Virtual Reality is Better Than Desktop for Training a Spatial Knowledge Task, but Not for Everyone

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Abstract

Advances in virtual reality (VR) technology have resulted in the ability to explore high-resolution immersive environments, which seem particularly useful for training spatial knowledge tasks. However, empirical research on the effectiveness of training in VR, including for spatial knowledge-based tasks, has yielded mixed results. One potential explanation for this discrepancy is that key individual characteristics may account for differences in who benefits most from VR-based training. Previous research has suggested that immersive VR imposes high cognitive load on learners and thus impedes learning, but the amount of cognitive load experienced may be dependent on an individual’s video-game experience (VGE). Therefore, the goal of this experiment was to explore the effects of VGE on learning in VR versus a desktop-based training environment, since VGE has been demonstrated to affect performance in previous spatial navigation studies in virtual environments. In this experiment, 62 participants trained in a virtual scavenger hunt task to learn the locations of different equipment in a submarine’s machinery room. After training, participants’ spatial knowledge was assessed in a drawing task of the room’s layout. The results showed no differences overall for experimental condition (i.e., Desktop or VR) or VGE, but there was a significant interaction between these two variables. The high-VGE participants in the VR condition outperformed low-VGE participants in both the Desktop and VR conditions. This suggests that VR may be particularly useful for training experienced gamers, but both VR and Desktop seem to be equally effective for less experienced gamers in a spatial task.

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APA

Marraffino, M. D., Johnson, C. I., & Garibaldi, A. E. (2022). Virtual Reality is Better Than Desktop for Training a Spatial Knowledge Task, but Not for Everyone. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13317 LNCS, pp. 212–223). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-05939-1_14

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