The neurotransmitter dopamine has been found to play a crucial role in choice, learning, and belief formation. The best-developed current theory of dopaminergic function is the “reward prediction error” hypothesis—that dopamine encodes the difference between the experienced and predicted “reward” of an event. We provide axiomatic foundations for this hypothesis to help bridge the current conceptual gap between neuroscience and economics. Continued research in this area of overlap between social and natural science promises to overhaul our understanding of how beliefs and preferences are formed, how they evolve, and how they play out in the act of choice.

How and to what extent “neuroeconomic” data (broadly interpreted as data other than standard choice data) should be used in advancing economic theory is open to question. Several authors have attempted to make use of such nonstandard data to shed light on the process of economic decision making. John W. Payne, James R. Bettman, and Eric. J. Johnson (1993), Miguel Costa Gomes, Vincent P. Crawford, and Bruno Broseta (2001), and Xavier Gabaix et al. (2006) have used MouseLab software in order to determine the manner in which people use information. Joseph Wang, Michael Spezio, and Colin Camerer (2006) make use of eye-tracking data for the same purpose. More dramatically, researchers such as Paul William Glimcher, Joseph Kable, and Kenway Louie (2007) are using brain-scanning data in an attempt to constrain economic models of discounting and time preference. Camerer (forthcoming) presents an excellent review of economic research involving nonstandard data. In opposition to this trend, Faruk Gul and Wolfgang Pesendorfer (forthcoming) present a strong critique of the use of nonchoice data within economics. They put forward two specific arguments that users of “neuroeconomic” data must refute if their work is to be taken seriously. First—economic models were designed only to explain choices. Thus, nonchoice data can be used neither to confirm nor deny a particular economic model. Second, it is by and large true that economists are interested in choice behavior. Any two models will either make different predictions for choice, in which case they can be differentiated by standard choice data, or they will not, in which case an economist will not be interested in differentiating between them. Economic Insights from “Neuroeconomic” Data