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

Authors: Griesbauer, E.M., Manley, E., Wiener, J.M. and Spiers, H.J.

Journal: Hippocampus

Volume: 32

Issue: 1

Pages: 3-20

eISSN: 1098-1063

ISSN: 1050-9631

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.

http://eprints.bournemouth.ac.uk/36400/

Source: Scopus

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

Authors: Griesbauer, E.-M., Manley, E., Wiener, J.M. and Spiers, H.J.

Journal: Hippocampus

Volume: 32

Issue: 1

Pages: 3-20

eISSN: 1098-1063

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.

http://eprints.bournemouth.ac.uk/36400/

Source: PubMed

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

Authors: Griesbauer, E.-M., Manley, E., Wiener, J.M. and Spiers, H.J.

Journal: HIPPOCAMPUS

Volume: 32

Issue: 1

Pages: 3-20

eISSN: 1098-1063

ISSN: 1050-9631

DOI: 10.1002/hipo.23395

http://eprints.bournemouth.ac.uk/36400/

Source: Web of Science (Lite)

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

Authors: Griesbauer, E.-M., Manley, E., Wiener, J.M. and Spiers, H.J.

Journal: Hippocampus

Volume: 32

Issue: 1

Pages: 3-20

eISSN: 1098-1063

ISSN: 1050-9631

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.

http://eprints.bournemouth.ac.uk/36400/

Source: Europe PubMed Central

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

Authors: Griesbauer, E.-M., Manley, E., Wiener, J.M. and Spiers, H.J.

Journal: Hippocampus

Volume: 32

Issue: 1

Pages: 3-20

ISSN: 1050-9631

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.

http://eprints.bournemouth.ac.uk/36400/

Source: BURO EPrints