Preserved motor learning after stroke is related to the degree of proprioceptive deficit
Bibliographic record
Abstract
BACKGROUND: Most motor learning theories posit that proprioceptive sensation serves an important role in acquiring and performing movement patterns. However, we recently demonstrated that experimental disruption of proprioception peripherally altered motor performance but not motor learning in humans. Little work has considered humans with central nervous system damage. The purpose of the present study was to specifically consider the relationship between proprioception and motor learning at the level of the central nervous system in humans. METHODS: Individuals with chronic (> 6mo) stroke and similarly aged healthy participants performed a continuous tracking task with an embedded repeating segment over two days and returned on a third day for retention testing. A limb-position matching task was used to quantify proprioception. RESULTS: Individuals with chronic stroke demonstrated the ability to learn to track a repeating segment; however, the magnitude of behavioral change associated with repeated segment-specific learning was directly related to the integrity of central proprioceptive processing as indexed by our limb-position matching task. CONCLUSION: These results support the importance of central sensory processing for motor learning. The confirmation of central sensory processing dependent motor learning in humans is discussed in the context of our prior report of preserved motor learning when sensation is disrupted peripherally.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".