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Updated: May 17, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

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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

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 Workflow Computational Psychiatry Hierarchical Gaussian Filter (HGF)Translational Neuromodeling multimodal response models robust 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.