Motor adaptation and proprioceptive recalibration in the absence of on-line movement corrections
Bibliographic record
Abstract
Reaching to targets in a virtual reality environment with misaligned visual feedback of the results in changes in movements (motor adaptation) and sense of felt position (proprioceptive recalibration) (Cressman and Henriques, 2009). In the current study we looked to determine if changes in the motor and sensory system are dependent on experiencing endpoint feedback and hence completing on-line movement corrections to the target during reach training. Subjects performed a shooting task to three targets with a cursor that was rotated 30° counter-clockwise relative to their hand. Specifically, subjects were instructed to move as quickly and accurately as possible, such that the visual representation of their passed through the target location. Thus, subjects did not complete on-line movement corrections and the visual representation of their did not stop at the target. Following training, subjects reached to the same three targets without visual feedback to determine motor adaptation in the form of aftereffects and provided estimates of their position relative to the three targets to establish proprioceptive recalibration. Results revealed that subjects adapted their reaches to all three targets and recalibrated their sense of felt position. Moreover, motor and sensory changes were similar in magnitude to those observed when subjects aimed to targets and performed on-line movement corrections (Cressman and Henriques, 2009). Thus, motor adaptation and proprioceptive recalibration are not dependent on on-line corrections. Furthermore, motor and sensory changes arise even when subjects do not have the experience of their hand landing on a target. Acknowledgments: Supported by NSERC
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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 | 0.001 |
| 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".