Exploring the Rate of Implicit and Explicit Learning in Motor Adaptation: Effects of Rotation Size, Aiming Strategy, and Delayed Feedback
Bibliographic record
Abstract
Motor adaptation relies on implicit and explicit learning systems. Although the extent of implicit reach aftereffects is explored, less is known about the rate it emerges in visuomotor adaptation Here, we measured the time courses of implicit reach aftereffects, measured after every single training trial, as a function of the size of the perturbation, and as a function of delay in terminal feedback. Using exponential fits to the data, we found that the asymptotes of implicit reach aftereffects increased with larger rotation sizes, while the relative rates of change decreased. However, in absolute terms, the first trial elicited ~2.5° of change in all conditions, suggesting the rate of change in response to the perturbation was independent of size. Aiming trials to measure explicit awareness in late training demonstrated a greater explicit contribution for larger perturbations. Therefore, we explored the effect of continuous aiming compared to only aiming at the end, which elucidated a higher rate but lower extent of implicit adaptation for a 45° rotation. Also, the continuous aiming group exhibited 80% greater explicit strategy use, with the onset of cognitive strategy emerging only slightly faster than implicit aftereffects. Despite studies suggesting that feedback delay leads to only cognitive strategy, we found that intervening reach aftereffects again emerged quite quickly and to a similar extent as that produced without delay. These results show that the unconscious component of learning, when directly tested during classical visuomotor adaptation, is quite robust and quickly emerges across various visuomotor adaptation paradigms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".