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

Abstract

ABSTRACTTo optimize their foraging strategy, animals must continuously make decisions about where to look for food and when to move between locations of possible food sources. Until now it was 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). Although the relevant reward dynamics were hidden from the animals, they were nonetheless encoded in the population activity and helped predict foraging choices. Surprisingly, the decoded reward dynamics were represented in a subspace of the high-dimensional population activity, and predicted animal’s subsequent choice better than either the true experimental variables or the raw neural responses. Our results indicate that monkeys’ foraging strategy is based on a cortical model of reward dynamics as animals freely explore their environment.