Motion Congruence Outweighs Appearance in Virtual Self-Identification

Abstract

The integration of multisensory information in virtual environments (VR) relies heavily on the brain’s ability to resolve cue conflicts between visual appearance and motor feedback. While traditional paradigms often treat visuomotor relationships using binary heuristics, this thesis introduces a novel methodological framework utilizing an exponential moving average (EMA) filter to dynamically quantify and manipulate motion congruence in real time. Across a series of controlled trials, this work investigates the relative influence of visuomotor congruence and avatar appearance on self-identification and subjective experiences of virtual presence and embodiment. The empirical findings demonstrate that motion congruence is a primary driver of avatar self-identification scores, though its efficacy might be modulated by viewing perspective and technological constraints. Specifically, third-person perspectives and hardware limitations (such as restricted fields of view and resolution) impose boundary conditions that alter cue weighting strategies. Furthermore, analyses accounting for sample size constraints and statistical power highlight the necessity of within-participant designs and robust multi-metric evaluation to mitigate subjective response biases. Ultimately, this thesis advances our theoretical understanding of human multisensory integration in virtual settings and provides practical guidelines for the design of interactive systems. By bridging computational innovation in motion filtering with rigorous empirical evaluation, this work establishes foundational principles for optimizing embodiment and fidelity in emerging virtual and augmented reality applications.