Savings in visuomotor learning is associated with connectivity changes within a cerebello-thalamo-cortical network encoding movement errors
Bibliographic record
Abstract
Savings refers to faster relearning upon re-exposure to a previously experienced movement perturbation. One theory suggests that the brain recognizes past errors and is therefore more able to learn from them. If true, there should be a modification of the neural response to errors during re-exposure to a perturbation. To test this idea, we imaged the brains of participants who underwent two sessions (1 day apart) of adaptation to a visuomotor perturbation and investigated brain responses to movement errors. The magnitude of movement error was entered 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 the processing of movement errors during adaptation. We found that connectivity between regions of this network (i.e., between the cerebellum and the thalamus, and between the primary somatosensory cortex and the anterior cingulate cortex) became stronger during re-adaptation. Importantly, participants with the largest increases in connectivity strength were those who demonstrated the largest amounts of savings. These results establish a relationship between the ability of the brain 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".