Game Theory in Neuroscience

Decisions made during social interaction are complex due to the inherent uncertainty about their outcomes, which are jointly determined by the actions of the decision maker and others. Game theory, a mathematical analysis of such interdependent decision making, provides a computational framework to extract core components of complex social situations and to analyze decision making in terms of those quantifiable components. In particular, normative prescription of optimal strategies can be compared to the strategies actually used by humans and animals, thereby providing insights into the nature of observed deviations from prescribed strategies. Here, we review the recent advances in decision neuroscience based on game theoretic approaches, focusing on two major topics. First, a number of studies have uncovered behavioral and neural mechanisms of learning that mediate adaptive decision making during dynamic interactions among decision agents. We highlight multiple learning systems distributed in the cortical and subcortical networks supporting different types of learning during interactive games, such as model-free reinforcement learning and model-based belief learning. Second, numerous studies have investigated the role of social norms, such as fairness, reciprocity and cooperation, in decision making and their representations in the brain. We predict that in combination with sophisticated manipulation of socio-cognitive factors, game theoretic approaches will continue to provide useful tools to understand multifaceted aspects of complex social decision making, including their neural substrates.