Explicit and implicit visuomotor adaptation differ based on the method of assessment
Bibliographic record
Abstract
The process dissociation procedure (PDP; action task) and the Verbal report framework (VRF; perceptual task) have been put forth to examine implicit visuomotor adaptation (IA) and explicit visuomotor adaptation (EA). It is unclear if the two methods (PDP vs. VRF) measure similar IA and EA, as the neural processes underlying the control of action versus perception have been shown to be dissociated (Goodale & Milner, 1992). 20 participants were divided into two groups (PDP vs. VRF) and reached in a virtual environment with an aligned cursor (1 block) and a cursor that was rotated 40° CW relative to hand motion (3 blocks). IA and EA were assessed immediately following each of the 4 blocks and following a 5-minute break in which participants sat quietly. The PDP group reached while using any learned strategy (IA+EA), or while not engaging in a strategy (IA). The VRF group verbally reported the number they planned to aim to (EA) before they reached to the target (IA+EA). Both groups adapted to the rotation and showed evidence of IA and EA. However, the groups differed with respect to the stability and extent of IA and EA observed over time. In the PDP group, IA decayed quickly (i.e., following the 5-minute break), while, IA was consistent across blocks and delay interval for the VRF group. The VRF group also had greater EA at all times. Given the different trends in performance between the groups, the PDP and VRF do not assess similar IA and EA.Acknowledgments: Supported by Natural Sciences and Engineering Research Council of Canada [EKC].
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".