Gaze-, Eye-, and Head-Movement Dynamics During Closed- and Open-Loop Gaze Pursuit
Bibliographic record
Abstract
Horizontal step-ramp stimuli were used to examine gaze-, eye-, and head-movement dynamics during head-unrestrained pursuit in two rhesus monkeys. In a first series of experiments, we characterized and compared head-restrained (HR) and -unrestrained (HU) pursuit responses to unpredictable, nonperiodic, constant velocity (20-80 degrees/s) stimuli. When the head was free to move, both monkeys used a combination of eye and head motion to initially fixate and then pursue the target. The pursuit responses (i.e., gaze responses) were highly stereotyped and nearly identical among the HR and HU conditions for a given step-ramp stimulus. In the HU condition, initial eye and initial head acceleration tended to increase as a function of target velocity but did not vary systematically with initial target eccentricity. In a second series of experiments, step-ramp stimuli (40 degrees/s) were presented, and, approximately 125 ms after pursuit onset, a constant retinal velocity error (RVE) was imposed for a duration of 300 ms. In each monkey, HR and HU gaze velocity was similarly affected by stabilizing the target with respect to the monkey's fovea (i.e., RVE = 0 degrees/s) and by moving the target with constant retinal velocity errors (i.e., RVE = +/- 10 degrees/s). In the HU condition, changes in both eye and head velocity trajectories contributed to the observed gaze velocity responses to imposed RVEs. We conclude that eye and head movements are not independently controlled during HU pursuit but rather are controlled, at least in part, by a shared upstream controller within the pursuit pathways.
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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 | 0.000 |
| 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 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".