Abstract The mechanisms by which humans arbitrate between reinforcement-learning strategies remain poorly understood, and the nature of these strategies has come under recent debate. Using a modified two-step decision task in two independent samples (N = 452 and N = 226), here we show that humans integrate three forms of reliability information when arbitrating control: transition reliability, model-free reward-prediction reliability, and model-based reward-prediction reliability.
