Perceptual and motor tasks assess different aspects of awareness following visuomotor adaptation
Bibliographic record
Abstract
Awareness has been shown to play a role in visuomotor adaptation, such that participants who are aware of the visual distortion or changes in their reaches tend to adapt their movements quicker, and engage in more explicit (i.e., strategic) processing (Neville and Cressman, 2018). To date, awareness has been established based on both perceptual reports at the end of the experiment and motor tasks, in which participants are instructed to reach while using any learned reaching strategies. Here we asked if these perceptual and motor tasks assess similar aspects of awareness of the visuomotor distortion. Participants were divided into 2 Rotation groups and adapted their reaches to a small (30degree) or large (40degree) visuomotor rotation. Awareness was assessed in both groups following adaptation using perceptual reports (verbally indicate if a visuomotor distortion was present and the size of the distortion) and a motor task (reach to the target using any learned strategies in the absence of visual feedback). In both Rotation groups, changes in reaches observed in the motor tasks was significantly less than the size of the visuomotor distortion verbally reported. Furthermore, while participants in the small rotation group were able to verbally report awareness of the visuomotor distortion, their reaches did not reflect this awareness (i.e., participants did not engage in any strategic reaching). Thus, perceptual and motor tasks assess different aspects of awareness, and future work should consider methods of assessment when interpreting the influence of awareness on visuomotor adaptation.Acknowledgments: Acknowledgements: 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".