Where's my hand? Updating proprioception and prediction for motor learning
Bibliographic record
Abstract
Knowing the position of one's limbs is essential for moving them, and hence it makes sense that several signals provide information on limb position. These include vision and proprioception, as well as predictive estimates based on efference copies of the movement. And since both proprioceptive and predictive estimates of hand position have been shown to change when we adapt our movements to altered visual feedback of the hand (i.e., a visuomotor rotation), it is unclear how much each contributes to post-adaptation changes in where we localize our hand. By having participants localize their hand with and without efference signals, we can tease the two contributions apart. In summary, we find that 1) visuomotor training leads to changes in both predicted, efferent-based and proprioceptive estimates of the hand, but that the change in prediction is smaller than that of perception, 2) these changes do not vary with awareness nor the size of the visual perturbation, but do differ in older adults, and 3) proprioception-based changes occur very rapidly, while efference-based contributions come about less rapidly, at about the same rate as motor changes. These findings imply that both these sources for estimating limb position are updated during learning and in turn contribute to changes in motor performance. This means that the plasticity in our estimates of limb position depends on multiple sources of feedback, and our brains likely take into account the peculiarities of the separate signals to arrive at a robust limb position signal.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".