Improving Effective Throughput Performance using Auditory Feedback in Virtual Reality
Bibliographic record
Abstract
During the complex process of motor skill acquisition, novices might focus on different criteria, such as speed or accuracy, in their training. Previous research on virtual reality (VR) has shown that effective throughput could also be used as an assessment criterion. Effective throughput combines speed, accuracy, and precision into one measure, and can be influenced by auditory feedback. This paper investigates through a user study how to improve participants’ effective throughput performance using auditory feedback. In the study, we mapped the speed and accuracy of the participants to the pitch of the auditory error feedback in an ISO 9241-411 multidirectional pointing task and evaluated participants’ performance. The results showed it is possible to regulate the time or accuracy performance of the participants and thus the effective throughput. Based on the findings of our work, we also identify that effective throughput is an appropriate assessment criterion for VR systems. We hope that our results can be used for VR applications.
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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 | 0.000 |
| 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".