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Ouvrai opens access to remote virtual reality studies of human behavioural neuroscience


Modern virtual reality (VR) devices record six-degree-of-freedom kinematic data with high spatial and temporal resolution and display high-resolution stereoscopic three-dimensional graphics. These capabilities make VR a powerful tool for many types of behavioural research, including studies of sensorimotor, perceptual and cognitive functions. Here we introduce Ouvrai, an open-source solution that facilitates the design and execution of remote VR studies, capitalizing on the surge in VR headset ownership. This tool allows researchers to develop sophisticated experiments using cutting-edge web technologies such as WebXR to enable browser-based VR, without compromising on experimental design. Ouvrai’s features include easy installation, intuitive JavaScript templates, a component library managing front- and backend processes and a streamlined workflow. It integrates with Firebase, Prolific and Amazon Mechanical Turk and provides data processing utilities for analysis. Unlike other tools, Ouvrai remains free, with researchers managing their web hosting and cloud database via personal Firebase accounts. Ouvrai is not limited to VR studies; researchers can also develop and run desktop or touchscreen studies using the same streamlined workflow. Through three distinct motor learning experiments, we confirm Ouvrai’s efficiency and viability for conducting remote VR studies.

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Fig. 1: A schematic of Ouvrai infrastructure.
Fig. 2: A portion of the FSM for experiment 1, represented as a flow chart.
Fig. 3: Experiment 1: spontaneous recovery.
Fig. 4: Experiment 2: dual adaptation with control points.
Fig. 5: Experiment 3: 3D generalization of visuomotor learning.
Fig. 6: Additional features of Ouvrai.

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Data availability

Raw data in JSON format, preprocessed data in XLSX format and Python analysis code are available on GitHub for experiment 1, experiment 2 and experiment 3. Analysis code relies on the Python data analysis utilities that come with the Ouvrai source code on GitHub.

Code availability

The production version of the study code as well as Python analysis code for experiments 1–3 is available in Jupyter notebooks at the following links: experiment 1, experiment 2 and experiment 3. The complete source code of Ouvrai is available at The Ouvrai GitHub repository will be updated as new features are developed. Previous releases can be accessed via the commit history of the main branch.


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This work was supported by the National Institutes of Health (R01NS117699 to D.M.W.) and the Air Force Office of Scientific Research under award (FA9550-22-1-0337 to D.M.W.). We thank Z. Zhang and A. Löffler for helpful discussions and testing of Ouvrai. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations



E.C., J.N.I. and D.M.W. conceived Ouvrai as a software toolbox for developing crowdsourced experiments on motor control and learning. E.C. created and developed Ouvrai, with assistance from S.S. and J.N.I. All authors conceived of the design of experiments 1–3. E.C. implemented, conducted and analysed experiments 1–3. All authors provided feedback on the presentation of the results, wrote and revised the manuscript.

Corresponding author

Correspondence to Evan Cesanek.

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Competing interests

D.M.W. is a consultant to CTRL-Labs Inc., in the Reality Labs Division of Meta. This entity did not support or influence this work. The authors declare no other competing interests.

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Nature Human Behaviour thanks Gavin Buckingham, Marta Russo, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary Figs. 1–2, Firebase Realtime Database and FSM.

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Cesanek, E., Shivkumar, S., Ingram, J.N. et al. Ouvrai opens access to remote virtual reality studies of human behavioural neuroscience. Nat Hum Behav 8, 1209–1224 (2024).

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