A massive thank you to all my co-authors for their support and contributions, to our reviewers for their constructive feedback and to the Co-Eds-in-Chief @cpsyjournal.bsky.social, @xiaosigu.bsky.social and @drrickadams.bsky.social.
6/6

Moreover, we argue that adopting Bayesian workflow for generative modelling helps increase the transparency and robustness of results, which is of fundamental importance for the long-term success of Computational Psychiatry.
5/6

We show that harnessing information from two different data streams (binary choices + continuous response times) improves the accuracy of inference (specifically, identifiability of parameters and models).
4/6

Our application example uses #HierarchicalGaussianFiltering (HGF). Next to highlighting the benefits of Bayesian workflow, we introduce multimodal response models in the #HGF framework which allow for simultaneous inference from multivariate data types.
3/6

We present a worked example of #BayesianWorkflow in the context of a typical application scenario for #ComputationalPsychiatry. Bayesian workflow encompasses iterative model building, checking, validation, comparison and understanding.
2/6

I am grateful for the support of my fabulous co-authors Dina, Liv, Jakob and Klaas and look forward to build onto our findings in future work! Happy holidays & merry christmas everyone!🎄 n/n

All of our analyses were prespecified (doi.org/10.5281/zeno...) and both data (doi.org/10.5281/zeno...) and analysis code (github.com/alexjhess/pb...) are openly available. 8/n

Our study represents an initial attempt to refine and formalize ASE theory using methods from causal inference. Our results confirm key predictions from ASE theory but also suggest revisions which require empirical verification in future studies. 7/n

Second, we confirmed the predicted negative average causal effect from metacognition of allostatic control (i.e. the feeling of being in control over one’s own body) to fatigue across different methods of estimation. 6/n

We identified specific aspects of the proposed SCM that were inconsistent with the available data. This enabled formulation of an updated SCM that can be tested against future data. 5/n

We converted ASE theory into a structural causal model (SCM). This allowed identification of empirically testable prespecified (!) hypotheses regarding causal relationships between the central variables of interest using questionnaire data from healthy volunteers. 4/n

In this work, we focus on a recently emerging computational perspective on fatigue and depression, the allostatic self-efficacy theory (ASE; doi.org/10.3389/fnhu...). 3/n

What started as a small pet project as part of a course on causality taught by the inspiring Jonas Peters @ethzurich.bsky.social has now become a nice little piece of work summarising my first steps in the realm of causal inference. 2/n