Scanpath Theory in Virtual Reality
Bibliographic record
Abstract
It has long been thought that visual perception is represented in sensorimotor processes that unfold over time. One prominent theory predicts that our memory for a scene consists of both the scene content and the motor commands (i.e., eye movements) used to explore that scene. This Scanpath Theory (Noton & Stark, Science 171 (1971) 308-311) has long been contested, with many studies providing evidence both for, and against it. That past work, however, has failed to account for the fact that visual perception is embodied within an active system of effectors, namely, that people routinely move both their eyes and head to explore visible space. In the present work we tested Scanpath Theory while observers were free to move within a 360-degree VR environment. Their task was to encode and later recognise panoramic scenes within this fully immersive world. During both encoding and recognition, we recorded their eye and head movements using a VR headset equipped with eye and head tracking. Our results reveal that eye and head movement patterns are diagnostic of memory performance; and that scene recognition improves when certain movements that had occurred during encoding are repeated. Finally, including head movement measures enhances performance prediction, strengthening the evidence for Scanpath Theory, and reinforcing the fact that the head moves in service of the eyes in allocating attention.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".