Fernandez Velasco, P. | bioRxiv Science

bioRxiv2024

Expert Navigators Deploy Rational Hierarchical Priorization Over Predictive Maps For Large-Scale Real-World Planning

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 specialisation 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 (UK). 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 prioritised environment states in an early phase of the planning process. In particular, we find that measures of path complexity predict human mental sampling prioritisation 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. Significance statementHumans can plan efficiently in incredibly complex situations. Existing work has looked at naive participants in simple tasks, which might not be representative of how experts plan in the real world. Here, we study the real-world planning process of London taxi drivers - famous for their expert knowledge of London. By analyzing their response times as a proxy for thinking times, we reveal that at an early stage in their thought process, they store decisions at key street junctions to keep them in mind for later planning. Using computational modeling, we show that taxi drivers prioritize inference at street junctions according to normative metrics measuring how critical a particular decision is for reducing the complexity of planning across the entire city.

neuroscience↗

bioRxiv2024

London taxi drivers exploit neighbourhood boundaries for hierarchical route planning

Humans show an impressive ability to plan over complex situations and environments. A classic approach to explaining such planning has been tree-search algorithms which search through alternative state sequences for the most efficient path through states. However, this approach fails when the number of states is large due to the time to compute all possible sequences. Hierarchical route planning has been proposed as an alternative, offering a computationally efficient mechanism in which the representation of the environment is segregated into clusters. Current evidence for hierarchical planning comes from experimentally created environments which have clearly defined boundaries and far fewer states than the real-world. To test for real-world hierarchical planning we exploited the capacity of London licensed taxi drivers to use their memory to construct a street by street plan across London, UK (>26,000 streets). The time to recall each successive street name was treated as the response time, with a rapid average of 1.8 seconds between each street. In support of hierarchical planning we find that the clustered structure of Londons regions impacts the response times, with minimal impact of the distance across the street network (as would be predicted by tree-search). We also find that changing direction during the plan (e.g. turning left or right) is associated with delayed response times. Thus, our results provide real-world evidence for how humans structure planning over a very large number of states, and give a measure of human expertise in planning.

neuroscience↗