Cortico-cerebellar network involved in saccade adaptation
Bibliographic record
Abstract
Saccade adaptation is the learning process that ensures that vision and saccades remain calibrated. The central nervous system network involved in these adaptive processes remains unclear because of difficulties in isolating the learning process from the correlated visual and motor processes. Here we imaged the human brain during a novel saccade adaptation paradigm that allowed us to isolate neural signals involved in learning independent of the changes in the amplitude of corrective saccades usually correlated with adaptation. We show that the changes in activation in the ipsiversive cerebellar vermis that track adaptation are not driven by the changes in corrective saccades and thus provide critical supporting evidence for previous findings. Similarly, we find that activation in the dorsomedial wall of the contraversive precuneus mirrors the pattern found in the cerebellum. Finally, we identify dorsolateral and dorsomedial cortical areas in the frontal and parietal lobes that encode the retinal errors following inaccurate saccades used to drive recalibration. Together, these data identify a distributed network of cerebellar and cortical areas and their specific roles in oculomotor learning. NEW & NOTEWORTHY The central nervous system constantly learns from errors and adapts to keep visual targets and saccades in registration. We imaged the human brain while the gain of saccades adapted to a visual target that was displaced while the eye was in motion, inducing retinal error. Activity in the cerebellum and precuneus tracked learning, whereas parts of the dorsolateral and dorsomedial frontal and parietal cortex encoded the retinal error used to drive learning.
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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 | 0.000 |
| 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".