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Evaluating the effects of real world distraction on user performance in virtual environments
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Source Virtual Reality Software and Technology archive
Proceedings of the ACM symposium on Virtual reality software and technology table of contents
Limassol, Cyprus
SESSION: Human factors I table of contents
Pages: 19 - 26  
Year of Publication: 2006
ISBN:1-59593-321-2
Authors
Yi Wang  Virginia Tech, Blacksburg, VA
Kunmi Otitoju  Virginia Tech, Blacksburg, VA
Tong Liu  Virginia Tech, Blacksburg, VA
Sijung Kim  Virginia Tech, Blacksburg, VA
Doug A. Bowman  Virginia Tech, Blacksburg, VA
Sponsors
ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
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ABSTRACT

Although many virtual environment (VE) technologies such as the four-screen CAVE are described as immersive, users can still perceive distractions from the real world. This exposure to real-world distraction may reduce users' sense of presence, and if presence is correlated with performance as some have claimed, the real-world distractions may also hinder performance. Thus, VE designers may want to consider ways to reduce real-world distraction. This paper presents an experiment to investigate the effect of reduced visual stimulus in the peripheral area on user performance and the usability of an immersive VE. We carefully designed three tasks that cause different levels of awareness of the real-world distraction. Using these tasks, we evaluated users' performance and preference in two conditions. The low-stimulus condition was created by hanging a black cloth across the missing back wall of a CAVE. The high-stimulus condition was created by projected animations and real human motion outside the CAVE. The experiments show that reduced distraction may have a positive or negative effect on user performance, depending on the specific tasks and environments.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Yi Wang: colleagues
Kunmi Otitoju: colleagues
Tong Liu: colleagues
Sijung Kim: colleagues
Doug A. Bowman: colleagues