Evidence for Gaze Feedback to the Cat Superior Colliculus: Discharges Reflect Gaze Trajectory Perturbations
Bibliographic record
Abstract
Rapid coordinated eye-head movements, called saccadic gaze shifts, displace the line of sight from one location to another. A critical structure in the gaze control circuitry is the superior colliculus (SC) of the midbrain, which drives gaze saccades by relaying cortical commands to brainstem eye and head motor circuits. We proposed that the SC lies within a gaze feedback loop and generates an error signal specifying gaze position error (GPE), the distance between target and current gaze positions. We investigated this feedback hypothesis in cats by briefly stopping head motion during large ( approximately 50 degrees ) gaze saccades made in the dark. This maneuver interrupted intended gaze saccades and briefly immobilized gaze (a plateau). After brake release, a corrective gaze saccade brought the gaze on goal. In the caudal SC, the firing frequency of a cell gradually increased to a maximum that just preceded the optimal gaze saccade encoded by the position of the cell and then declined back to zero near gaze saccade end. In brake trials, the activity level just preceding a brake-induced plateau continued steadily during the plateau and waned to zero only near the end of the corrective saccade. The duration of neural activity was stretched to reflect the increased time to target acquisition, and firing frequency during a plateau was proportional to the GPE of the plateau. In comparison, in the rostral SC, the duration of saccade-related pauses in fixation cell activity increased as plateau duration increased. The data show that the cat's SC lies in a gaze feedback loop and that it encodes GPE.
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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".