Population coding of strategic variables during foraging in freely-moving macaques

Abstract

Until now it has been difficult to examine the neural bases of foraging in naturalistic environments because previous approaches have relied on restrained animals performing trial-based foraging tasks. Here, we allowed unrestrained monkeys to freely interact with concurrent reward options while we wirelessly recorded population activity in dorsolateral prefrontal cortex (dlPFC). The animals decided when and where to forage, based on whether their prediction of reward was fulfilled or violated. This prediction was not solely based on a history of reward delivery, but also on the understanding that waiting longer improves the chance of reward. The decoded reward dynamics were continuously represented in a subspace of the high-dimensional population activity, and predicted animals subsequent choice better than the true experimental variables and as well as the raw neural activity. Our results indicate that monkeys foraging strategy is based on a cortical model of reward dynamics as animals freely explore their environment.