Agency and Performance of Reach-to-Grasp With Modified Control of a Virtual Hand: Implications for Rehabilitation

This study investigated how modified control of a virtual hand executing reach-to-grasp affects functional performance and agency (perception of control). The objective of this work was to demonstrate positive relationships between reaching performance and grasping agency and motivate greater consideration of agency in movement rehabilitation. We hypothesized that agency and performance have positive correlation across varying control modes of the virtual hand. In this study, each participant controlled motion of a virtual hand through motion of his or her own hand. Control of the virtual hand was modified according to a specific control mode. Each mode involved the virtual hand moving at a modified speed, having noise, or including a level of automation. These specific modes represent potential control features to adapt for a rehabilitation device such as a prosthetic arm and hand. In this study, significant changes in agency and performance were observed across the control modes. Overall, a significant positive relationship (p < 0.001) was observed between the primary performance metric of reach (tracking a minimum path length trajectory) and an implicit measurement of agency (intentional binding). Intentional binding was assessed through participant perceptions of time-intervals between grasp contact and a sound event. Other notable findings include improved movement efficiency (increased smoothness, reduced acceleration) during expression of higher agency and shift toward greater implicit versus explicit agency with higher control speed. Positively relating performance and agency incentivizes control adaptation of powered movement devices, such as prostheses or exoskeletons, to maximize both user engagement and functional performance. Agency-based approaches may foster user-device integration at a cognitive level and facilitate greater clinical retention of the device. Future work should identify robust and automated methods to adapt device control for increased agency. Objectives include how virtual reality (VR) may identify optimal control of real-world devices and assessing real-time agency from neurophysiological signals.