Prior knowledge of stylus mass and the online regulation of goal-directed movement
Bibliographic record
Abstract
Prior knowledge about the sensory information available on an upcoming aiming trial allows performers to execute a movement that strategically utilizes this information. When prior knowledge is not provided and performers are unaware of the sensory environment they will be faced with, they typically prepare for the worst-case scenario (e.g., Hansen et al., 2006). The purpose of this study was to examine whether prior knowledge about the mass of a stylus impacted the execution of a goal-directed movement, so that the consequences of spatial variability (i.e., target relative location of primary submovement end points) could be minimized. In this case, planning for the worst-case scenario is associated with movements made with a heavier stylus, since they generally involve greater initial force requirements and higher spatial variability. Three groups of participants performed goal-directed aiming movements with a light (36g) and heavy (243g) stylus. Two groups were provided with prior knowledge about the stylus to be used on the upcoming trial and followed either a random (PK) or blocked (BL) protocol; another group was yoked (Y) to the PK group and was not provided prior knowledge. Movements with the heavy stylus were spatially more variable at the peaks of velocity and deceleration, and demonstrated greater undershoot biases with the primary submovements. However, there were no differences between groups in these measures. Therefore, independent of prior knowledge, kinematic differences in movements with the styli were suggested to be rectified online by a process of visual regulation that guided the limb onto the target.Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC)
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".