Neural Random Utility: Relating Cardinal Neural Observables to Stochastic Choice Behavior

Abstract

We assess whether a cardinal model can be used to relate neural observables to stochastic choice behavior. We develop a general empirical framework for relating any neural observable to choice prediction and propose a means of benchmarking their predictive power. In a previous study, measurements of neural activity were made while subjects considered consumer goods. Here, we find that neural activity predicts choice behavior with the degree of stochasticity in choice related to the cardinality of the measurement. However, we also find that current methods have a significant degree of measurement error which severely limits their inferential and predictive performance.