Selection Performance Using a Scaled Virtual Stylus Cursor in VR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it