Proprioceptive training improves sense of felt hand position but does not influence implicit visuomotor adaptation
Bibliographic record
Abstract
Visuomotor adaptation arises when reaching in an altered visual environment, where one's seen hand position does not match their actual felt (i.e., proprioceptive) hand position in space. Here, we investigated if proprioceptive training (PT) benefits visuomotor adaptation, and if these benefits arise due to implicit (unconscious) or explicit (conscious strategy) processes. A total of 72 participants were divided equally into 3 groups: Proprioceptive training with feedback (PTWF), Proprioceptive training no feedback (PTNF), and Control (CTRL). The PTWF and PTNF groups completed proprioceptive training (PT), where a participant's hand was passively moved to an unknown reference location and they indicated the felt position of their unseen hand relative to their body midline on every trial. The PTWF group received verbal feedback with respect to their response accuracy on the middle 60% of trials. The CTRL group did not complete PT and instead sat quietly during this time. Following PT or time delay, all three groups reached with 30° rotated cursor feedback, followed by a series of no-cursor reaches to assess implicit and explicit adaptation. Results indicated that the PTWF group improved their sense of felt hand position following PT. However, this improved proprioceptive acuity did not benefit visuomotor adaptation, as all three groups showed similar visuomotor adaptation across rotated reach training trials. Visuomotor adaptation arose implicitly, with minimal explicit contribution for all three groups. Thus, these results suggest that passive proprioceptive training with feedback does not benefit, nor hinder, implicit visuomotor adaptation.Acknowledgments: Supported by the Natural Sciences and Engineering Research Council of Canada [EKC].
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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".