulations and empirical data from a speed-incentivised associative reward learning (SPIRL) task.

    • Main Results:

    • Models utilizing both binary responses and response times ensure robust statistical inference and parameter identifiability.
    • A linear relationship was identified between log-transformed response times and outcome uncertainty in the SPIRL task.
    • The study illustrates the benefits of Bayesian workflow for TN/CP applications.

    Conclusions:

    • Bayesian workflow increases the transparency and robustness of generative modeling in TN/CP.
    • Adopting Bayesian workflow is crucial for the long-term success of Translational Neuromodeling and Computational Psychiatry.
    • The developed multivariate response models improve inference from limited behavioral data.