Mechanism of integrating force and vibrotactile cues for 3D user interaction within virtual environments
Bibliographic record
Abstract
Proper integration of sensory cues facilitates 3D user interaction within virtual environments (VEs). Studies showed that the integration of visual and haptic cues follows maximum likelihood estimation (MLE). Little effort focuses however on the mechanism of integrating force and vibrotactile cues. We thus investigated MLE's suitability for integrating these cues. Within a VE, human users undertook 3D interaction of navigating a flying drone along a high-voltage transmission line for inspection. The users received individual force or vibrotactile cues, and their combinations in collocated and dislocated settings. The users' task performance including completion time and accuracy was assessed under each individual cue and setting. The presence of the vibrotactile cue promoted a better performance than the force cue alone. This agreed with the applicability of tactile cues for sensing 3D surfaces, herein setting a baseline for using MLE. The task performance under the collocated setting indicated a degree of combining the individual cues. In contrast, the performance under the dislocated setting was alike under the individual vibrotactile cue. These observations imply a possible role of MLE in integrating force and vibrotactile cues for 3D user interaction within VEs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".