EZCursorVR: 2D Selection with Virtual Reality Head-Mounted Displays
Bibliographic record
Abstract
We present an evaluation of a new selection technique for virtual reality (VR) systems presented on head-mounted displays. The technique, dubbed EZCursorVR, presents a 2D cursor that moves in a head-fixed plane, simulating 2D desktop-like cursor control for VR. The cursor can be controlled by any 2DOF input device, but also works with 3/6DOF devices using appropriate mappings. We conducted an experiment based on ISO 9241-9, comparing the effectiveness of EZCursorVR using a mouse, a joystick in both velocity-control and position-control mappings, a 2D-constrained ray-based technique, a standard 3D ray, and finally selection via head motion. Results indicate that the mouse offered the highest performance in terms of throughput, movement time, and error rate, while the position-control joystick was worst. The 2D-constrained ray-casting technique proved an effective alternative to the mouse when performing selections using EZCursorVR, offering better performance than standard ray-based selection.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".