Eye and head movements while looking at rotated scenes in VR.
Bibliographic record
Abstract
Video stream: https://vimeo.com/356859979 Production and publication of the video stream was sponsored by SCIANS Ltd http://www.scians.ch/ We examined the extent to which image shape (square vs. circle), image rotation, and image content (landscapes vs. fractal images) influenced eye and head movements. Both the eyes and head were tracked while observers looked at natural scenes in a virtual reality (VR) environment. In line with previous work, we found a horizontal bias in saccade directions, but this was affected by both the image shape and its content. Interestingly, when viewing landscapes (but not fractals), observers rotated their head in line with the image rotation, presumably to make saccades in cardinal, rather than oblique, directions. We discuss our findings in relation to current theories on eye movement control, and how insights from VR might inform traditional eyetracking studies. - Part 2: Observers looked at panoramic, 360 degree scenes using VR goggles while eye and head movements were tracked. Fixations were determined using IDT (Salvucci & Goldberg, 2000) adapted to a spherical coordinate system. We then analyzed a) the spatial distribution of fixations and the distribution of saccade directions, b) the spatial distribution of head positions and the distribution of head movements, and c) the relation between gaze and head movements. We found that, for landscape scenes, gaze and head best fit the allocentric frame defined by the scene horizon, especially when taking head tilt (i.e., head rotation around the view axis) into account. For fractal scenes, which are isotropic on average, the bias toward a body-centric frame gaze is weak for gaze and strong for the head. Furthermore, our data show that eye and head movements are closely linked in space and time in stereotypical ways, with volitional eye movements predominantly leading the head. We discuss our results in terms of models of visual exploratory behavior in panoramic scenes, both in virtual and real 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".