Reevaluating the Gaze Cursor in Virtual Reality: A Comparative Analysis of Cursor Visibility, Confirmation Mechanisms, and Task Paradigms
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
Cursors and how they are presented significantly influence user experience in both VR and non-VR environments by shaping how users interact with and perceive interfaces. In traditional interfaces, cursors serve as a fundamental component for translating human movement into digital interactions, enhancing interaction accuracy, efficiency, and experience. The design and visibility of cursors can affect users' ability to locate interactive elements and understand system feedback. In VR, cursor manipulation is more complex than in non-VR environments, as it can be controlled through hand, head, and gaze movements. With the arrival of the Apple Vision Pro, the use of gaze-controlled non-visible cursors has gained some prominence. However, there has been limited exploration of the effect of this type of cursor. This work presents a comprehensive study of the effects of cursor visibility (visible versus invisible) in gaze-based interactions within VR environments. Through two user studies, we investigate how cursor visibility impacts user performance and experience across different confirmation mechanisms and tasks. The first study focuses on selection tasks, examining the influence of target width, movement amplitude, and three common confirmation methods (air tap, blinking, and dwell). The second study explores pursuit tasks, analyzing cursor effects under varying movement speeds. Our findings reveal that cursor visibility significantly affects both objective performance metrics and subjective user preferences, but these effects vary depending on the confirmation mechanism used and task type. We propose eight design implications based on our empirical results to guide the future development of gaze-based interfaces in VR. These insights highlight the importance of tailoring cursor metaphors to specific interaction tasks and provide practical guidance for researchers and developers in optimizing VR user interfaces.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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