A Fitts’ Law Evaluation of Visuo-haptic Fidelity and Sensory Mismatch on User Performance in a Near-field Disc Transfer Task in Virtual Reality
Bibliographic record
Abstract
The trade-off between speed and accuracy in precision tasks is important to evaluate during user interaction with input devices. When different sensory cues are added or altered in such interactions, those cues have an effect on this trade-off, and thus, they affect overall user performance. For instance, adding cues like haptic feedback and stereoscopic viewing will result in more realistic user interaction, thus improving performance in these tasks. Also, adding a noticeable disparity between physical and virtual movements creates a mismatch between visual and proprioceptive systems, which generally has a negative effect on performance. In this study, we investigate the effects of haptic feedback, stereoscopic viewing, and visuo-proprioceptive mismatch on how quickly and accurately users complete a virtual pick-and-place task using the PHANToM OMNI. Through this experiment, we find that in the movement phase of a ring transfer, movement time and user performance are affected by haptic feedback and visuo-proprioceptive mismatch, and the main effects of stereoscopic viewing appears to be limited to the more precise step when the ring is around the target peg.
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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 | 0.002 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".