Don't Miss Notifications: Exploring Gaze Notifications for Virtual Reality Cooking Environment
Bibliographic record
Abstract
In 3D environments, designing efficient notifications is crucial for capturing user attention. While visual notifications -such as objectattached and fixed-position ones- are commonly used in virtual environments, they often require users to shift their gaze away from task-relevant areas, which can interrupt workflow and delay responses. To address these limitations, we designed two gaze-based notification techniques to provide responsive and intuitive notification in a virtual reality (VR) cooking task. We evaluated four different notification types: two world-fixed notifications (onObject and onDock) and two gaze-based methods (GazeCue and Gaze+Dock) with 16 participants. Our results show that participants performed better in using gaze-based notifications compared to world-fixed ones. Questionnaire results indicated higher usability, greater perceived presence, and lower cognitive load for gaze-based notifications. These results highlight the effectiveness of gaze-based notification techniques in VR by reducing the need for visual search and minimizing cognitive load. Our findings provide insights for developers or engineers to design more intuitive and responsive user-interfaces for 3D environments.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".