In our seventh episode, we turn the tables: co-host Dr Olga Perski joins Christoph as the guest to introduce formal and computational modelling in health psychology.

We discuss the “theory crisis” in psychology and why theories expressed only in natural language can be difficult to test. If a theory cannot generate precise predictions, it can be unclear what evidence would support or challenge it—and what exactly it says about the timing, strength, and direction of psychological processes.

Olga explains what formal and computational modelling involves and how equations and computer simulations can function as practical thinking tools. Together, we explore Theory Construction Methodology and the steps to formalising a theory.

Using smoking cessation and the COMPLAPSE model as an example, we discuss how dynamical models can be developed through interdisciplinary and participatory research. The conversation covers the role of stakeholder and lived-experience input, measurement and time resolution, individual differences, model assumptions, and the potential for models to inform just-in-time adaptive interventions.

If this episode sparked your interest and you would like to explore the topic further, here are some helpful resources:

• Perski, O., & Allen, J. (2025). Formal, dynamical systems modelling to advance health psychology theories: Interdisciplinary working where psychology meets mathematics. European Health Psychologist. https://ehps.net/ehp/index.php/contents/article/view/3478
• Perski, O., Allen, J. M., Kim, M., Pavel, M., Hankonen, N., & Hekler, E. (2026). Using Formal and Computational Modelling to Develop an Initial Within-Person System Dynamics Model of Relapse in Smoking Cessation: A Participatory, Iterative, Multi-Method Approach. Peer Community Journal, 6. 10.24072/pcjournal.802
• https://nicebread.github.io/FOMO-Psy/ - course in Formal Modeling of Psychological Theories
• Oberauer, K., & Lewandowsky, S. (2019). Addressing the theory crisis in psychology. Psychonomic Bulletin & Review, 26(5), 1596-1618. https://doi.org/10.3758/s13423-019-01645-2
• Deterding, S., Guckelsberger, C., Lintunen, E. M., & Ady, N. M. (2026). Advancing Self-Determination Theory via computational modelling: the case of competence and optimal challenge. Motivation and Emotion, 50(1), 80-99. https://doi.org/10.1007/s11031-025-10156-8
• Borsboom, D., Van Der Maas, H. L., Dalege, J., Kievit, R. A., & Haig, B. D. (2021). Theory construction methodology: A practical framework for building theories in psychology. Perspectives on Psychological Science, 16(4), 756-766. 10.1177/1745691620969647

For Episode 5, we invited Louise Poppe to introduce us to causal models, with a particular focus on Directed Acyclic Graphs (DAGs). Together, we discuss why researchers in epidemiology — and, in fact, across many scientific disciplines — can benefit from integrating DAGs into their study workflow. We talk about how to get started with causal thinking, common pitfalls and challenges when building DAGs, and what insights these models can provide for study design, analysis, and interpretation.

This episode offers an accessible introduction to causal thinking with DAGs and is a great opportunity to expand your methodological research toolbox.

If this episode sparked your interest and you would like to explore the topic further, here are some helpful resources

• Poppe, L., Steen, J., Loh, W. W., Crombez, G., De Block, F., Jacobs, N., ... & Paepe, A. L. D. (2025). How to develop causal directed acyclic graphs for observational health research: a scoping review. https://doi.org/10.1080/17437199.2024.2402809
• Poppe, L., De Paepe, A. L., Deforche, B., Van Dyck, D., Loeys, T., & Van Cauwenberg, J. (2025). Experience sampling method studies in physical activity research: the relevance of causal reasoning. https://doi.org/10.1186/s12966-025-01723-w
• Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. https://doi.org/10.1177/251524591774562
• Online Harvard EdX course by Hernán: https://harvardonline.harvard.edu/course/causal-diagrams-draw-your-assumptions-your-conclusions
• Hernán MA, Robins JM (2020). Causal Inference: What If. Causal Inference: What If (the book) — Miguel Hernán
• Tennant, P. W., Murray, E. J., Arnold, K. F., Berrie, L., Fox, M. P., Gadd, S. C., ... & Ellison, G. T. (2021). Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. https://doi.org/10.1093/ije/dyaa213