Goal-directed navigation strategies in humans and deep meta-learning agents

Much has been learned about the cognitive and neural mechanisms by which humans and other animals navigate to reach their goals. However, most studies have involved a single, well-learned environment. By contrast, real-world wayfinding often occurs in unfamiliar settings, requiring people to combine memories of landmark locations with on-the-fly information about transitions between adjacent states. Here, we studied the strategies that support human navigation in wholly novel environments. We found that during goal-directed navigation, people use a mix of strategies, adaptively deploying both associations between proximal states (transitions) and directions between distal landmarks (vectors) at stereotyped points on a journey. Deep neural networks meta-trained with reinforcement learning to find the shortest path to goal exhibited near-identical strategies, and in doing so, developed units specialised for the implementation of vector- and transition-based strategies. These units coded for spatial positions, landmarks, and their conjunction. This suggests that optimal navigation in novel environments relies on an adaptive mix of transition- and vector-based strategies.