The influence of augmented somatosensory feedback on visuomotor adaptation and inter-limb transfer
Bibliographic record
Abstract
Healthy individuals can quickly adapt their movements when aiming in a virtual reality environment in which the visual representation of their hand is rotated relative to their actual hand motion. Specifically, individuals learn to compensate for the misaligned visual feedback by aiming to the left or right of the target, in the opposite direction of the rotation. These altered movements continue even when the rotated feedback is removed (i.e. individuals exhibit aftereffects). The purpose of the current study was to determine if augmented somatosensory feedback would be benefit motor learning in elderly participants and hence lead to greater aftereffects. Two groups of older adults (age range:40-75 years old ) aimed to targets when: 1) the cursor accurately indicated hand position, and 2) the cursor was rotated 30 degrees counter-clockwise from the actual hand position. One group of subjects received enhanced somatosensory feedback at the end of their reaching movements such that the robot handle they were holding vibrated with a frequency of 5 Hz for 1500 msec. Results showed that aftereffects were similar for both groups following reaches with the distorted visual feedback, with no significant influence of the augmented sensory feedback. Moreover, these changes in reaches transferred to the opposite (untrained) non-dominant hand, regardless of whether participants received enhanced sensory feedback or not. These findings suggest that the central nervous system ignores somatosensory information and relies more on visual feedback when adapting to altered visual feedback.
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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) | 0.001 | 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".