Three-Dimensional Eye-Head Coordination Is Implemented Downstream From the Superior Colliculus
Bibliographic record
Abstract
How the brain transforms two-dimensional visual signals into multi-dimensional motor commands, and subsequently how it constrains the redundant degrees of freedom, are fundamental problems in sensorimotor control. During fixations between gaze shifts, the redundant torsional degree of freedom is determined by various neural constraints. For example, the eye- and head-in-space are constrained by Donders' law, whereas the eye-in-head obeys Listing's law. However, where and how the brain implements these laws is not yet known. In this study, we show that eye and head movements, elicited by unilateral microstimulations of the superior colliculus (SC) in head-free monkeys, obey the same Donders' strategies observed in normal behavior (i.e., Listing's law for final eye positions and the Fick strategy for the head). Moreover, these evoked movements showed a pattern of three-dimensional eye-head coordination, consistent with normal behavior, where the eye is driven purposely out of Listing's plane during the saccade portion of the gaze shift in opposition to a subsequent torsional vestibuloocular reflex slow phase, such that the final net torsion at the end of each head-free gaze shift is zero. The required amount of saccade-related torsion was highly variable, depending on the initial position of the eye and head prior to a gaze shift and the size of the gaze shift, pointing to a neural basis of torsional control. Because these variable, context-appropriate torsional saccades were correctly elicited by fixed SC commands during head-free stimulations, this shows that the SC only encodes the horizontal and vertical components of gaze, leaving the complexity of torsional organization to downstream control systems. Thus we conclude that Listing's and Donders' laws of the eyes and head, and their three-dimensional coordination mechanisms, must be implemented after the SC.
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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 | 0.001 |
| 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".