The Effect of Pitch in Auditory Error Feedback for Fitts' Tasks in Virtual Reality Training Systems
Bibliographic record
Abstract
Fitts' law and the associated throughput measure characterize user pointing performance in virtual reality (VR) training systems and simulators well. Yet, pointing performance can be affected by the feedback users receive from a VR application. This work examines the effect of the pitch of auditory error feedback on user performance in a Fitts' task through a distributed experiment. In our first study, we used middle- and high-frequency sound feedback and demonstrated that high-pitch error feedback significantly decreases user performance in terms of time and throughput. In the second study, we used adaptive sound feedback, where we increased the frequency with the error rate, while asking subjects to execute the task “as fast/as precise/as fast and precise as possible”. Results showed that adaptive sound feedback decreases the error rate for “as fast as possible” task execution without affecting the time. The results can be used to enhance and design various VR systems.
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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 | 0.002 |
| 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".