Topic: Intrinsic reward

How do humans explore environments with sparse rewards? Recent studies in computational neuroscience have proposed an answer by integrating intrinsic reward signals into traditional reinforcement learning models of human behavior. However, different choices of intrinsic reward result in fundamentally different exploration strategies. Here, we focus on three representative intrinsic reward signals (novelty, surprise, and information-gain) and ask which one explains human exploration best. We design an experimental paradigm for multi-step decision-making where the three representative intrinsic