Savings in visuomotor learning are associated with connectivity changes within a cerebello-thalamo-cortical network encoding movement errors
Bibliographic record
Abstract
Savings refer to faster relearning upon re-exposure to a previously experienced movement perturbation. One theory posits that the brain recognizes past errors, enabling more efficient learning from them. If this is the case, there should be a modification in the neural response to errors during re-exposure to the perturbation. To investigate this hypothesis, we used fMRI to measure brain activity as participants adapted to a visuomotor perturbation across two sessions spaced one day apart, focusing on neural responses to movement errors. The magnitude of the movement error was incorporated into different types of GLMs to study error-related activation and co-activation (or functional connectivity). We identified a cerebello-thalamo-cortical network involved in processing movement errors during adaptation. We observed strengthened connectivity within this network during re-adaptation, particularly between the cerebellar lobule VI and the ventrolateral thalamus, as well as between the primary somatosensory cortex and the rostral cingulate motor zone. Importantly, participants with the greatest increases in connectivity strength also exhibited the largest amounts of savings. These results establish a link between the brain's ability to represent errors and the phenomenon of savings.
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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 | 0.000 |
| 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".