Saccadic Compensation for Smooth Eye and Head Movements During Head-Unrestrained Two-Dimensional Tracking
Bibliographic record
Abstract
Spatial updating is the ability to keep track of the position of world-fixed objects while we move. In the case of vision, this phenomenon is called spatial constancy and has been studied in head-restraint conditions. During head-restrained smooth pursuit, it has been shown that the saccadic system has access to extraretinal information from the pursuit system to update the objects' position in the surrounding environment. However, during head-unrestrained smooth pursuit, the saccadic system needs to keep track of three different motor commands: the ocular smooth pursuit command, the vestibuloocular reflex (VOR), and the head movement command. The question then arises whether saccades compensate for these movements. To address this question, we briefly presented a target during sinusoidal head-unrestrained smooth pursuit in darkness. Subjects were instructed to look at the flash as soon as they saw it. We observed that subjects were able to orient their gaze to the memorized (and spatially updated) position of the flashed target generally using one to three successive saccades. Similar to the behavior in the head-restrained condition, we found that the longer the gaze saccade latency, the better the compensation for intervening smooth gaze displacements; after about 400 ms, 62% of the smooth gaze displacement had been compensated for. This compensation depended on two independent parameters: the latency of the saccade and the eye contribution to the gaze displacement during this latency period. Separating gaze into eye and head contributions, we show that the larger the eye contribution to the gaze displacement, the better the overall compensation. Finally, we found that the compensation was a function of the head oscillation frequency and we suggest that this relationship is linked to the modulation of VOR gain. We conclude that the general mechanisms of compensation for smooth gaze displacements are similar to those observed in the head-restrained condition.
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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) | 0.001 | 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".