Beep, aim, repeat: Movement repetition bias during sequential aiming movements
Bibliographic record
Abstract
Consecutive goal-directed actions are not executed completely independently from one another. Actions are influenced by both recent motor history, and the characteristics of future movements. Hence, during movement sequences, current movement can be biased toward the direction of a preceding target and/or an upcoming target through the activation of movement repetition mechanisms: use-dependent and advance preparation processes, respectively. Here, we sought to assess the contribution of use-dependent and advance-preparation processes during sequential actions. Participants performed two consecutive aiming movements in time with the last two tones of a sequence of five. First movement was made to a fixed target (450 relative to origin), followed by a movement to either 450 or 900 direction. Hence movement pairs were always 450-450 or 450-900. A movement bias would be revealed if there were larger movement angles on the first 450 movement in a 450-900 movement pair than on the first 450 movement in a 450-450 movement pair. Participants performed the pairs of movement in different blocks in which movement pairs (450-450 or 450-900) were blocked or alternated. In the blocked trials, participants were presented with a single target combination (450-450 or 450-900), whereas for the alternating trials, the target combination alternated between 450-900 and 450-450. Unexpectedly, the results revealed larger movement angles in the blocked 450-450 trials than blocked 450-900 trials and the alternating trials. These results were not consistent with predictions based on use-dependent and advance preparation mechanisms. These results will be discussed with regard to planning efficiency and movement repetition processes.
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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 | 0.011 |
| 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) | 0.002 | 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".