A core strength of computational psychiatry is its focus on theory-driven research, in which cognitive processes are precisely quantified using computational models that formalize specific theoretical mechanisms. However, the data used in these studies often come from traditional laboratory-based cognitive tasks, which have unclear ecological validity. In this review we propose that the same theoretical frameworks and computational models can be applied to real-world data such as experience sampling, passive data, and digital-behavior data (e.g., online activity such as on social media). In turn, modeling real-world data can benefit from a theory-driven computational approach to move from purely predictive to explanatory power. We illustrate these points using emerging studies and discuss the challenges and opportunities of using real-world data in computational psychiatry.

Human exploration is driven by two distinct neural mechanisms, a valence-independent rate signal and a valence-dependent global noise signal.

Individual dopaminergic neurons encode future rewards over distinct temporal horizons.

Reinforcement learning task-based behavioral and computational measures displayed low test–retest reliability at the individual level. Also in contrast to self-assessed personality measures, behaviora...

The brain and body are necessarily connected. Here the authors show that brain blood flow and electrical activity are coupled with systemic physiological changes in the body.