Viewpoint Optimisation for Virtual Environment Navigation Using Dynamic Tethering - A Study of Tether Rigidity
Bibliographic record
Abstract
Much research has been carried out on providing navigational aids and improving users' wayfinding ability across a variety of navigation related tasks. In this study, we focus on investigating users' navigational performance with respect to the display frame of reference in a large scale virtual environment. Dynamic viewpoint tethering is proposed as a means to improve user control and reduce the need for mental rotations, thus facilitating the acquisition of configurational knowledge about the virtual space. The modelling of dynamic viewpoint tethering is explained and recent research findings are presented. Twelve volunteers participated in an experiment in which they were instructed to control an aircraft-shaped cursor flying through a set of virtual tunnels (local guidance) and to answer questions about the environment (global awareness). Experimental results showed that neither the very loose dynamic tether nor the completely rigid tether supported the best control performance. Rather, an optimal tether configuration lies at the centre of the rigidity continuum. Research results are discussed from the point of view of design of navigational system interfaces.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".