Bringing precision to the understanding and treatment of mental disorders requires
instruments for studying clinically relevant individual differences. One promising approach is …

Cognitive tasks are capable of providing researchers with crucial insights into the
relationship between cognitive processing and psychiatric phenomena. However, many …

Importance Computational psychiatry studies have investigated how reinforcement learning
may be different in individuals with mood and anxiety disorders compared with control …

Distinct model-free and model-based learning processes are thought to drive both typical
and dysfunctional behaviours. Data from two-stage decision tasks have seemingly shown …

Computational psychiatry is a rapidly growing field attempting to translate advances in
computational neuroscience and machine learning into improved outcomes for patients …

Computational models have great potential to revolutionise psychiatry research and clinical
practice. These models are now used across multiple subfields, including computational …

Most psychiatric disorders do not occur in isolation, and most psychiatric symptom
dimensions are not uniquely expressed within a single diagnostic category. Current …

Dopamine fundamentally contributes to reinforcement learning, but recent accounts also
suggest a contribution to specific action selection mechanisms and the regulation of …

Background Computational models show great promise in mapping latent decision-making
processes onto dissociable neural substrates and clinical phenotypes. One prominent …

The transition from adolescence to adulthood is a period when ongoing brain development
coincides with a substantially increased risk of psychiatric disorders. The developmental …