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Quantifying immersion in virtual reality

Pausch, Randy, Proffitt, Dennis, Williams, George·1997·Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97

Abstract

Virtual Reality (VR) has generated much excitement but little formal proof that it is useful. Because VR interfaces are difficult and expensive to build, the computer graphics community needs to be able to predict which applications will benefit from VR. In this paper, we show that users with a VR interface complete a search task faster than users with a stationary monitor and a hand-based input device. We placed users in the center of the virtual room shown in Figure 1 and told them to look for camouflaged targets. VR users did not do significantly better than desktop users. However, when asked to search the room and conclude if a target existed, VR users were substantially better at determining when they had searched the entire room. Desktop users took 41% more time, re-examining areas they had already searched. We also found a positive transfer of training from VR to stationary displays and a negative transfer of training from stationary displays to VR.

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Metadata

Authors
Pausch, R., Proffitt, D., & Williams, G.
Venue
Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97
Publisher
ACM Press
Published
1997
Pages
13-18
OpenAlex
W2056920319
Cited by
339
References
0
FWCI
8.40
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Quantifying immersion in virtual reality. https://app.cochat.ai/papers/10.1145/258734.258744
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Pausch, R., Proffitt, D., & Williams, G. (1997). Quantifying immersion in virtual reality. Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97. https://doi.org/10.1145/258734.258744

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.