Independent planning of timing and sequencing for complex movements
Bibliographic record
Abstract
The current studies examined the processes involved in response sequencing and timing initiation for complex, multiple-element movements. Participants performed three element key-press movements in simple and choice reaction time (RT) paradigms (Experiment 1), or a study time paradigm that allowed the participants to control the foreperiod delay, which is thought to reflect advance preparation duration (Experiment 2). Sequencing requirements were manipulated by using either one hand (low sequencing complexity) or two hands (high sequencing complexity) and timing was manipulated by using either an isochronous (low timing complexity) or non-isochronous (high timing complexity) pattern. Increasing sequencing complexity had little effect on simple RT but increased participant-controlled foreperiod delay (i.e., study time). Conversely, increasing timing complexity had no effect on foreperiod delay but increased simple RT. These results provide compelling evidence that in a simple RT paradigm, sequencing preparation is performed during the foreperiod while timing preparation is delayed until the RT interval. Furthermore, choice RT increased with sequencing complexity and was relatively unaffected by timing complexity, indicative of sequencing preparation occurring during the choice RT interval and timing preparation occurring on-line. Collectively, the data indicate a dissociation and independence of the preparation of response timing and sequencing for complex movements, which is discussed in relation to the potential neural structures involved. Acknowledgments: Supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".