The rate of visuomotor adaptation correlates with cerebellar white‐matter microstructure
Bibliographic record
Abstract
Convergent experimental evidence points to the cerebellum as a key neural structure mediating adaptation to visual and proprioceptive perturbations. In a previous study, we have shown that activity in the anterior cerebellum varies with the rate of learning, with fast learners exhibiting more activity in this region than slow learners. Here, we investigated whether this variability in behavior may partly reflect inter-individual differences in the structural properties of cerebellar white-matter output tracts. For this purpose, we used diffusion-weighted magnetic resonance imaging to estimate fractional anisotropy (FA), and correlated the FA with the rate of adaptation to an optical rotation in 11 subjects. We found that FA in a region consistent with the superior cerebellar peduncle (SCP), containing fibers connecting the cerebellar cortex with motor and premotor cortex, was positively correlated with the rate of adaptation but not with the general level of performance or the initial deviation. The same pattern was observed in a region of the lateral posterior cerebellum. In contrast, FA in the angular gyrus of the posterior parietal cortex correlated positively both with the rate of adaptation and the overall level of performance. Our results show that the rate of learning a visuomotor task is associated with FA of cerebellar pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".