Bibliographic record
Abstract
PURPOSE OF REVIEW: New insights into the psychophysiological determinants of performance changes and brain plasticity associated with motor sequence learning have recently been gained through behavioral and imaging studies in healthy individuals. In addition, using a variety of motor sequential paradigms in groups of patients affected by a movement disorder, major advances have been achieved in our understanding of the pathophysiological mechanisms underlying Parkinson's and Huntington's diseases, as well as primary forms of dystonia. RECENT FINDINGS: This review begins by describing the latest findings in normal participants with regards to the dynamic alterations in neural networks observed across the different phases of motor sequence learning. It then focuses on the hotly debated issue of motor memory consolidation, highlighting the results of novel studies that investigated the role of both day and night sleep, the neural substrates and the developmental evolution mediating this process. Finally, this paper addresses current work looking at motor sequence learning in movement disorders that helps to better comprehend the functional contribution of basal ganglia structures to this type of memory, to assess the impact of such diseases on related patterns of brain activation, as well as to identify the neuronal compensatory mechanisms educed by these basal ganglia disorders. SUMMARY: Such advances have major implications, not only for optimizing ways to learn new skilled behaviors in real-life situations, but also for guiding therapeutic approaches in patients with movement disorders.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".