State-dependent Online Reactivations for Different Learning Strategies in Foraging

Abstract

Reactivation of neural responses associated with navigation is thought to facilitate learning. We wondered whether reactivation is subject to contextual control, meaning that different types of learning promote different reactivation patterns. We trained macaques to forage in a first-person virtual maze and identified two distinct learning states prioritizing reward and information using unsupervised ethogramming based on low-level features. In orbitofrontal (OFC) and retrosplenial (RSC) cortices, representations of the goal, the path towards it, and recently traveled paths were strongly reactivated - online - during reward-prioritizing choices. During learning, reactivation of optimal paths increased in RSC after reward-prioritizing choices, and reactivation of uninformative paths decreased in RSC and OFC after information-prioritizing choices. Reactivation in OFC selectively covaried with ongoing RSC activity when prioritizing information; vice versa during prioritizing reward. These results highlight that cognitive states can drive learning and reactivation patterns can be tailored to the needs of the moment.