Vibrotactile cues on multiuser collaboration within virtual environments
Bibliographic record
Abstract
Multiuser collaboration within virtual environments (VEs) need effective means of communication. A real-world collaboration benefits from verbal and nonverbal communication channels. To promote the nonverbal communication within VEs, some research studies have explored various forms of vibrotactile cues that are either spatially co-located with an interaction device held by a user's hand, or dislocated from the device. These studies are focused on VEs that are established on a leader and a follower paradigm. A multiuser collaborative VE is however governed by an interaction model to handle the simultaneous interactive commands issued by multiple peer users. Proposed in our earlier work, the dynamic priority model (DP) is an interaction model that yields perceived equality in interaction and promotes the multiuser collaboration when peer users communicate through verbal dialogue. As a key means of nonverbal communication in VEs, co-located and dislocated vibrotactile cues might affect the perceived equality in interaction, and how users collaborate under the DP model. In this study, we have thus investigated the role of vibrotactile cues on multiuser collaboration under the DP model. We undertook this investigation in two cue settings: co-located and dislocated. We observed that the DP model yields perceived equality in interaction both in the absence and presence of vibrotactile cues. Also, the co-located cues significantly enhanced the multiuser collaboration compared to the dislocated cues. These observations imply a potential application of co-located vibrotactile cues to enhance multiuser collaboration 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".