Impact of Avatar-Locomotion Congruence on User Experience and Identification in Virtual Reality
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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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 | 0.003 | 0.024 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".