Into the unknown: head-based selection is less dependent on peripheral information than gaze-based selection in 360-degree virtual reality scenes
Bibliographic record
Abstract
People naturally move both their head and eyes to attend to information. Yet, little is known about how the head and eyes coordinate in attentional selection due to the relative sparsity of past work that has simultaneously measured head and gaze behaviour. In the present study, participants were asked to view fully immersive 360-degree scenes using a virtual reality headset with built-in eye tracking. Participants viewed these scenes through a small moving window that was yoked either to their head or gaze movements. We found that limiting peripheral information via the head- or gaze-contingent windows affected head and gaze movements differently. Compared with free viewing, gaze-contingent viewing was more disruptive than head-contingent viewing, indicating that gaze-based selection is more reliant on peripheral information than head-based selection. These data dovetail with the nested effectors hypothesis, which proposes that people prefer to use their head for exploration into non-visible space while using their eyes to exploit visible or semi-visible areas of space. This suggests that real-world orienting may be more head-based than previously thought. Our work also highlights the utility, ecological validity, and future potential of unconstrained head and eye tracking in virtual reality.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".