A similar time course for motor preparation in both direct and indirectly cued movements
Bibliographic record
Abstract
Research has shown that reaction time (RT) is longer for indirect vs. directly cued movements (Maraj & Heath 2010), which may be due to differences in the time course of motor preparation. Recent results from our lab using an anticipation-timing task revealed that the RT increases may be more attributable to non-motoric transformations than to the time required for motor activation. The present study examined whether a variable foreperiod RT task (1650-2350 ms), which has been found to lead to early motor preparation, would reveal differences in the timing of motor preparation between direct and indirect movements. In a simple RT task, subjects reacted to a go-signal by making a forearm rotation directly to a visual cue or to an instructed location that deviated by 60°, 90°, or 120° with respect to a visual cue. A startling acoustic stimulus (SAS) was randomly presented 500, 1000, or 1500 ms following visual cue onset. Results revealed that subjects were reliably startled (81%) at all time points, and movements were elicited early more often (p < 0.05) when the SAS was delivered at 1500 ms (40%) compared to1000 (21%) or 500 ms (29%) following the visual cue. Importantly, a similar rate of response triggering was found regardless of angular rotation required. These results indicate that the time course of motor preparation was similar for all movements, suggesting that differences in RT between direct and indirectly cued movements arises due to non-motoric processes.Acknowledgments: This work was supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC), awarded to Anthony N. Carlsen and Erin K. Cressman.
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".