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Running virtual reality experiments online: A brief introduction and tutorial
Levi Kumle1, Alfie Brazier2, Joel Kovoor2
1Department of Experimental Psychology, University of Oxford, Oxford, UK. levi.kumle@psy.ox.ac.uk.
Behavior Research Methods
|April 29, 2026
Summary
This study presents a practical method for conducting virtual reality (VR) experiments online. The developed tools and tutorial enable scalable, representative, and externally valid behavioral research using VR technology.
Area of Science:
- Cognitive and Behavioral Science
- Human-Computer Interaction
- Virtual Reality Applications
Background:
- Conducting representative cognitive and behavioral research requires both realistic experimental settings and diverse participant samples.
- Virtual reality (VR) enhances experimental realism and control, while online testing expands participant pools.
- Integrating VR with online testing is compelling but faces technical challenges due to tool incompatibilities.
Purpose of the Study:
- To provide a practical solution for conducting virtual reality (VR) experiments online.
- To bridge the gap between lab-based VR studies and remote data collection.
- To facilitate scalable, inclusive, and externally valid behavioral science research.
Main Methods:
- Developed a step-by-step tutorial for converting lab-based VR studies to remote deployments using Unity and Steam.
- Created an open-source onlineVR-toolbox with code, workflows, and Notebooks for participant management and data transfer.
- Validated the approach through a proof-of-concept case study replicating a known memory effect.
Main Results:
- Demonstrated the technical feasibility of remote data collection for VR experiments via Steam.
- Showcased that online VR study results are consistent with traditional laboratory-based findings.
- Validated the effectiveness of the open-source toolbox in facilitating online VR research.
Conclusions:
- Online VR studies are both possible and practical, overcoming previous technical hurdles.
- The developed resources enable more scalable, inclusive, and externally valid behavioral science.
- This approach opens new avenues for diverse and representative data collection in cognitive and behavioral research.

