Music Attenuates Excessive Visual Guidance of Skilled Reaching in Advanced but Not Mild Parkinson's Disease
Bibliographic record
Abstract
Parkinson's disease (PD) results in movement and sensory impairments that can be reduced by familiar music. At present, it is unclear whether the beneficial effects of music are limited to lessening the bradykinesia of whole body movement or whether beneficial effects also extend to skilled movements of PD subjects. This question was addressed in the present study in which control and PD subjects were given a skilled reaching task that was performed with and without accompanying preferred musical pieces. Eye movements and limb use were monitored with biomechanical measures and limb movements were additionally assessed using a previously described movement element scoring system. Preferred musical pieces did not lessen limb and hand movement impairments as assessed with either the biomechanical measures or movement element scoring. Nevertheless, the PD patients with more severe motor symptoms as assessed by Hoehn and Yahr (HY) scores displayed enhanced visual engagement of the target and this impairment was reduced during trials performed in association with accompanying preferred musical pieces. The results are discussed in relation to the idea that preferred musical pieces, although not generally beneficial in lessening skilled reaching impairments, may normalize the balance between visual and proprioceptive guidance of skilled reaching.
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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 | 0.000 |
| 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".