Optimizing movement performance with altered sensation
Bibliographic record
Abstract
Somatosensory feedback provides information about ongoing performance and success of movements. Individuals who experience disrupted somatosensory feedback are often faced with re-learning basic goal-directed movements. The present experiment assessed the impact of induced paresthesia, in an otherwise healthy nervous system, on movement performance. Sixteen healthy young (10 females, M age = 21.7) participants attended two sessions on two separate days and completed four conditions: paresthsia/no paresthesia and vision/no vision of the target. Order of the four conditions and associated target locations were blocked and counterbalanced across participants. Paresthesia was induced using a constant current stimulator and confirmed with standardized sensory testing. Participants performed 100 trials per condition and were motivated to improve their movement time (MT) by providing incentive for accurate movements with associated shorter MTs. Movements were recorded using a 3D motion analysis system (300Hz) and analyzed using a 2 (paresthesia condition) by 2 (vision condition) by 2 (early/ late performance) repeated measures ANOVA. Analyses revealed no significant differences for reaction time or constant error. Although MT and time to peak velocity (ttPV) improved with practice, paresthesia led to significantly longer MTs and ttPV. A paresthesia by vision condition interaction revealed that movement endpoints were more variable without vision only when paresthesia was not present. Findings will be discussed in the context of sensorimotor integration and the implications for upper limb rehabilitation.Acknowledgments: This research was funded by the Manitoba Medical Service Foundation, the Manitoba Health Research Council, and the Natural Sciences and Engineering Research Council of Canada. The authors would like to thank Leah Harpelle, Kelsey Brown, and Michele Berthelette for their assistance with target set-up, data collection, data organization, respectively.
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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) | 0.000 | 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) | 0.002 | 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".