Specific Increases within Global Decreases: A Functional Magnetic Resonance Imaging Investigation of Five Days of Motor Sequence Learning
Bibliographic record
Abstract
Our capacity to learn movement sequences is fundamental to our ability to interact with the environment. Although different brain networks have been linked with different stages of learning, there is little evidence for how these networks change across learning. We used functional magnetic resonance imaging to identify the specific contributions of the cerebellum and primary motor cortex (M1) during early learning, consolidation, and retention of a motor sequence task. Performance was separated into two components: accuracy (the more explicit, rapidly learned, stimulus-response association component) and synchronization (the more procedural, slowly learned component). The network of brain regions active during early learning was dominated by the cerebellum, premotor cortex, basal ganglia, presupplementary motor area, and supplementary motor area as predicted by existing models. Across days of learning, as performance improved, global decreases were found in the majority of these regions. Importantly, within the context of these global decreases, we found specific regions of the left M1 and right cerebellar VIIIA/VIIB that were positively correlated with improvements in synchronization performance. Improvements in accuracy were correlated with increases in hippocampus, BA 9/10, and the putamen. Thus, the two behavioral measures, accuracy and synchrony, were found to be related to two different sets of brain regions-suggesting that these networks optimize different components of learning. In addition, M1 activity early on day 1 was shown to be predictive of the degree of consolidation on day 2. Finally, functional connectivity between M1 and cerebellum in late learning points to their interaction as a mechanism underlying the long-term representation and expression of a well learned skill.
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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 | 0.001 |
| 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".