Expert Navigators Deploy Rational Complexity-Based Decision Prioritization for Large-Scale Real-World Planning

Abstract

Efficient planning is a distinctive hallmark of human intelligence. Computational analyses of human behavior during planning provide evidence for hierarchically segmented representations of the state space of options. However, such evidence derives from simplistic tasks with small state-spaces that belie the complexity of real-world planning. Here, we examine the street-by-street route plans of London taxi drivers navigating across more than 26,000 streets in London (UK). Response times were faster for states with higher successor representations, providing evidence for predictive mapping. We also find an effect for the interaction between the successor representation of a state and local transition entropy, which indicates hierarchical chunking of transition sequences, and thus provides real-world support to existing theories of hierarchical state-space representations and planning. Finally, we explored how planning unfolded dynamically over different phases of the constructed journey and identify theoretic principles by which expert planners rationally prioritize specific states during the planning process. Overall, our findings provide real-world evidence for predictive maps and rational hierarchical prioritization in expert route planning.