The influence of foreperiod duration on the preparation and control of sequential aiming movements
Bibliographic record
Abstract
Reaction time (RT) and movement times (MTs) to the first target are typically longer for two-target sequential movements compared to one-target movements. While this one-target advantage has been shown to be dependent on the availability of advance information about the numbers of targets, there has been no systematic investigation of how foreperiod duration (i.e., interval between presentation of the target(s) and stimulus) influences the planning and execution of sequential movements. Two experiments were performed to examine how the one-target advantage is influenced by the availability and timing of advance target information. In Experiment 1, participants performed one- and two-target movements in two separate blocks. In Experiment 2, target conditions were randomised from trial to trial. The interval between target(s) appearing and stimulus tone (i.e., foreperiod) was varied randomly (0, 500, 1,000, 1,500, and 2,000 ms). The results of Experiment 1 revealed that while the one-target advantage in RT was not influenced by foreperiod duration, the one-target advantage in MT increased as foreperiod duration increased. The variability of endpoints at the first target was greater in the two- compared to one-target condition. In Experiment 2, the one-target advantage in both RT and MT increased as the length of the foreperiod increased. However, there was no difference in limb trajectory variability between target conditions. The implication of these findings for theories of motor planning and execution of multiple segment movements is discussed.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".