Predictive Maps in Rats and Humans for Spatial Navigation

Much of our understanding of navigation comes from the study of individual species, often with specific tasks tailored to those species. Here, we provide a novel experimental and analytic framework, integrating across humans, rats and simulated reinforcement learning (RL) agents to interrogate the dynamics of behaviour during spatial navigation. We developed a novel open-field navigation task (Tartarus Maze) requiring dynamic adaptation (shortcuts and detours) to frequently changing obstructions in the path to a hidden goal. Humans and rats were remarkably similar in their trajectories. Both species showed the greatest similarity to RL agents utilising a successor representation, which creates a predictive map. Humans also displayed trajectory features similar to model-based RL agents, which implemented an optimal tree-search planning procedure. Our results help refine models seeking to explain mammalian navigation in dynamic environments, and highlight the utility of modelling the behaviour of different species to uncover the shared mechanisms that support behaviour.