The neural control of fast vs. slow vergence eye movements
Bibliographic record
Abstract
When looking between targets located in three-dimensional space, information about relative depth is sent from the visual cortex to the motor control centers in the brainstem, which are responsible for generating appropriate motor commands to move the eyes. Surprisingly, how the neurons in the brainstem use the depth information supplied by the visual cortex to precisely aim each eye on a visual target remains highly controversial. This review will consider the results of recent studies that have focused on determining how individual neurons contribute to realigning gaze when we look between objects located at different depths. In particular, the results of new experiments provide compelling evidence that the majority of saccadic neurons dynamically encode the movement of an individual eye, and show that the time-varying discharge of the saccadic neuron population encodes the drive required to account for vergence facilitation during disconjugate saccades. Notably, these results suggest that an additional input (i.e. from a separate vergence subsystem) is not required to shape the activity of motoneurons during disconjugate saccades. Furthermore, whereas motoneurons drive both fast and slow vergence movements, saccadic neurons discharge only during fast vergence movements, emphasizing the existence of distinct premotor pathways for controlling fast vs. slow vergence. Taken together, these recent findings contradict the traditional view that the brain is circuited with independent pathways for conjugate and vergence control, and thus provide an important new insight into how the brain controls three-dimensional gaze shifts.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".