Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.game theory | neuroeconomics | computational modeling | functional MRI D ecision-making in the presence of competitive intelligent agents is fundamental for social and economic behavior (1, 2). Here, in addition to learning about rewards and punishments available in the environment, agents also need to anticipate and respond to actions of others competing for the same rewards. This ability to behave strategically has been the subject of intense study in theoretical biology and game theory (1, 2). However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. We studied neural computations underlying learning in a stylized but well-characterized setting of a population with many anonymously interacting agents and low probability of reencounter. This setting provides a natural model for situations such as commuters in traffic or bargaining in bazaars (1). Importantly, in minimizing the role of reputation and higher-order belief considerations, the population setting using a random matching protocol is perhaps the most widely studied experimental setting and has served as a basic building block for a number of models in evolutionary biology and game theory (1, 2).Behaviorally, there is substantial evidence that strategic learning can be parsimoniously characterized by using two l...