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The Applied Computational Psychiatry lab focuses on developing computational tools with real potential for clinical applications.

Computational Psychiatry is a multidisciplinary field of research at the intersection of psychiatry, neuroscience, machine learning and statistics. The aim of the field is to harness advances in these fields to advance treatments for mental illnesses. For overviews over computational psychiatry, see Huys et al., 2016 Nature Neuroscience and Huys et al. 2020 Neuropsychopharmacology

When an organ is unable to meet the demands placed on it, illness can arise. As the main functions of the brain are to compute and learn, an understanding of mental illnesses will benefit from an understanding of the computational and learning functions the brain performs, and how these are affected in states of ill-health.

Latest Publications

Onysk, Jakub; Chen, Jiazhou; Huys, Quentin JM

On the computational nature of emotions: insights from metareasoning and transformers Journal Article

In: psyArxiv, 2026.

Links | BibTeX

Serfaty, Jade; Huys, Quentin J. M.

Subjective emotion judgements adhere to principles of Bayesian inference and efficient representation Journal Article

In: psyArxiv, 2026.

Links | BibTeX

Kim, Taekwan; Viding, Essi; Huys, Quentin J M

Reliable detection of longitudinal change incomputational models Journal Article

In: psyArxiv, 2026.

Links | BibTeX

Corlett, Philip R; Huys, Quentin J. M.

IMPACT-ING the practice of computational psychiatry Journal Article

In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2026.

Links | BibTeX

Hall, Anna F; Huys, Quentin J. M.

Goal progress shapes hedonic experience Journal Article

In: psyArxiv, 2026.

Links | BibTeX