Gaze Analysis in Early Warning Visual Feedback System for Hand Tracking Failures in Virtual Reality
Bibliographic record
Abstract
Hand-tracking failures significantly affect user performance and experience in VR; thus, minimizing the negative effects of such failures remains a critical area of research. Although prior studies have introduced early-warning feedback systems to mitigate hand-tracking failures, it remains unclear <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">why</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how</i> such feedback enhances user performance and reduces frustration. To address this gap, we investigated user gaze behaviors in a visual early-warning feedback system with three common hand-tracking failure scenarios: Low-Intensity Light Level, Out-of-Vision Hands, and Self-Occlusion. Our findings indicate that user attention toward feedback notifications varies depending on the type of simulated tracking failure. Providing early-warning feedback did not disrupt users’ attention to their primary tasks. Instead, users maintained their focus on object placement, resulting in fewer placement errors during pick-and-place tasks. These results contribute to our understanding of early-warning visual feedback systems and provide valuable insights for designing feedback mechanisms that effectively improve performance without creating distractions in VR interactions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".