The Relationship Between Embodiment Perception and Motor Learning in Virtual Reality-based Interventions
Bibliographic record
Abstract
Virtual reality (VR) is a rapidly evolving technology that offers immersive experiences by simulating realistic environments and interactions. In the context of motor learning and rehabilitation, VR has emerged as a promising tool because of its ability to provide controlled, customizable and engaging training scenarios. A key factor in the effectiveness of VR-based interventions is the sense of embodiment, which refers to the user's perception of being present in the virtual environment and having control over a virtual body. This thesis investigates the influence of different sensory feedback modalities on the sense of embodiment and task performance in VR-based motor learning. Through two studies, we examine how the combination of visual and tactile feedback affects embodiment perception and motor task performance in VR environment. In the first study, we explore the effects of pressure feedback on task performance and embodiment in VR-based mirror therapy. Twenty-two able-bodied participants were divided into two groups, with one group receiving the pressure feedback on their thumb and index fingertips during a pick-and-place task. The results indicate that the group with haptic feedback achieved a 15.07% higher task success rate and reported a 12.80% higher embodiment perception compared to the control group. The second study extends the investigation to the impact of vibrotactile feedback in a ball-and-beam control task. Nineteen participants were exposed to four conditions: No Feedback, Vibrotactile Feedback only, Visual Feedback Only and Both Vibrotactile and Visual Feedback. The condition with Both Vibrotactile and Visual Feedback had a 14.52% improvement in task performance and a higher embodiment perception compared to other conditions. Overall, this thesis contributes to the understanding of how sensory feedback modalities can be effectively integrated into VR systems to enhance embodiment and motor learning, suggesting that incorporating haptic feedback into a visual interaction may be associated with higher embodiment and improved motor task performance. These insights have implications for the design of more effective VR-based interventions for training and rehabilitation purposes, emphasizing the value of multisensory feedback in these contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".