Collaboration Efficiency with Gesture Recognition Interfaces in Mixed Reality Studios: Causal Modeling

Abstract

The rapid integration of mixed reality environments into professional and creative workflows has necessitated the development of more intuitive and efficient user interfaces. Among these, gesture recognition interfaces have emerged as a prominent solution, promising to reduce cognitive load and enhance spatial interaction. However, evaluating their true impact on collaborative efficiency remains challenging due to the presence of numerous confounding variables such as user experience, task complexity, and spatial cognitive abilities. Traditional observational studies often fail to isolate the causal effect of interface types on performance metrics, relying instead on correlational analyses that may lead to spurious conclusions. This paper introduces a comprehensive causal modeling framework to assess collaboration efficiency in mixed reality studios. By employing directed acyclic graphs and propensity score matching, this study systematically controls for environmental and user-specific confounders. The research design involves a controlled experimental setup where participants engage in collaborative spatial design tasks using either advanced gesture recognition interfaces or conventional handheld controllers. The empirical findings provide robust causal evidence indicating that while gesture interfaces significantly improve subjective feelings of co-presence and naturalistic interaction, their actual impact on objective task completion times is heavily moderated by the baseline spatial reasoning skills of the users. This study bridges a critical gap in human-computer interaction literature by transitioning from correlational observations to rigorous causal inference, offering actionable insights for the design and implementation of future mixed reality collaborative systems.