Cognitive Modeling of Real-World Behavior for Understanding Mental Health

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 lab-based cognitive tasks, which have unclear ecological validity. Here, we argue that the same theoretical frameworks and computational models can be applied to real-world data such as experience sampling, passive and/or 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 challenges and opportunities of using real-world data in computational psychiatry.