L'effet de la pratique de mouvements par imagerie motrice sur l'apprentissage d'habiletés et l'organisation cérébrale fonctionnelle
Bibliographic record
Abstract
The main objective of this thesis was to examine the behavioural and functional cerebral reorganization produced by the mental practice of a motor skill in order to promote the use of this training method in neurologic rehabilitation. A first study was conducted to validate a new foot sequence task (FST) that allows precise measurement of the learning of sequential foot movements in normal subjects and patients with motor impairments. The results of a second study, which aimed at determining if motor imagery is essential to the learning of the FST through mental practice, showed that mental practice with motor imagery improved the learning of this task more than physical practice alone. Moreover, it was found that another type of mental practice, verbal rehearsal, can also improve the learning of the FST. However, only the subjects that had used motor imagery during practice remembered the sequence after several months without practice, which suggests that motor imagery contributes to learning but more importantly, that it is essential for the long term retention of motor skills learned with mental practice. A single-case study was then conducted to examine the effect of combined physical and mental practice on the learning of the FST in a patient who had had a stroke 4 months earlier. The results confirm that MP with motor imagery can improve the learning of this task when combined with physical practice and can contribute to the retention of the skill when used alone. Finally, a study using positron emission tomography showed that subjects who practiced the FST with motor imagery improved their performance, and displayed a functional reorganization similar to that observed after physical practice. However, practice with motor imagery seems to produce changes on the systems subserving motor preparation and anticipation rather than execution per se. Overall, the findings of this thesis confirm that MP with motor imagery is efficient at improving motor skill learning, and that this form of training changes the cerebral organization of subjects. The various results also provided new guidelines for future research and the use of mental practice in a neurologic rehabilitation.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".