Transsaccadic Awareness of Scene Transformations in a 3D Virtual Environment
Bibliographic record
Abstract
In gaze-contingent displays, the viewer’s eye movement data are processed in real-time to adjust the graphical content. To provide a high-quality user experience, these graphical updates must occur with minimum delay. Such updates can be used to introduce imperceptible changes in virtual camera pose in applications such as networked gaming, collaborative virtual reality and redirected walking. For such applications, perceptual saccadic suppression can help to hide the graphical artifacts. We investigated whether the visibility of these updates depends on the type of image transformation. Users viewed 3D scenes in which the displacement of a target object triggered them to generate a vertical or horizontal saccade, during which a translation or rotation was applied to the virtual camera used to render the scene. After each trial, users indicated the direction of the scene change in a forced-choice task. Results show that type and size of the image transformation affected change detectability. During horizontal or vertical saccades, rotations along the roll axis were the most detectable, while horizontal and vertical translations were least noticed. We confirm that large 3D adjustments to the scene viewpoint can be introduced unobtrusively and with low latency during saccades, but the allowable extent of the correction varies with the transformation applied.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".