Automaticity of online control processes in manual aiming
Bibliographic record
Abstract
Experiments that manipulated the visual feedback of the moving limb have suggested the existence of efficient and automatic online correction processes. We wanted to determine whether the latency/gain of the correction for a cursor jump are only influenced by the size of the cursor jump or whether they are also influenced by the need of a correction for the target to be reached. In Experiment 1, we used two target sizes (5 and 30 mm) and three cursor-jump amplitudes (5, 15, and 25 mm), so that for some target size/cursor-jump combinations, no correction would be needed to reach the target. Participants were not aware of the cursor jump, but we observed a 65% correction regardless of target size. In Experiment 2, participants pointed at a large target for which a 15-mm cursor jump never impeded target attainment. Participants modified the trajectory of their movement in the direction opposite to the cursor jump (42% of the cursor jump). Our results indicate that the latency of the correction for a cursor jump was not influenced by the size of the cursor jump or that of the target. However, the correction tailored the movement's initial impulse according to the target's characteristics.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".