Understanding Strategic Motor Learning as a Process of Hypothesis Testing

Abstract

Multiple learning processes contribute to successful goal-directed actions under changing physiological states, biomechanical constraints, and environmental contexts. Among these, explicit strategies enable us to discover new movement patterns when existing ones no longer achieve the desired outcome. Yet, how strategies are discovered during motor learning remains unknown. To address this, we developed a novel behavioral paradigm that isolates strategy discovery in response to a range of visuomotor perturbations. This approach revealed that strategy discovery unfolds through an initial period of systematic exploration across multiple candidate strategies, followed by an 'aha' moment in which behavior converges on a stable solution. To account for these dynamics, we developed a computational model based on hypothesis testing in which learners generate and evaluate visuomotor rules to counteract the perturbation. This hypothesis testing model outperformed a range of alternative accounts, including gradual error reduction, win-stay lose-shift learning, and sudden one-shot insight. Together, these findings identify hypothesis testing as a computational mechanism by which humans discover new strategies during motor learning.