Exploring methodologies : the effect of explicit aim reporting on implicit adaptation
Bibliographic record
Abstract
Humans adjust their movements to maintain accuracy in changing environments, relying on both explicit strategies and implicit adaptation. Understanding how these processes interact across contexts is critical in interpreting sensorimotor adaptation behaviour and for designing experiments and interventions that effectively target specific learning mechanisms. The present study uses a visuomotor rotation task to investigate how explicit processes – specifically providing a verbal report of one’s intended aim direction, influence implicit adaptation and its generalization. Recent adaptation protocols attempt to break down explicit and implicit contributions to adaptation, often by using verbal aim reports on a number circle to estimate the explicit adaptation. Studies using this explicit report protocol have shown that implicit adaptation across movement directions is largest around a participant’s intended aim direction, rather than the target direction. This implies that aim reporting itself may alter the generalization of implicit adaptation. Aim reporting could shift focus toward the aim point and, as a result, alter implicit adaptation and the generalization across directions. The current study compared the generalization of implicit adaptation for participants who reported their aim with those who did not. Total adaptation was assessed during perturbation trials where participants were instructed to move a rotated visual cursor to a target. Implicit adaptation and its generalization were isolated on probe trials to untrained target directions where participants were instructed to move their unseen hand to the target. We found that verbal aim reporting significantly affected implicit sensorimotor adaptation. Participants who reported their aim displayed a shift in the peak of implicit adaptation away from the target and toward the reported aiming direction, along with a decrease in the magnitude of adaptation compared to the control group. These findings challenge the assumption that implicit adaptation is rigidly centered on the target or aiming location and highlight that commonly used measurement techniques like aim reporting can meaningfully affect the implicit adaptation being quantified. These findings emphasize the need to carefully consider methodology, instruction, and participant strategies, as they can meaningfully influence the expression of implicit adaptation.
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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 | 0.044 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".