Visual exploration of omnidirectional panoramic scenes
Bibliographic record
Abstract
How do we explore the visual environment around us, and how are head and eye movements coordinated during our exploration? To investigate this question, we had observers look at omnidirectional panoramic scenes, composed of both landscape and fractal images, using a virtual reality viewer while their eye and head movements were tracked. We analyzed the spatial distribution of eye fixations and the distribution of saccade directions and the spatial distribution of head positions and the distribution of head shifts, as well as the relation between eye and head movements. The results show that, for landscape scenes, eye and head behavior best fit the allocentric frame defined by the scene horizon, especially when head tilt (i.e., head rotation around the view axis) is considered. For fractal scenes, which have an isotropic texture, eye and head movements were executed primarily along the cardinal directions in world coordinates. The results also show that eye and head movements are closely linked in space and time in a complementary way, with stimulus-driven eye movements predominantly leading the head movements. Our study is the first to systematically examine eye and head movements in a panoramic virtual reality environment, and the results demonstrate that a virtual reality environment constitutes a powerful and informative research alternative to traditional methods for investigating looking behavior.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".