Skip to main content

Virtual navigation tested on a mobile app is predictive of real-world wayfinding navigation performance.

Coutrot, A., Schmidt, S., Coutrot, L., Pittman, J., Hong, L., Wiener, J. M., Hölscher, C., Dalton, R. C., Hornberger, M. and Spiers, H. J., 2019. Virtual navigation tested on a mobile app is predictive of real-world wayfinding navigation performance. PLoS One, 14 (3), e0213272.

Full text available as:

[img]
Preview
PDF (OPEN ACCESS ARTICLE)
journal.pone.0213272.pdf - Published Version
Available under License Creative Commons Attribution.

2MB

DOI: 10.1371/journal.pone.0213272

Abstract

Virtual reality environments presented on tablets and smartphones have potential to aid the early diagnosis of conditions such as Alzheimer's dementia by quantifying impairments in navigation performance. However, it is unclear whether performance on mobile devices can predict navigation errors in the real world. We compared the performance of 49 participants (25 females, 18-35 years old) at wayfinding and path integration tasks designed in our mobile app 'Sea Hero Quest' with their performance at similar tasks in a real-world environment. We first performed this experiment in the streets of London (UK) and replicated it in Paris (France). In both cities, we found a significant correlation between virtual and real-world wayfinding performance and a male advantage in both environments, although smaller in the real world (Cohen's d in the game = 0.89, in the real world = 0.59). Results in London and Paris were highly similar, and controlling for familiarity with video games did not change the results. The strength of the correlation between real world and virtual environment increased with the difficulty of the virtual wayfinding task, indicating that Sea Hero Quest does not merely capture video gaming skills. The fact that the Sea Hero Quest wayfinding task has real-world ecological validity constitutes a step toward controllable, sensitive, safe, low-cost, and easy to administer digital cognitive assessment of navigation ability.

Item Type:Article
ISSN:1932-6203
Group:Faculty of Science & Technology
ID Code:32100
Deposited By: Symplectic RT2
Deposited On:27 Mar 2019 15:39
Last Modified:14 Mar 2022 14:15

Downloads

Downloads per month over past year

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