Skip to main content

London taxi drivers: A review of neurocognitive studies and an exploration of how they build their cognitive map of London.

Griesbauer, E-M., Manley, E., Wiener, J.M. and Spiers, H.J., 2022. London taxi drivers: A review of neurocognitive studies and an exploration of how they build their cognitive map of London. Hippocampus, 32 (1), 3-20.

Full text available as:

[img]
Preview
PDF (OPEN ACCESS ARTICLE)
Hippocampus - 2021 - Griesbauer - London taxi drivers A review of neurocognitive studies and an exploration of how they.pdf - Published Version
Available under License Creative Commons Attribution.

5MB

DOI: 10.1002/hipo.23395

Abstract

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.

Item Type:Article
ISSN:1050-9631
Additional Information:Research Funding Engineering and Physical Sciences Research Council Ordnance Survey
Uncontrolled Keywords:cognitive maps ; learning strategies ; navigation ; spatial cognition ; spatial learning ; wayfinding
Group:Faculty of Science & Technology
ID Code:36400
Deposited By: Symplectic RT2
Deposited On:20 Dec 2021 11:16
Last Modified:14 Mar 2022 14:31

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -