Emerging Topics in Computational Psychiatric Research: Clarity Through Complexity?

Computational psychiatry refers to an interdisciplinary area of research in psychiatry that broadly seeks to bridge different levels of biobehavioral inference via “computation.” Initially emphasizing theory-driven approaches to uncovering mechanisms of disease anchored on cognitive models of learning and decision making, the field was quickly broadened to include a more theory-agnostic, and data-driven, branch that emphasized prediction through the application of machine learning. A decade on, what comprises computational psychiatry has substantially evolved, and the lines between theory- and data-driven approaches have become blurred in favor of more hybrid, and increasingly finely derived—albeit ultimately complex and abstracted—approaches.