Reduced feedback barely slows down proprioceptive recalibration
Bibliographic record
Abstract
Abstract Introducing altered visual feedback of the hand results in quick adaptation of reaching movements. And while this may be partly due to explicit strategies, our lab has shown that implicit changes like reach aftereffects and shift in estimates of the unseen hand, can also emerge and even saturate within a few training trials. The goal of the current study is to determine whether these rapid changes in unseen hand position that occur during classical visuomotor adaptation are diminished or slowed when feedback during training is reduced. We reduced feedback by either providing visual feedback only at the end of the reach (terminal feedback) or constraining hand movements to reduce efferent contribution (exposure). We measured changes as participants completed reaches with a 30° rotation, a −30° rotation and clamped visual feedback, with these two “impoverished” training conditions, along with classical visuomotor adaptation training, while continuously estimating their felt hand position. Classic continuous-cursor training produced exemplary learning curves and rapid and robust shifts in felt hand position. Training with terminal feedback slightly reduced the initial rate of change in overall adaptation and but not the magnitude of shifts in felt hand position. Finally using a robot to constrain and deviate hand movement direction, called exposure training, only delayed saturation of proprioceptive changes by a single trial and these changes were slightly smaller than those during classical training. Taken together, adaptation and shifts in felt hand position are a rapid and robust responses to sensory mismatches and are only slightly modulated when feedback is reduced. This means that, given a visuo-proprioceptive mismatch, the resulting shift in sense of limb position can contribute to movements from the start of adaptation.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".