Learning The Knowledge: How London Taxi Drivers Build Their Cognitive Map of London

Griesbauer, Eva‐Maria; Manley, Ed; Wiener, Jan; Spiers, Hugo J.

doi:10.1101/2021.06.04.447168

Cited by 4 publications

(7 citation statements)

References 72 publications

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“…Individual variation in such skill is not something an app on a mobile device can easily aid our understanding of. It will be important in future work to explore the links between the skill of highly expert navigators such as London taxi drivers (Griesbauer, Manley, Wiener, & Spiers, 2021; Maguire et al., 2006; Spiers & Maguire 2008). Moreover, it would be beneficial to understand how navigation performance in Sea Hero Quest relates to other spatial and nonspatial performance, given evidence that spatial navigation may be distinct from performance on more object‐based spatial tasks (Malanchini et al., 2020), nonspatial association memory (Ngo, Weisberg, Newcombe, & Olson, 2016), or autobiographical memory (Fan, Abdi, & Levine, 2021).…”

Section: Development Of a Worldwide Virtual Assessment Of Navigation ...mentioning

confidence: 99%

Explaining World‐Wide Variation in Navigation Ability from Millions of People: Citizen Science Project Sea Hero Quest

Spiers

Coutrot

Hornberger

2021

Topics in Cognitive Science

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Navigation ability varies widely across humans. Prior studies have reported that being younger and male has an advantage for navigation ability. However, these studies have generally involved small numbers of participants from a handful of western countries. Here we review findings from our project Sea Hero Quest which used a video game for mobile and tablet devices to test 3.9 million people on their navigation ability, sampling across every nation state and from 18-99 years of age. Results revealed that the task has good ecological validity and, across all countries sufficiently sampled (N=63), age is linked to a near linear decline in navigation ability from the early 20s. All countries showed a male advantage, but this varied considerably and could be partly predicted by gender inequality. We found that those reported growing up in a city were on average worse at navigating than those who grew up outside cities, and that navigation performance helped identify those at greater genetic-risk of Alzheimer's disease. We discuss the advantages and challenges of using a mobile app to study cognition and the future avenues for understanding individual differences in navigation ability arising from this research.

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Section: Development Of a Worldwide Virtual Assessment Of Navigation ...mentioning

confidence: 99%

Explaining World‐Wide Variation in Navigation Ability from Millions of People: Citizen Science Project Sea Hero Quest

Spiers

Coutrot

Hornberger

2021

Topics in Cognitive Science

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“…Particularly illuminating could be the work on a parallel form of navigational skill in expert drivers: London cabbies (Spiers and Maguire, 2006 , 2007 ; Griesbauer et al, 2021 ). While vehicle dynamics, performance measures, and environment scale/3D complexity are different in these two domains of expertise, the design of the human memory system is the same, and therefore many of the computational problems and strategies may be the same.…”

Section: Discussionmentioning

confidence: 99%

“…The analysis that follows builds on the interpretation of perceptual-cognitive skills in race driving in Lappi ( 2018 ), which was based on analyzing domain expert knowledge, elicited from content analysis of technical training literature and fitted into the McRuer hierarchy (cf. also the related analysis of expert knowledge through verbal protocol elicitation in London taxi drivers in Griesbauer et al, 2021 ). It was found that one standard way for racing drivers to describe the track is in terms of reference points ( Figure 3 ; see e.g., Code, 1983 , 1986 ).…”

Section: Navigation Guidance and Control: A Cognitive Hierarchymentioning

confidence: 99%

Egocentric Chunking in the Predictive Brain: A Cognitive Basis of Expert Performance in High-Speed Sports

Lappi

2022

Front. Hum. Neurosci.

