Desktop and Virtual-Reality Training Under Varying Degrees of Task Difficulty in a Complex Search-and-Shoot Scenario

2Citations
Citations of this article
13Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Two-dimensional (2D) desktop and three-dimensional (3D) Virtual-Reality (VR) play a significant role in providing military personnel with training environments to hone their decision-making skills. The nature of the environment (2D versus 3D) and the order of task difficulty (novice to expert or expert to novice) may influence human performance in these environments. However, an empirical evaluation of these environments and their interaction with the order of task difficulty has been less explored. The primary objective of this research was to address this gap and explore the influence of different environments (2D desktop or 3D VR) and order of task difficulty (novice to expert or expert to novice) on human performance. In a lab-based experiment, a total of 60 healthy subjects executed scenarios with novice or expert difficulty levels across both 2D desktop environments (N = 30) and 3D VR environments (N = 30). Within each environment, 15 participants executed the novice scenario first and expert scenario second, and 15 participants executed the expert scenario first and novice scenario second. Results revealed that the participants performed better in the 3D VR environment compared to the 2D desktop environment. Participants performed better due to both expert training (performance in novice second better compared to novice first) and novice training (performance in expert second better compared to expert first). The combination of a 3D VR environment with expert training first and novice training second maximized performance. We expect to use these conclusions for creating effective training environments using VR technology.

Cite

CITATION STYLE

APA

Rao, A. K., Chandra, S., & Dutt, V. (2020). Desktop and Virtual-Reality Training Under Varying Degrees of Task Difficulty in a Complex Search-and-Shoot Scenario. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12428 LNCS, pp. 421–439). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59990-4_31

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free