Revisiting the Role of Uncertainty-Driven Exploration in a (Perceived) Non-Stationary World.

Humans are often faced with an exploration-versus-exploitation trade-off. A commonly used paradigm, multi-armed bandit, has shown humans to exhibit an "uncertainty bonus", which combines with estimated reward to drive exploration. However, previous studies often modeled belief updating using either a Bayesian model that assumed the reward contingency to remain stationary, or a reinforcement learning model. Separately, we previously showed that human learning in the bandit task is best captured by a dynamic-belief Bayesian model. We hypothesize that the estimated uncertainty bonus may depend on which learning model is employed. Here, we re-analyze a bandit dataset using all three learning models. We find that the dynamic-belief model captures human choice behavior best, while also uncovering a much larger uncertainty bonus than the other models. More broadly, our results also emphasize the importance of an appropriate learning model, as it is crucial for correctly characterizing the processes underlying human decision making.