Empirical Studies on Selection and Travel Performance of Eye-tracking in Virtual Reality
Bibliographic record
Abstract
We presented two studies on VR selection and travel performances using eyebased interaction via FOVE head-mounted display (HMD).Our selection experiment was modelled after the ISO 9241-9 reciprocal selection task, with targets presented at varying depths in a custom virtual environment.We compared eye-based and head-based in isolation, and the combination of eye-tracking and head-tracking.Results indicate that eye-only offered the worst performance in terms of error rate, selection times, and throughput.Head-only offered significantly better performance.In our travel study, the task involved controlling movement direction while flying through target rings in the air by seven techniques.We found that the completion time and success rates of head+eye were very close to head-only, while eye-only did not perform better than head+eye due to learning effects and calibration issues, which also yield high cybersickness.Head+eye compensated for the eye-tracker issues and would be potentially an alternative to traditional traveling techniques.5 Chapter: Conclusion ....
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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 | 0.046 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".