Motor learning in neurological rehabilitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While most upper limb training interventions in neurological rehabilitation are based on established principles of motor learning and neural plasticity, recovery potential may be improved if the focus includes remediating an individual's specific motor impairment within the framework of a motor control theory. This paper reviews current theories of motor control and motor learning and describes how they can be incorporated into training programs to enhance sensorimotor recovery in patients with neurological lesions. An emphasis is placed on dynamical systems theory and the use of new technologies such as virtual, augmented and mixed reality applications for rehabilitation to facilitate learning.Implications for RehabilitationKinematic abundance allows the healthy nervous system to produce different combinations of joint rotations to perform a desired task.The structure of practice to improve the movement repertoire in rehabilitation should take into account the kinematic abundance of the system.Learning can be enhanced by varied practice with feedback about key movement elements.Virtual reality environments provide opportunities to manipulate the structure and schedule of practice and feedback.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it