Investigating implicit and explicit contributions to dual visuomotor adaptation
Bibliographic record
Abstract
The ability to seamlessly switch between different visuomotor mappings is critical for effective interactions in a dynamic environment. This experiment aimed to establish the implicit (unconscious) and explicit (conscious strategy) contributions to adapting one’s reaches to two small visuomotor mappings simultaneously (DUAL visuomotor adaptation). 59 right-handed participants were divided into two groups: a DUAL adaptation group and a SINGLE adaptation group. The DUAL group trained to reach when cursor feedback was rotated 20° clockwise relative to hand motion when a left target was displayed and 20° counterclockwise relative to hand motion when a right target was displayed. The SINGLE group trained to reach with just one 20° cursor distortion (clockwise or counterclockwise) to both the left and right targets. Results revealed that while both groups adapted their reaches to the distorted cursor feedback, it took the DUAL group significantly more trials for reach adaptation to plateau in comparison to the SINGLE group. Furthermore, the magnitude of final visuomotor adaptation achieved in the DUAL group after 360 training trials was less than the SINGLE group who reached with a clockwise cursor distortion for 180 trials. Similarly, the DUAL group demonstrated significantly less implicit adaptation than the Single group after 180 trials. However, after 360 training trials, both groups demonstrated similar levels of implicit adaptation. There was no evidence of explicit adaptation in either group. Together, these results highlight the role of implicit processes in simultaneously updating two visuomotor mappings to a small cursor distortion.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".