Saccadic Trajectories Receive Online Correction: Evidence for a Feedback-Based System of Oculomotor Control
Bibliographic record
Abstract
Although a considerable amount of research has investigated the planning and production of saccadic eye movements, it remains unclear whether (a) central planning processes prior to movement onset largely determine these eye movements or (b) they receive online correction during the actual trajectory. To investigate this issue, the authors measured the spatial position of the eye at specific kinematic markers during saccadic movements (i.e., peak acceleration, peak velocity, peak deceleration, saccade endpoint). In 2 experiments, the authors examined saccades ranging in amplitude from 4 to 20 degrees and computed the variability profiles (SD) of eye position at each kinematic marker and the proportion of explained variance (R2) between each kinematic marker and the saccade endpoint. In Experiment 1, the authors examined differences in the kinematic signature of saccadic online control between eye movements made in gap or overlap conditions. In Experiment 2, the authors examined the online control of saccades made from stored target information after delays of 500, 1,500, and 3,500 ms. Findings evince a robust and consistent feedback-based system of online oculomotor control during saccadic eye movements.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".