Rolling motion makes the eyes roll: torsion during smooth pursuit eye movements
Bibliographic record
Abstract
Introduction: We frequently observe horizontal and vertical movements of the eyes, but what is less often noted is the fact that the eye can also rotate about the line of sight, resulting in torsional eye movements. Torsion may serve to compensate for rotations of the head, but its exact function is unclear. Here we examine the functional role of torsion during smooth pursuit eye movements by testing whether torsion can be visually triggered. Methods: Observers (n=8) tracked a random-dot pattern, which moved to the left or right at 10 deg/s, and rotated around its center, either clockwise or counter-clockwise relative to translational motion, at speeds ranging from 151-208 rad/s. In control experiments, we varied stimulus size (4-12 deg) and elevation of gaze. We recorded 3D eye position with a head-mounted Chronos ETD in head-fixed observers. Results: We discovered strong torsion in the direction of stimulus rotation during smooth pursuit. Torsion was fastest in response to natural rotation, the direction an object would rotate if it was rolling on the ground. Natural and unnatural rotation triggered two different patterns of torsion: natural rotation resulted in smooth, continuous torsion at a significantly higher rotational speed than unnatural rotation, which triggered a torsional nystagmus. Natural rotation also produced more accurate pursuit. Torsion increased as a function of stimulus size, but effects were constant across gaze elevations. Conclusion: We provide the first evidence of visually-triggered torsion during pursuit. The torsional strength varied systematically with visual stimulus properties such as direction and size, indicating that torsion could play an important role in stabilizing pursuit during image rotation. Listing’s Law, which describes the kinematics of 3D eye movements and predicts zero torsion during pursuit, does not hold here. Meeting abstract presented at VSS 2013
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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 | 0.000 | 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 | 0.000 |
| 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".