Expert navigators deploy rational complexity–based decision precaching for large-scale real-world planning - Pablo Fernandez Velasco; Eva-Maria Griesbauer; Iva K. Brunec; Jeremy Morley; Ed Manley; Daniel C. McNamee; Hugo J. Spiers (Proceedings of the National Academy of Sciences, 2025)
Efficient planning is a distinctive hallmark of intelligence in humans, who routinely make rapid inferences over complex world contexts. However, studies investigating how humans accomplish this tend to focus on naive participants engaged in simplistic tasks with small state spaces, which do not reflect the intricacy, ecological validity, and human specialization in real-world planning. In this study, we examine the street-by-street route planning of London taxi drivers navigating across more than 26,000 streets in London (United Kingdom). We explore how planning unfolded dynamically over different phases of journey construction and identify theoretic principles by which these expert human planners rationally precache decisions at prioritized environment states in an early phase of the planning process. In particular, we find that measures of path complexity predict human mental sampling prioritization dynamics independent of alternative measures derived from the real spatial context being navigated. Our data provide real-world evidence for complexity-driven remote state access within internal models and precaching during human expert route planning in very large structured spaces.