Proportional Likelihood Estimation for Integrating Vibrotactile and Force Cues in 3D User Interaction
Bibliographic record
Abstract
A model of integration for vibrotactile and force cues is important for facilitating human users' task performance in human-machine systems. One of such human-machine systems is an interactive three-dimensional (3D) virtual environment (VE). In this paper, we proposed proportional likelihood estimation (PLE) as a model of integration for vibrotactile and force cues. Assuming human responses to cues as Gaussian distributions, PLE integrates these cues proportionally according to certain weighted contributions. We conducted an experiment to verify the suitability of PLE. For the experiment, we created a VE in which a human user executed interactively an identification task. The task required the user to identify visually indiscernible defects on a transmission line with a flying drone. The defects were indicated to the user through vibrotactile and/or force cues. These cues were in a co-located or dis-located setting, respectively, on the user's right hand and/or forearm. The PLE predictions of integrating the vibrotactile and force cues were able to match the empirical observation of these combined cues. PLE also elucidated this cue integration successfully when applying to an existing dataset acquired under a different experimental condition. Further analyses revealed that the cue integration may not be entirely additive. Hence, PLE could shed a light on the cue integration for facilitating user interaction in human-machine systems, like 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 | 0.003 | 0.017 |
| 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 | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".