Closed-Loop Scientific Discovery in the Behavioral Sciences

Closed-loop scientific discovery represents a transformative approach to empirical research by automating the continuous cycle of data collection, modeling, and experimental design, all aimed at generating new scientific knowledge. This chapter introduces closed-loop discovery as a paradigm for behavioral research, detailing both its potential to advance our understanding of human behavior and the challenges it faces. We begin by formalizing the behavioral research process as an iteration between data collection, computational inference, and experimental design. We then introduce AutoRA, an open-source framework designed for automating various steps of empirical research, and showcase its utility for advancing our understanding of human behavior, both in terms of discovering scientific models and guiding novel experiments. We conclude by addressing the specific challenges that closed-loop discovery encounters in the behavioral sciences and by outlining prospective paths for research, with the aim of refining closed-loop discovery systems to more effectively contribute to our understanding of human behavior.