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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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Exploring and Modeling Directional Effects on Steering Behavior in Virtual Reality.

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    This study introduces the SθModel to predict user steering behavior in virtual reality (VR). The model accurately predicts movement time and speed, improving upon the original Steering Law for VR environments.

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    Area of Science:

    • Human-Computer Interaction
    • Virtual Reality
    • Usability Engineering

    Background:

    • Steering tasks are crucial in virtual reality (VR) for interactions like menu adjustment and object manipulation.
    • Previous research explored directional effects on user behavior in 2D environments (tablets, PCs), but not in immersive VR.
    • Barehand interaction in VR presents unique challenges and opportunities for steering behavior.

    Purpose of the Study:

    • To investigate and model the directional effect on user behavior in VR steering tasks.
    • To develop a predictive model for steering performance in virtual environments (VEs) focusing on barehand interaction.
    • To enhance user experience and interaction efficiency in VR through informed design recommendations.

    Main Methods:

    • Conducted two user studies to collect behavioral data, including movement time, speed, success rate, and reenter times.
    • Developed the SθModel based on directional effects observed in the first study.
    • Empirically evaluated and validated the SθModel using data from both studies, including variations in devices and steering directions.

    Main Results:

    • The SθModel demonstrated high prediction accuracy (r2 > 0.95) for movement time and speed.
    • Achieved over 15% improvement in prediction accuracy compared to the original Steering Law.
    • The model's optimal performance was consistent across different devices and steering directions in VR.

    Conclusions:

    • The SθModel effectively captures and predicts directional influences on steering behavior in VR.
    • The proposed model offers superior predictive power over existing models for VR steering tasks.
    • Findings provide actionable design recommendations to optimize steering interactions and user experience in virtual reality systems.