P.060 Altered oculomotor learning in thalamic stroke patients
Bibliographic record
Abstract
Background: Visuomotor learning can be elicited experimentally by displacing the target of a saccade during the ongoing eye movement. In healthy subjects, the resulting mismatch between expected and experienced visual error after saccade completion elicits a gradual adaptation of saccade amplitude. The goal of this project was to explore the role of cerebro-thalamo-cerebellar circuits in the dynamics of visuomotor learning. Methods: Patient RK is a 38-year-old right hand dominant male who suffered a focal thalamic stroke of the right thalamus, confined to ventral lateral posterior and ventral medial nuclei. We employed a standard saccadic adaptation paradigm and assessed dynamics of visuomotor learning by fitting a simple state-equation to saccade amplitudes towards the ipsi- and contralesional hemifield. Results: While RK was able to adapt saccade amplitudes in both directions, adaptation dynamics were different for leftward versus rightward saccades. Rightward, ipsilesional saccades exhibited a lower learning rate but similar retention of altered saccade metrics, compared to leftward, contralesional saccades. Conclusions: The present study assessed a patient with a focal lesion to the right cerebellar thalamus on a saccade adaptation paradigm. Results demonstrated slower visuomotor learning for saccades into the ipsilesional hemifield, suggesting an important contribution of cerebello-cortical projections mediated by thalamic relays for visuomotor 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".