Eye-head coordination during exploration of 360-degree scenes in virtual reality
Bibliographic record
Abstract
People naturally move both their head and eyes to attend to information. Yet, studies of attentional orienting normally immobilize the head in a chin rest in order to focus on eye movements, thus questioning their ecological validity. Here, using a virtual reality headset with built-in eye tracking, participants were asked to view indoor and outdoor, fully immersive, 360-degree scenes while their head and eyes were tracked. In order to investigate eye-head coordination, participants viewed the scenes through a small moving window that was yoked either to their head or eye movements. We found that perturbations induced by the head- or gaze-contingent windows affected head and eye movements differentially, in line with their distinct roles in head-eye coordination. Compared with windowless viewing, gaze-contingent viewing was more disruptive than head-contingent viewing, indicating a functional separation between head and eye. Indeed, gaze-contingency actually decreased the coupling between head and gaze movements in 360-degree scene exploration, while head-contingent viewing looked more like windowless viewing. These data dovetail with the nested effectors hypothesis, which proposes that the head prefers exploration into non-visible space while the eyes prefer to exploit visible areas delivered by the head. It also suggests that real-world orienting may be much more head-based than previously thought. We discuss our findings in relation to the cognitive repercussions of eye vs. head movements, as well as highlighting the utility and ecological validity of unconstrained eye and head 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 | 0.000 | 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 | 0.001 |
| 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".