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What principles and mechanisms allow humans to encode complex 3D information, and how can it be so fast, so accurately and so flexibly transformed into coordinated action? How do these processes work when developed to the limit of human physiological and cognitive capacity—as they are in high-speed sports, such as alpine skiing or motor racing? High-speed sports present not only physical challenges, but present some of the biggest perceptual-cognitive demands for the brain. The skill of these elite athletes is in many ways an attractive model for studying human performance “in the wild”, and its neurocognitive basis. This article presents a framework theory for how these abilities may be realized in high-speed sports. It draws on a careful analysis of the case of the motorsport athlete, as well as theoretical concepts from: (1) cognitive neuroscience of wayfinding, steering, and driving; (2) cognitive psychology of expertise; (3) cognitive modeling and machine learning; (4) human-in-the loop modellling in vehicle system dynamics and human performance engineering; (5) experimental research (in the laboratory and in the field) on human visual guidance. The distinctive contribution is the way these are integrated, and the concept of chunking is used in a novel way to analyze a high-speed sport. The mechanisms invoked are domain-general, and not specific to motorsport or the use of a particular type of vehicle (or any vehicle for that matter); the egocentric chunking hypothesis should therefore apply to any dynamic task that requires similar core skills. It offers a framework for neuroscientists, psychologists, engineers, and computer scientists working in the field of expert sports performance, and may be useful in translating fundamental research into theory-based insight and recommendations for improving real-world elite performance. Specific experimental predictions and applicability of the hypotheses to other sports are discussed.

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“…Taxi drivers were chosen because they are trained on the 'Knowledge of London' (e.g. TFL (n.d.)), which requires them to learn the entire London street network in order to navigate flexibly between places without consulting additional navigation aids (see Griesbauer et al, 2021). Years of training and extensive experience driving a taxi ensure exceptional familiarity within streets and districts in the six-mile area around Charing Cross station (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…This knowledge consists of a profound knowledge of the street network, the street names, the location of places of interest and precise driving instructions (e.g. TFL, n.d.;Lordan, 2018;Electronic Blue Book, 2019;Griesbauer et al, 2021). As a result, licensed London taxi drivers daily navigate across sections of the ∼56,000 streets that form London (OS MasterMap Integrated Transport Network, 2018) without relying on any physical navigation aids.…”

Section: Introductionmentioning

confidence: 99%

What determines a boundary for navigating a complex street network: evidence from London taxi drivers

et al. 2021

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Spatial boundaries play an important role in defining spaces, structuring memory and supporting planning during navigation. Recent models of hierarchical route planning use boundaries to plan efficiently first across regions and then within regions. However, it remains unclear which structures (e.g. parks, rivers, major streets, etc.) will form salient boundaries in real-world cities. This study tested licensed London taxi drivers, who are unique in their ability to navigate London flexibly without physical navigation aids. They were asked to indicate streets they considered as boundaries for London districts or dividing areas. It was found that agreement on boundary streets varied considerably, from some boundaries providing almost no consensus to some boundaries consistently noted as boundaries. Examining the properties of the streets revealed that a key factor in the consistent boundaries was the near rectilinear nature of the designated region (e.g. Mayfair and Soho) and the distinctiveness of parks (e.g. Regent's Park). Surprisingly, the River Thames was not consistently considered as a boundary. These findings provide insight into types of environmental features that lead to the perception of explicit boundaries in large-scale urban space. Because route planning models assume that boundaries are used to segregate the space for efficient planning, these results help make predictions of the likely planning demands of different routes in such complex large-scale street networks. Such predictions could be used to highlight information used for navigation guidance applications to enable more efficient hierarchical planning and learning of large-scale environments.

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Learning The Knowledge: How London Taxi Drivers Build Their Cognitive Map of London

Cited by 4 publications

References 72 publications

Explaining World‐Wide Variation in Navigation Ability from Millions of People: Citizen Science Project Sea Hero Quest

Explaining World‐Wide Variation in Navigation Ability from Millions of People: Citizen Science Project Sea Hero Quest

Egocentric Chunking in the Predictive Brain: A Cognitive Basis of Expert Performance in High-Speed Sports

What determines a boundary for navigating a complex street network: evidence from London taxi drivers

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