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A novel virtual-reality-based route-learning test suite: Assessing the effects of cognitive aging on navigation.

Wiener, J.M., Carroll, D., Moeller, S., Bibi, I., Ivanova, D., Allen, P and Wolbers, T, 2020. A novel virtual-reality-based route-learning test suite: Assessing the effects of cognitive aging on navigation. Behavior Research Methods, 52, 630-640.

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DOI: 10.3758/s13428-019-01264-8

Abstract

Most research groups studying human navigational behavior with virtual environment (VE) technology develop their own tasks and protocols. This makes it difficult to compare results between groups and to create normative data sets for any specific navigational task. Such norms, however, are prerequisites for the use of navigation assessments as diagnostic tools-for example, to support the early and differential diagnosis of atypical aging. Here we start addressing these problems by presenting and evaluating a new navigation test suite that we make freely available to other researchers ( https://osf.io/mx52y/ ). Specifically, we designed three navigational tasks, which are adaptations of earlier published tasks used to study the effects of typical and atypical aging on navigation: a route-repetition task that can be solved using egocentric navigation strategies, and route-retracing and directional-approach tasks that both require allocentric spatial processing. Despite introducing a number of changes to the original tasks to make them look more realistic and ecologically valid, and therefore easy to explain to people unfamiliar with a VE or who have cognitive impairments, we replicated the findings from the original studies. Specifically, we found general age-related declines in navigation performance and additional specific difficulties in tasks that required allocentric processes. These findings demonstrate that our new tasks have task demands similar to those of the original tasks, and are thus suited to be used more widely.

Item Type:Article
ISSN:1554-351X
Uncontrolled Keywords:Cognitive aging; Navigation; Navigation test; Route learning
Group:Faculty of Science & Technology
ID Code:32458
Deposited By: Symplectic RT2
Deposited On:01 Jul 2019 13:31
Last Modified:14 Mar 2022 14:16

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