A neuronal prospect theory model in the brain reward circuitry

Prospect theory, arguably the most prominent theory of choice, is an obvious candidate for neural valuation models. How the activity of individual neurons, a possible computational unit, obeys prospect theory remains unknown. Here, we show, with theoretical accuracy equivalent to that of human neuroimaging studies, that single-neuron activity in four core reward-related cortical and subcortical regions represents the subjective valuation of risky gambles in monkeys. The activity of individual neurons in monkeys passively viewing a lottery reflects the desirability of probabilistic rewards parameterized as a multiplicative combination of utility and probability weighting functions, as in the prospect theory framework. The diverse patterns of valuation signals were not localized but distributed throughout most parts of the reward circuitry. A network model aggregating these signals reconstructed the risk preferences and subjective probability weighting revealed by the animals' choices. Thus, distributed neural coding explains the computation of subjective valuations under risk.

Automated summary

This study demonstrates that the activity of individual neurons in reward-related regions can represent subjective valuations, consistent with prospect theory. A network model that aggregates these signals can reconstruct an animal's risk preferences and subjective probability weighting.