Neural Activity Reveals Preferences Without Choices.
We investigate the feasibility of inferring the choices people would make (if given the opportunity) based on their neural responses to the pertinent prospects when they are not engaged in actual decision making. The ability to make such inferences is of potential value when choice data are unavailable, or limited in ways that render standard methods of estimating choice mappings problematic. We formulate prediction models relating choices to "non-choice" neural responses and use them to predict out-of-sample choices for new items and for new groups of individuals. The predictions are sufficiently accurate to establish the feasibility of our approach.
Humanities and Social Sciences, California Instititue of Technology, MC 228-77, Pasadena, CA 91125.
Alec
A
Smith
Department of Economics, Stanford University, Stanford, CA 94305-6072, and NBER.
B Douglas
BD
Bernheim
Humanities and Social Sciences and Computational and Neural Systems, California Instititue of Technology, MC 228-77, Pasadena, CA 91125.
Colin
C
Camerer
Humanities and Social Sciences and Computational and Neural Systems, California Instititue of Technology, MC 228-77, Pasadena, CA 91125.
Antonio
A
Rangel