Department of Experimental Psychology, Division of Psychology and Language Sciences, Institute of Behavioural Neuroscience, University College London, London, UK.
Centre for Advanced Spatial Analysis, University College London, London, UK.
Hippocampus. 2022 Jan;32(1):3-20. doi: 10.1002/hipo.23395. Epub 2021 Dec 16.
Licensed London taxi drivers have been found to show changes in the gray matter density of their hippocampus over the course of training and decades of navigation in London (UK). This has been linked to their learning and using of the "Knowledge of London," the names and layout of over 26,000 streets and thousands of points of interest in London. Here we review past behavioral and neuroimaging studies of London taxi drivers, covering the structural differences in hippocampal gray matter density and brain dynamics associated with navigating London. We examine the process by which they learn the layout of London, detailing the key learning steps: systematic study of maps, travel on selected overlapping routes, the mental visualization of places and the optimal use of subgoals. Our analysis provides the first map of the street network covered by the routes used to learn the network, allowing insight into where there are gaps in this network. The methods described could be widely applied to aid spatial learning in the general population and may provide insights for artificial intelligence systems to efficiently learn new environments.
研究发现，经过培训和在伦敦（英国）导航几十年后，伦敦持照出租车司机的海马体灰质密度会发生变化。这与他们学习和使用“伦敦知识”有关，“伦敦知识”包括伦敦 26000 多条街道和数千个兴趣点的名称和布局。在这里，我们回顾了过去关于伦敦出租车司机的行为和神经影像学研究，涵盖了与在伦敦导航相关的海马体灰质密度和大脑动态的结构差异。我们研究了他们学习伦敦布局的过程，详细介绍了关键的学习步骤：系统地研究地图、在选定的重叠路线上行驶、对地点的心理可视化以及对子目标的最佳利用。我们的分析提供了用于学习网络的路线所涵盖的街道网络的第一张地图，使我们能够深入了解网络中存在的空白。所描述的方法可以广泛应用于帮助普通人群进行空间学习，并为人工智能系统提供有关如何有效学习新环境的见解。
