Implicit Adaptation is Fast, Robust and Independent from Explicit Adaptation
Bibliographic record
Abstract
Abstract During classical visuomotor adaptation, the implicit process is believed to emerge rather slowly; however, recent evidence has found this may not be true. Here, we further quantify the time-course of implicit learning in response to diverse feedback types, rotation magnitudes, feedback timing delays, and the role of continuous aiming on implicit learning. We find that implicit learning unfolds at a higher rate than conventionally expected in all feedback conditions. Increasing rotation size not only raises asymptotes, but also generally heightens explicit awareness, with no discernible difference in implicit rates. Cursor-jump and terminal feedback, with or without delays, predominantly enhance explicit adaptation while slightly diminishing the extent or the speed of implicit adaptation. In a continuous aiming reports condition, there is no discernible impact on implicit adaptation, and implicit and explicit adaptation progress at indistinguishable speeds. Finally, investigating the assumed negative correlation as an indicator of additivity of implicit and explicit processes, we observe weak associations at best across conditions. Our observation of implicit learning early in training in all tested conditions signifies how fast and robust our innate adaptation system is.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".