Implicit and Explicit Adaptation Just Don’t Add Up
Bibliographic record
Abstract
Our ability to adapt movements is likely supported by both implicit and explicit adaptation. Many studies and methods (tacitly) rely on the assumption these two add linearly, but this seems an unlikely neural mechanism and has not been tested rigorously. Here we test this by measuring implicit and explicit adaptation independently, using exclude strategy trials and re-aiming responses. We have two control groups that did not do re-aiming trials, and either received instructions on the perturbation or not. Testing predictions from additivity using both simple sums (strictly additive) and weighted sums (loosely additive), does not confirm additivity: there is no relation between implicit and explicit adaptation in our data. We also re-analyze data from nine other studies (total N=831) and only observe loose additivity in 6/43 subgroups (N=128). While this larger data set suggests explicit and implicit adaptation are combined in some way, it does not support linear addition. We conclude that implicit and explicit adaptation should both be directly and separately measured. More importantly, we are far away from understanding how various motor adaptation processes combine to shape behaviour.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".