The effects of practice and delays on tool-related motor skill learning and retention in Parkinson's disease
Bibliographic record
Abstract
The ability to perform motor skills relies heavily on procedural memory, mediated by various brain regions. Recent research demonstrated that individuals with Parkinson's disease (PD) learned new tool-related motor skills, but were impaired in retention after a three-week delay, possibly due to striatal dysfunction. In the current study, we evaluated whether shorter delays and more extensive practice might reduce motor skill retention deficits in PD. Individuals with mild to moderate PD (n = 18) and healthy age-matched controls (n = 18) were trained to use novel tools across four sessions, spaced one-day, one-week and three-weeks apart. Recall of tool attributes (e.g., function) and motor skill performance using tools was investigated using linear mixed models, which examined patterns of learning and forgetting over time. A p-value of .05 was used to determine statistical significance. Results showed that tool attribute recall was unimpaired in PD participants relative to controls. For motor skill performance, PD participants demonstrated intact motor skill learning within each session, but they did not retain these skills between sessions, and were impaired even after a one-day delay. This pattern of performance suggests that the striatum plays a critical role in retention, but not learning, of motor skills required in tool use. Interestingly, despite between-session forgetting of motor skills, PD participants demonstrated gradual, but significant improvement across four sessions with additional practice. These findings suggest that individuals with PD may not have an absolute deficit in procedural memory, and with sufficient practice they can acquire and retain new motor skills. Acknowledgments: Parkinson's Disease Foundation (PDF), Canadian Foundation for Innovation (CFI), Canadian Institutes of Health Research (CIHR), Sun Life Financial
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".