Studying neural responses for multi-component economic choices in human and non-human primates using concept-based behavioral choice experiments.

Realistic, everyday rewards contain multiple components, such as taste and size. However, our reward valuations and the associated neural reward signals are single dimensional (vector to scalar transformation). Here, we present a protocol to identify these single-dimensional neural responses for multi-component choice options in humans and monkeys using concept-based behavioral choice experiments.

Monkeys choose as if maximizing utility compatible with basic principles of revealed preference theory.

Revealed preference theory provides axiomatic tools for assessing whether individuals make observable choices "as if" they are maximizing an underlying utility function. The theory evokes a tradeoff between goods whereby individuals improve themselves by trading one good for another good to obtain the best combination. Preferences revealed in these choices are modeled as curves of equal choice (indifference curves) and reflect an underlying process of optimization.