Eye and head movements while encoding and recognizing panoramic scenes in virtual reality
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
One approach to studying the recognition of scenes and objects relies on the comparison of eye movement patterns during encoding and recognition. Past studies typically analyzed the perception of flat stimuli of limited extent presented on a computer monitor that did not require head movements. In contrast, participants in the present study saw omnidirectional panoramic scenes through an immersive 3D virtual reality viewer, and they could move their head freely to inspect different parts of the visual scenes. This allowed us to examine how unconstrained observers use their head and eyes to encode and recognize visual scenes. By studying head and eye movement within a fully immersive environment, and applying cross-recurrence analysis, we found that eye movements are strongly influenced by the content of the visual environment, as are head movements-though to a much lesser degree. Moreover, we found that the head and eyes are linked, with the head supporting, and by and large mirroring the movements of the eyes, consistent with the notion that the head operates to support the acquisition of visual information by the eyes.
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 | 0.000 |
| 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