Are you new to the field of Computational Psychiatry or just looking for resources on applying Bayesian models of cognition to behavioural data? Then check out our new paper "Bayesian Workflow for Generative Modeling in Computational Psychiatry": .1/6.

A massive thank you to my co-authors Sandra, Laura, Stephanie, Matthias, @lionel_rigoux, @chmathys, Liv, Jakob, @stefan_fraessle and Klaas for their support and contributions, to the reviewers for their constructive feedback and to the editorial team @CPSYJournal. 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

RT @Entropy_MDPI: 🧐🧐#Entropy recent Article @alex_j_hess and colleagues propose, test and refine a structural caus….

I am grateful for the support of my co-authors Dina, Liv, Jakob and Klaas and look forward to building onto our findings in future work! 9/9.

All of our analyses were prespecified ( and both data ( and analysis code ( are openly available. 8/9.

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. 7/9.

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. 6/9.

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. 5/9.

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. 4/9

In this work, we focus on a recently emerging computational perspective on fatigue and depression, the allostatic self-efficacy theory (ASE; . 3/9.

What started as a small pet project as part of a course on causality taught by the inspiring Jonas Peters @ETH_en has now become a nice little piece of work summarising my first steps in the realm of causal inference. 2/9.

I am delighted to share the first first-author publication from my PhD @MDPIOpenAccess . 1/9.

RT @katenuss: I’m recruiting PhD students for my (soon-to-launch) lab at Boston University! If you’re interested in the intersection of com….

RT @behrenstimb:

RT @AshleyTyrer: Had an absolute blast as always at #CPCZurich2024 - thanks so much to everyone who came to my DCM for Evoked Responses tut….

RT @KordingLab: Looks like young comp/systems neuro professors would appreciate a summer school. What should they learn? If you are a young….