Effects of 3D Rotational Jitter and Selection Methods on 3D Pointing Tasks
Why this work is in the frame
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Bibliographic record
Abstract
3D pointing is an integral part of Virtual Reality interaction. Typical pointing devices rely on 3D trackers and are thus subject to fluctuations in the reported pose, i.e., jitter. In this work, we explored how different levels of rotational jitter affect pointing performance and if different selection methods can mitigate the effects of jitter. Towards this, we designed a Fitts' Law experiment with three selection methods. In the first method, subjects used a single controller to position and select the object. In the second method, subjects used the controller in their dominant hand to point at objects and the trigger button of a second controller, held in their non-dominant hand, to select objects. Finally, subjects used the controller in their dominant hand to point the objects and pressed the space bar on a keyboard to select the object in the third condition. During the pointing task we added five different levels of jitter: no jitter, ±0.5°, ±1°, and ±2° uniform noise, as well as White Gaussian noise with 1° standard deviation. Results showed that the Gaussian noise and ±2° of jitters significantly reduced the throughput of the participants. Moreover, subjects made fewer errors when they performed the experiment with two controllers. Our results inform the design of 3D user interfaces, input devices and interaction techniques.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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