The influence of trial sequence on the preparation and control of sequential aiming movements
Bibliographic record
Abstract
When moving from a starting position to a single target, movement time is faster than when you must continue the movement to a second target (i.e., one-target advantage). Research has now shown that processes underlying both the movement integration and constraint hypotheses account for the preparation and control of sequential aiming movements. In the present study, we investigated how these processes are influenced by the sequencing of single and two target movements and prior knowledge of the number of targets. Participants performed aiming movements to one or two targets on a horizontally positioned touch screen. Three different trial sequences were administered. All movements were performed as an extension from the center of the participant outward. One and two target responses were organised as blocked (i.e., 1-1-1-2-2-2), alternating (i.e., 1-2-1-2-1-2), and random (i.e., 1,1,2,1,2,2) trial sequences. The one-target advantage emerged during the blocked and alternate conditions but not the random condition. This finding suggests that the emergence of the one-target advantage is contingent on prior knowledge of the number of targets. This finding complements previous research that has shown that reaction time increases as a function of the number of movement segments only when the number of segments is known in advance of stimulus onset. Results are discussed as they pertain to the movement constraint and movement integration hypotheses. Keywords: one-target advantage, reaction time, movement constraint hypothesis, movement integration hypothesis
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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 | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".