Audiohaptic Feedback Enhances Motor Performance in a Low-Fidelity Simulated Drilling Task
Bibliographic record
Abstract
When used in educational settings, simulations utilizing virtual reality (VR) technologies can reduce training costs while providing a safe and effective learning environment. Tasks can be easily modified to maximize learning objectives of different levels of trainees (e.g., novice, intermediate, expert), and can be repeated for the development of psychomotor skills. VR offers a multisensory experience, providing visual, auditory, and haptic sensations with varying levels of fidelity. While simulating visual and auditory stimuli is relatively easy and cost-effective, similar representations of haptic sensation still require further development. Evidence suggests that mixing high- and low-fidelity realistic sensations (e.g., audition and haptic) can improve the overall perception of realism, however, whether this also leads to improved performance has not been examined. The current study examined whether audiohaptic stimuli presented in a virtual drilling task can lead to improved motor performance and subjective realism, compared to auditory stimuli alone. Right-handed participants (n = 16) completed 100 drilling trials of each stimulus type. Performance measures indicated that participants overshot the target during auditory trials, and undershot the target during audiohaptic trials. Undershooting is thought to be indicative of improved performance, optimizing both time and energy requirements.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".