Motor preparation of spatially and temporally defined movements: evidence from startle
Bibliographic record
Abstract
Previous research has shown that the preparation of a spatially targeted movement performed at maximal speed is different from that of a temporally constrained movement (Gottlieb et al. 1989b). In the current study, we directly examined preparation differences in temporally vs. spatially defined movements through the use of a startling stimulus and manipulation of the task goals. Participants performed arm extension movements to one of three spatial targets (20°, 40°, 60°) and an arm extension movement of 20° at three movement speeds (slow, moderate, fast). All movements were performed in a blocked, simple reaction time paradigm, with trials involving a startling stimulus (124 dB) interspersed randomly with control trials. As predicted, spatial movements were modulated by agonist duration and timed movements were modulated by agonist rise time. The startling stimulus triggered all movements at short latencies with a compression of the kinematic and electromyogram (EMG) profile such that they were performed faster than control trials. However, temporally constrained movements showed a differential effect of movement compression on startle trials such that the slowest movement showed the greatest temporal compression. The startling stimulus also decreased the relative timing between EMG bursts more for the 20° movement when it was defined by a temporal rather than spatial goal, which we attributed to the disruption of an internal timekeeper for the timed movements. These results confirm that temporally defined movements were prepared in a different manner from spatially defined movements and provide new information pertaining to these preparation differences.
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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 | 0.001 |
| 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".