Evaluating and Modeling the Effect of Frame Rate on Steering Performance in Virtual Reality
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
Prior work has shown that frame rate significantly influences user behavior in fast-response tasks in 2D and 3D contexts. However, its impact on a steering task, which involves navigating an object along a path from the start to the end, remains relatively unexplored, especially in the context of virtual reality (VR). This task is considered a typical non-fast-response activity, as it does not demand rapid reactions within a limited time frame. Our work aims to understand and model users' steering behavior and predict movement time with different task complexities and frame rates in VR environments. We first conducted a user study to collect user behavior in a steering task with four factors: frame rate, path length, width, and radius of curvature. Based on the results, we then quantified the effects of frame rate and built two predictive models. Our models exhibited the best fit ($r^{2}> 0.957$r2>0.957) and over 17% improvement in prediction accuracy for movement time compared to existing models. Our models' robustness was further validated by applying them to predict steering performance with different VR tasks and frame rates. The two models keep the best predictability for both movement time and speed.
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 | 0.000 |
| 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