Enhancing the Sense of Agency by Transitional Weight Control in Virtual Co-Embodiment

Virtual reality helps us learn complex motor skills by providing a situation in which we observe or follow a teacher’s movements from a first-person perspective. However, it has been suggested that if the learners themselves do not behave actively, their body schemes will not be updated and motor skills will be acquired temporarily, but will not be retained in the long term. As a solution to this problem, “co-embodiment” in which two people embody an avatar that reflects the weighted average of their movements was proposed, and it is shown that the user can feel an excessive sense of agency (SoA) even when their control weight is small. From the perspective of motor skill learning, the learner must feel as strong a SoA as possible while performing the exercise as close to the teacher as possible. Therefore, in this study, we propose a method to transitionally change the weights in a situation where co-embodiment is used, such that a strong SoA is felt despite the high weights of control by others. Considering the two-step account of the agency model, which states that the SoA is influenced by context, we tested the hypothesis that an initially strong SoA can maintain the SoA despite a gradually decreased control weight. The experimental results support this hypothesis, and it is expected that the proposed method will enhance the effectiveness of motor skill learning using co-embodiment.