The Relationship Between the Virtual Hand Illusion and Motor Performance

Abstract

Bodily self-consciousness consists of agency (i.e., the feeling of controlling one’s actions and causing external events) and body ownership (i.e., the feeling that one’s body belongs to one’s self). If a visual presentation of a virtual (fake) hand matches the active movement of a real hand, both the agency and body ownership of the virtual hand are induced [i.e., the active virtual hand illusion (VHI)]. However, previous active VHI studies have rarely considered the effects of goal-related movement errors (i.e., motor performance) on the senses of agency and ownership. Hence, the current study aimed to clarify the relationship between the active VHI and motor performance. To induce the VHI, 18 healthy subjects (three men and 15 women; 20.7 ± 7.3 years) were required to continuously move a virtual hand around a circle at a predetermined speed (i.e., spatial and temporal goals) using their active hand movements. While moving the virtual hand actively, five visual feedback delays were introduced: 90, 210, 330, 450, and 570 ms. It was found that the subjective ratings of both the agency and body ownership of the virtual hand decreased as a function of the delay intervals, whereas most of the spatial and temporal movement errors linearly increased. Using multiple regression analyses, we examined whether the agency and ownership ratings could be explained effectively by both the delay and movement errors. The results demonstrated that the agency was determined not only by the delay but also by the movement variability, whereas the body ownership was mostly determined by the delay. These findings suggest a possibility that the goal-related motor performance of the active VHI influences the agency judgment more strongly, while its effect on the ownership judgment is weaker.

Toward Enhanced Teleoperation Through Embodiment

“…Ownership is mainly determined by the realism of the fake body part (Argelaguet et al, 2016 ) and the temporal match between the perceived visual and tactile stimulation (Tsakiris and Haggard, 2005 ; Tsakiris, 2010 ; Kalckert and Ehrsson, 2012 ), whereas agency primarily arises from the match between predicted (intended) movements and actual kinesthetic feedback (i.e., the perceived efficiency of control: Frith et al, 2000 ; Argelaguet et al, 2016 ; Braun et al, 2018 ). This becomes manifest in the virtual hand illusion (VHI), where ownership is mostly determined by delays (temporal mismatches between visual and tactile stimulation; Ismail and Shimada, 2016 ; Shibuya et al, 2018 ), while agency is determined both by delays and by movement variability (visual-motor mismatches; Shibuya et al, 2018 ). Functional Magnetic Resonance Imaging (fMRI) studies have shown that ownership and agency are linked to different brain areas.…”

Sequential Motor Learning Transfers from Real to Virtual Environment

“…Previous research on VEs employed several tools, such as a two-joint mechanical arm 16 , joysticks 9 , and pinch force sensors 15 , which showed improvement in the motor learning ability. These experiments were performed using sensory information as clues instead of visual information, like the arm-reaching task used in the present study.…”

“…Virtual hand illusion (VHI) was also used in motor learning owing to its applicability and expandability. Several studies have reported that motor learning is promoted even in VEs 9,15,16 . In addition, a previous study has suggested that post-training performance in virtual and real training is equivalent and both of which signi cantly exceeds without training 17 .…”

Interpersonal Motor Interactions Shape Multisensory Representations of the Peripersonal Space

“…Similarly, pairing of motor efferent signals with perceived sensory consequences may also affect the rubber hand illusion, such as in the moving and virtual Rubber Hand illusions [ 51 , 133 , 134 , 135 , 136 , 137 , 138 ]. The relation between the role of efferent motor and afferent sensory signals to the incorporation process has been recently cast in the perspective of Bayesian sensory filtering through predictive coding [ 139 ].…”