Controlling the Sense of Embodiment for Virtual Avatar Applications: Methods and Empirical Study
Bibliographic record
Abstract
BACKGROUND: The sense of embodiment (SoE) is the feeling of one's own body, and research on the SoE extends from the rubber hand illusion to the full-body ownership illusion with a virtual avatar. OBJECTIVE: The key to utilizing a virtual avatar is understanding and controlling the SoE, and it can be extended to several medical applications. In this study, we aimed to clarify these aspects by considering the following three subcomponents of SoE: sense of agency, ownership, and self-location. METHODS: We defined a human avatar (HA), point light avatar (PLA), and out-of-body point light avatar (OBPLA) and compared them in three user studies. In study 1, 28 participants were recruited and the three avatar conditions (HA, PLA, and OBPLA) were compared. In study 2, 29 new participants were recruited, and there were two avatar conditions (HA ad PLA) and two motion synchrony conditions (synchrony and asynchrony). In study 3, 29 other participants were recruited, and there were two avatar conditions (PLA and OBPLA) and two motion synchrony conditions (synchrony and asynchrony). Dependent measures included sense of agency, ownership, and self-location; emotional response; presence; and simulator sickness. RESULTS: The findings of study 1 showed that the three avatar generation methodologies can control the sense of ownership and self-location in a stepwise manner while maintaining a high sense of agency. In studies 2 and 3, we found dependencies among the three subcomponents of SoE and observed that they affected users' subjective experiences. CONCLUSIONS: Our findings may have implications for boosting the effects of virtual avatar applications in medical areas, by understanding and controlling the SoE with a full-body illusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".