Disambiguating planning from heuristics in rodent spatial navigation

Abstract

A longstanding question in neuroscience is how animals and humans select actions in complex decision trees. Planning, the evaluation of action sequences by anticipating their outcomes, is thought to coexist in the brain with simpler decision-making strategies, such as habit learning and heuristics. Though planning is often required for optimal choice, for many problems simpler strategies yield similar decisions, making them difficult to disambiguate. The scarcity of behavioral tasks that can dissociate planning from other decision mechanisms while generating rich decision data has hindered our understanding of the neural basis of planning. We developed a novel navigation task in which mice navigate to cued goal locations in a complex maze. A targeted search through the large space of possible maze layouts in that environment maximizes the number of decisions that are informative about the use of planning. Over the course of training mice learn shorter paths to goals, and the individual decisions composing these paths are better accounted for by planning than vector navigation. With hundreds of informative decisions per behavioral session, this paradigm opens the door to the study of the neural basis of route planning.