Learning in Games : Neural Computations underlying Strategic Learning

The past decade has witnessed an unprecedented growth in our understanding of the brain basis of economic decision-making. In particular, research is uncovering not only the location of brain regions where certain processes are taking place, but also the nature of the (economically meaningful) latent variables that are represented, as well as how they relate to behavior. This transition from understanding where to how economic decisions are being made in the brain has been integral to relating neural processes to economic models of behavior. This progress, however, has been notably uneven. Neuroeconomic studies of individual decision-making, such as those involve risk and time preferences, have the benefit of drawing on decades of work from neuroscientific studies of animal behavior. Critically, many of these findings are based on quantitative, computational approach that lends well to economic experimentation. In contrast, our understanding of the neural systems underlying social behavior is much less specific. A large measure of the current challenge in fact arises from the empirical shortcomings of standard game theoretic predictions of behavior, which are largely equilibrium-based. Using our own study as an example, we show how one can directly search for the latent variables implied by current economic models of strategic learning, and attempt to localize them in the brain. Specifically, we show that the neural systems underlying strategic learning build directly on top of those involved in simple trial-and-error learning, but incorporate additional computations that capture belief-based learning. Finally, we discuss how our approach can be extended to address fundamental problems in economics. JEL Classification: C92, D83.