Selection Performance Using a Scaled Virtual Stylus Cursor in VR
Bibliographic record
Abstract
We propose a surface warping technique we call warped virtual surfaces (WVS). WVS is similar to applying CD gain to mouse cursor on a screen and is used with traditionally 1:1 input devices, in our case, a tablet and stylus, for use with VR head-mounted displays (HMDs). WVS allows users to interact with arbitrarily large virtual panels in VR while getting the benefits of passive haptic feedback from a fixed-sized physical panel. To determine the extent to which WVS affects user performance, we conducted an experiment with 24 participants using a Fitts' law reciprocal tapping task to compare different scale factors. Results indicate there was a significant difference in movement time for large scale factors. However, for throughput (ranging from 3.35 3.47 bps) and error rate (ranging from 3.6 5.4%), our analysis did not find a significant difference between scale factors. Using non-inferiority statistical testing (a form of equivalence testing), we show that performance in terms of throughput and error rate for large scale factors is no worse than a 1-to-1 mapping. Our results suggest WVS is a promising way of providing large tactile surfaces in VR, using small physical surfaces, and with little impact on user performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".