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Top Cogn Sci. 2015 Jul;7(3):391-415. doi: 10.1111/tops.12138. Epub 2015 Mar 23.

Novelty and Inductive Generalization in Human Reinforcement Learning.

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Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.
Princeton Neuroscience Institute and Department of Psychology, Princeton University.


In reinforcement learning (RL), a decision maker searching for the most rewarding option is often faced with the question: What is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: How can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and we describe an equivalence between the Bayesian model and temporal difference learning algorithms that have been proposed as models of RL in humans and animals. According to our view, the search for the best option is guided by abstract knowledge about the relationships between different options in an environment, resulting in greater search efficiency compared to traditional RL algorithms previously applied to human cognition. In two behavioral experiments, we test several predictions of our model, providing evidence that humans learn and exploit structured inductive knowledge to make predictions about novel options. In light of this model, we suggest a new interpretation of dopaminergic responses to novelty.


Bayesian inference; Exploration-exploitation dilemma; Neophilia; Neophobia; Reinforcement learning

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