Selection Performance Using a Smartphone in VR with Redirected Input
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 present a method to track a smartphone in VR using a fiducial marker displayed on the screen. Using WebRTC transmission protocol, we capture smartphone touchscreen input and the screen contents, copying them to a virtual representation in VR. We present two Fitts’ law experiments assessing the performance of selecting targets displayed on the virtual smartphone screen using this method. The first compares direct vs. indirect input (i.e., virtual smartphone co-located with the physical smartphone, or not), and reveals there is no difference in performance due to input indirection. The second experiment assesses the influence of input scaling, i.e., decoupling the virtual cursor from the actual finger position on the smartphone screen so as to provide a larger virtual tactile surface. Results indicate a small effect for extreme scale factors. We discuss implications for the use of smartphones as input devices in VR.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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