Accuracy instructions modulate both visual and non-visual contributions to ongoing reaches
Bibliographic record
Abstract
The control of ongoing goal-directed reaches is influenced by both visual and non-visual sensorimotor processes. Further, intentions to produce accurate movements influence reaching performance as well. However, it is not known how these improvements associated with accuracy-based intentions can be attributed to changes in movement planning and/or online control. Indeed, such improvements may influence both visual and non-visual online control processes. Using frequency domain analyses, the relative online contributions of such visual and non-visual sub-processes have been previously identified (e.g., de Grosbois & Tremblay, 2015; de Grosbois & Tremblay, 2016b; de Grosbois & Tremblay, 2017). The current study tested if the relative contributions of these online control sub-processes are influenced by the intention to be accurate. Reaching movements were completed in the presence of three experimental manipulations. First, vision during voluntary reaches was either provided or occluded. Second, high- and low-accuracy instruction sets were provided. And third, the predictability of visual information was manipulated through a blocked and randomized feedback scheduling. The results indicated that the contribution of online visuomotor processes (i.e., visual sub-process) was increased by the availability of online vision and the instructed intention to be accurate. In contrast, the non-visual sub-process was promoted in the absence of online vision, but suppressed when a randomized feedback schedule was implemented with instructions to be accurate. Ultimately, the intention to be accurate increases the relative contribution of vision-based online sensorimotor processes and can decrease that of non-visual online sensorimotor processes.Acknowledgments: This research was supported by the Natural Sciences and Engineering Research Council of Canada, the Canada Foundation for Innovation, the Ontario Research Fund, and a University of Toronto Graduate Student Fellowship.
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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 | 0.005 |
| 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 | 0.001 | 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".