Impact of Avatar-Locomotion Congruence on User Experience and Identification in Virtual Reality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As virtual reality (VR) continues to expand, particularly in social VR platforms and immersive gaming environments, understanding the factors that shape user experience is becoming increasingly important. Avatars and locomotion methods play central roles in influencing how users identify with their virtual representations and navigate virtual spaces. Despite extensive research on these elements individually, their relationship remains underexplored. In particular, little is known about how congruence between avatar appearance and locomotion method affects user perceptions. This study investigates the impact of avatar-locomotion congruence on user experience and avatar identification in VR. We conducted a within-subjects experiment with 30 participants, employing two visually distinct avatar types (human and gorilla) and two locomotion methods (human-like arm-swinging and gorilla-like arm-rolling), to assess their individual and combined effects. Our results indicate that congruence between avatar appearance and locomotion method enhances both avatar identification and user experience. These findings contribute to the understanding of the relationship between avatars and locomotion in VR, with potential applications in enhancing user experience in immersive gaming, social VR, and gamified remote physical therapy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it