Sequential Aiming with Two Limbs and the One-Target Advantage
Bibliographic record
Abstract
Movement times to the first target in a 2-target sequence are typically slower than in 1-target aiming tasks. The 1-target movement time advantage has been shown to emerge regardless of hand preference, the hand used, the amount of practice, and the availability of visual feedback. The authors tested central and peripheral explanations of the 1-target advantage, as postulated by the movement integration hypothesis, by asking participants to perform single-target movements, 2-target movements with 1 limb, and 2-target movements in which they switched limbs at the first target. Reaction time and movement time data showed a 1-target advantage that was similar for both 1- and 2-limb sequential aiming movements. This outcome demonstrates that the processes underlying the increase in movement time to the 1st target in 2-target sequences are not specific to the limb, suggesting that the 1-target advantage originates at a central rather than a peripheral level.
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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 | 0.006 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".