Generative Cognitive Maps for Structure-Sensitive Planning

Existing models of human planning generally explain human behavior through limited planning depth or resource-rational constraints on computation. While such models are agnostic to environmental structure, real-world environments such as natural landscapes and building interiors often contain repeated or compositional structure. Here we test experimentally whether human planning exploits such structure, finding strong structure sensitivity that is not captured by current state-of-the-art models of human planning. To instantiate this principle computationally, we propose GenPlan, a computational framework that shows how structure sensitivity can be implemented in practice through compositional generative map representations and reusable policies. The framework integrates (1) a Generative Map Module that infers latent structural regularities, and (2) a Structure-Based Planner that exploits them for hierarchical planning. We show that GenPlan captures sensitivity to structure empirically observed in human behavior, while reducing planning cost relative to structure-agnostic baselines with only a bounded performance penalty. Together, these results establish structural sensitivity as an important principle of human planning and demonstrate how it can be incorporated into resource-efficient planning algorithms.