Visual exploration of omnidirectional panoramic scenes
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
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.
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 | 0.001 |
| 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