Interdependency of explicit and implicit learning processes for motor skill adaptation
Bibliographic record
Abstract
In this experiment, post-trial knowledge of results (KR) was used to promote explicitly guided, re-adaptation of an implicitly acquired adaptation. There is debate about the independence of implicit and explicit learning processes during adaptation. In a key study, implicit learning based on error in expected sensory consequences was found to guide learning independent of "correct" strategic, explicit processes (Mazzoni & Krakauer, 2006). If these processes are independent later explicit re-adaptation should not influence what has been implicitly acquired (evidenced by unchanged after-effects in a normal environment). Fifteen participants gradually adapted targeted reaching movements to a 30º CW visual rotation (with cursor trajectory (CT) feedback of the first half of each trial). After implicit adaptation and tests of after-effects, participants practiced with correct or incorrect (+/-15º) KR about the accuracy of the CT's endpoint. Both incorrect KR groups showed high variable error relative to the correct group, indicative of strategic adjustments to reduce endpoint error. Only participants in the +15º error group showed re-adaptation based on KR. Importantly, these explicitly-induced changes were manifest as larger after-effects (~7º increase) following exposure to erroneous KR than before. This suggests an interdependency of explicit and implicit processes whereby the internal model for reaching can be updated by explicit processes, resulting in augmented after-effects.Acknowledgments: The final author would like to acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (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 | 0.002 | 0.010 |
| 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 | 0.001 |
| 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".