A natural look at scanpath theory: The way we move our head and eyes predicts scene recognition
Bibliographic record
Abstract
It has long been thought that visual perception is represented in sensorimotor processes that unfold over time. One prominent theory states that our memory for a scene contains both the scene's content and the motor commands used to explore the scene (i.e., eye movements). Scanpath theory (Noton & Stark, 1971) has long been contested, with studies providing evidence both for and against it. That past work, however, did not include the fact that visual perception is embodied within an active system of effectors; namely, that people normally move their head as well as their eyes when exploring visible space. The present work tested scanpath theory in fully immersive 360 degree scenes leaving individuals free to move their heads and well as their eyes while they explored scenes for a subsequent memory test. During both encoding and recognition, we recorded their head and eye movements using a virtual reality headset equipped with eye and head tracking. Relevant to scanpath theory, as well as to embodied conceptualizations of perception and memory, our results show that repeating certain head and eye movement patterns made during encoding renders those scenes more likely to be recognized.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".