Influence of training environment and cognitive deficits on use of feedback for motor learning in chronic stroke
Bibliographic record
Abstract
Feedback provision is an essential component of motor learning for improving upper limb recovery in people with stroke. Along with sensorimotor impairments, many individuals post-stroke have cognitive deficits that can influence arm motor recovery. However, few studies have identified whether the training environment and presence of cognitive deficits influences the ability to use feedback in individuals post-stroke. We evaluated the influence of the training environment and cognitive impairments on the ability to use feedback to enhance arm motor recovery. Twenty-four subjects with chronic post-stroke upper limb hemiparesis were randomized to practice pointing movements in a 3D virtual environment (VE) or a similarly designed physical environment (PE; n=12/group) for 12 sessions (72 trials/session, 3 days/week). All participants were provided with feedback about movement speed (Knowledge of Results) and trunk displacement (Knowledge of Performance). Neurocognitive functioning was assessed only before task practice (PRE), while kinematic assessments were carried out at PRE, immediately after (POST) and 3 months (RET) after task practice. Repeated measures ANOVAs with mixed models assessed the changes in kinematic outcomes. Neurocognitive function was correlated with kinematic outcomes. Those training in the VE had greater endpoint speed and ranges of shoulder horizontal adduction, shoulder flexion and elbow extension. They also tended to use less trunk displacement. Kinematic deficits influenced the ability to use feedback in people with chronic stroke for upper limb motor learning and recovery. Information about the presence of these deficits can help in the selection of the most appropriate interventions for maximizing arm motor recovery and motor learning in chronic stroke.
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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 | 0.002 |
| 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) | 0.001 | 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".