Signatures of Reinforcement Learning in Natural Behavior

Abstract

Across myriad real-world contexts, people encounter the challenge of learning to take actions that bring about desirable outcomes. The theoretical framework of reinforcement learning proposes formal algorithms through which agents learn from experience to make rewarding choices. These formal models capture many aspects of reward-guided human behavior in controlled laboratory contexts. Here, we suggest that the algorithms and the constructs (i.e., states, actions, and rewards) formalized within reinforcement-learning theory can be operationally defined and extended to additionally account for learning in complex natural environments. We discuss several recent examples of empirical studies that provide evidence of signatures of reinforcement learning across diverse human behaviors in everyday environments.