An Exploration on the Integration of Vibrotactile and Force Cues for 3D Interactive Tasks
Bibliographic record
Abstract
Vibrotactile and force cues of the haptic modality is increasing used to facilitate interactive tasks in three-dimensional (3D) virtual environments (VE). While maximum likelihood estimation (MLE) explains the integration of multi-sensory cues in many studies, an existing work yielded mean and amplitude mismatches when using MLE to interpret the integration of vibrotactile and force cues. To investigate these mismatches, we proposed mean-shifted MLE and conducted a study of comparing MLE and mean-shift MLE. Mean-shifted MLE shared the same additive assumption of the cues as MLE, but took account mean differences of both cues. In a VE, the study replicated the visual scene, the 3D interactive task, and the cues from the existing work. All human participants in the study were biased to rely on the vibrotactile cue for their task, departing from unbiased reliance towards both cues in the existing work. After validating the replications, we applied MLE and mean-shifted MLE to interpret the integration of the vibrotactile and force cues. Similar to the existing work, MLE failed to explain the mean mismatch. Mean-shifted MLE remedied this mismatch, but maintained the amplitude mismatch. Further examinations revealed that the integration of the vibrotactile and force cues might violate the additive assumption of MLE and mean-shifted MLE. This sheds a light for modeling the integration of vibrotactile and force cues to aid 3D interactive tasks within VEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".