Proprioceptive recalibration is a purely implicit process
Bibliographic record
Abstract
Recently there has been renewed interest in understanding the different contributions of explicit and implicit learning to motor learning, such as visuomotor rotation adaptation. Visuomotor rotation adaptation also evokes proprioceptive recalibration, but the contribution of explicit and implicit learning to proprioceptive recalibration is unknown. To investigate this, we instructed one group of participants with an explicit strategy to counter the rotation and another group received no such instruction (see Werner et al., 2015). Learning in the explicitly instructed group is almost instantaneous, but settles more slowly in the implicit group. When asked not to apply the learned strategy, the implicit group is not able to do so, whereas the explicit group can. These results both show a typical difference in learning explained by the explicit and implicit instructions. We also recorded estimates of hand position before and after training in each group. Crucially, participants either moved their own hand, enabling them to use (updated) predicted sensory consequences in estimating their hand position, or the apparatus moved their hand, so that only proprioception was available. Active localization leads to larger shifts in localization than passive movements, but the shifts in localization are identical for the implicitly and explicitly instructed groups. The availability of prediction also doesn't interact with instruction. This is clear evidence that proprioceptive recalibration is an exclusively implicit process that does not rely on motor errors, or instructions on how to counter them, but rather on the discrepancy between visual and proprioceptive feedback.Acknowledgments: Supported by DFG HA 6861/2-1 to BMtH; NSERC to DYPH
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".