Fernandez Velasco Pablo, Griesbauer Eva-Maria, Brunec Iva K, Morley Jeremy, Manley Ed, McNamee Daniel C, Spiers Hugo J
Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, London WC1H 0AP, United Kingdom.
Department of Philosophy, University of York, York YO10 5DD, United Kingdom.
Proc Natl Acad Sci U S A. 2025 Jan 28;122(4):e2407814122. doi: 10.1073/pnas.2407814122. Epub 2025 Jan 23.
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.
高效规划是人类智能的一个显著标志，人类通常会在复杂的世界环境中迅速做出推断。然而，研究人类如何做到这一点的研究往往集中在参与具有小状态空间的简单任务的新手参与者身上，这些任务无法反映现实世界规划中的复杂性、生态效度和人类专业性。在本研究中，我们考察了伦敦出租车司机在英国伦敦26000多条街道上逐街规划路线的情况。我们探究了规划在行程构建的不同阶段是如何动态展开的，并确定了理论原则，即这些专业的人类规划者在规划过程的早期阶段，如何在优先的环境状态下合理地预先缓存决策。特别是，我们发现路径复杂性的度量能够预测人类心理采样的优先级动态，而与从所导航的实际空间背景中得出的其他度量无关。我们的数据为在非常大的结构化空间中进行人类专家路线规划时，内部模型中由复杂性驱动的远程状态访问和预先缓存提供了现实世界的证据。
