Modern neuroscience increasingly recognizes that human behavior emerges from intricate interactions between internal cognitive processes and external environmental signals. By leveraging digital behavioral data—everything from social media interactions to mobile device patterns—researchers can develop more holistic models of mental wellness that respect individual variation and contextual complexity.
This research represents a critical shift toward understanding mental health as a dynamic, contextual experience rather than a static condition. For Indigenous communities and other populations traditionally marginalized by clinical research, computational approaches that honor lived experience could revolutionize how we conceptualize psychological resilience. Tracking real-world behavioral patterns allows us to move beyond simplified diagnostic frameworks and recognize the rich adaptive strategies humans develop in response to complex social environments.
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.