Preceding movement effects during discrete reciprocal manual aiming
Bibliographic record
Abstract
While most research in motor control has supported a highly online control account of human limb movement, recent research suggests that there are priming effects between movements, when the movements are: a) performed in close succession and b) are similar in nature. In the present study, the individual components of the index of difficulty were perturbed during a discrete reciprocal manual aiming task. Participants performed sequences of 20 aiming movements with the index finger to targets presented to the left and right of the midline. Target perturbations occurred between the 8th and the 12th movement, manipulating target size in the first experiment and the distance between the two targets in the second. The trials of interest were the trials that immediately followed the target perturbation, as we wanted to see how individuals would react to the change in the difficulty of the task. The results of the experiment revealed that for the target size perturbation, two trials were required for movement time to coincide with the new index of difficulty. While for the distance perturbation, movement times always coincided with the given distance between the two targets. These findings suggest that information gathered from these two components may lead to different movement control strategies used by the participant. Acknowledgments: Natural Sciences and Engineering Research Council of Canada (Binsted & Cheng)
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".