The influence of awareness on explicit and implicit contributions to visuomotor adaptation
Bibliographic record
Abstract
Explicit (strategic) and implicit (unconscious) processes play a role in visuomotor adaptation (Bond & Taylor 2015; Werner et al. 2015). We investigated the contributions of explicit and implicit processes to visuomotor adaptation when awareness was manipulated directly versus indirectly, and ask how these contributions change over time. Participants were assigned to a Strategy or No-Strategy group. Those in the Strategy group were made aware of the visuomotor distortion directly. Participants were further subdivided into groups to train with a large (60°), medium (40°) or small (20°) visuomotor distortion, providing the potential for awareness to develop indirectly. Participants reached with their respective distorted cursor, followed by a series of no-cursor reaches to assess the contributions of explicit and implicit processes to visuomotor adaptation after every 30 reach training trials. Within the no-cursor reaching trials, participants reached (i) with any strategies they had gained during training (explicit + implicit processes), and (ii) as accurately to the target as possible (implicit processes). Results showed that implicit contributions to visuomotor adaptation were greatest in the No-Strategy group and took time to develop. Explicit processes were greatest in the Strategy group, increased with rotation size in the No-Strategy group, and remained consistent over time. Taken together, results reveal that there are notable differences in explicit and implicit contributions to visuomotor distortions depending on if, and how participants become aware of the perturbation. Moreover, the results highlight the importance of instructions when evaluating reaching performance in no-cursor trials, as they can modulate reaching errors.Acknowledgments: This work was supported by a Discovery Grand provided by the Natural Sciences and Engineering Research Council of Canada (E. K. Cressman)
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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 | 0.007 |
| 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 | 0.000 |
| 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 | 0.000 |
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".