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    This study introduces a novel virtual reality group selection method using probability permutation to enhance multi-object selection efficiency. The approach significantly improves user task load and usability in immersive environments.

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    Area of Science:

    • Human-Computer Interaction
    • Virtual Reality
    • Usability Engineering

    Background:

    • Multi-object selection in virtual reality (VR) is crucial for operational efficiency.
    • Existing methods may not fully optimize the speed and ease of grouping numerous items.

    Purpose of the Study:

    • To propose and validate a novel group selection method for virtual reality.
    • To enhance the efficiency and reduce the task load of multi-object selection in immersive VR.

    Main Methods:

    • A group selection method based on multiple rounds of probability permutation.
    • Interactive selection, object grouping probability computation, and position rearrangement.
    • Ablation experiments for algorithm coefficient determination and validation.
    • Empirical user study to evaluate task efficiency and usability.

    Main Results:

    • The proposed method significantly improves the efficiency of group selection tasks in VR.
    • The iterative probability permutation approach facilitates easier batch selection in subsequent rounds.
    • Reduced operations led to decreased user task load and enhanced usability.

    Conclusions:

    • The probability permutation-based group selection method offers substantial improvements in VR multi-object selection.
    • This technique enhances user experience by increasing efficiency and reducing cognitive load.
    • The method is effective for optimizing interactions in immersive virtual environments.