Default motor preparation under conditions of response uncertainty
Bibliographic record
Abstract
In a choice reaction time (RT) paradigm, providing partial advance information (a precue) about the upcoming response has been shown to decrease RT, presumably due to preprogramming of the precued parameters. If arm and direction are precued, but amplitude is rendered uncertain, a number of different preparation strategies have been suggested to occur during the foreperiod. Initial preprogramming of a default movement lying between response alternatives has been suggested in studies whereby response preparation time was manipulated (Favilla et al. 1989; Favilla et al. 1990; Ghez et al 1997). However in a RT paradigm, this strategy was discounted due to the absence of online adjustments to movement, therefore it appeared movements were not initiated until after all parameters had been correctly specified and programmed by the nervous system (Bock and Arnold 1992). The present study reinvestigated the validity of default motor preparation as a strategy in a choice RT paradigm, employing the triggering effects of a startling acoustic stimulus. On control trials (80 dB imperative stimulus), the movements were performed to the correct targets. Presenting a startle stimulus (124 dB) resulted in the early trigger of a default movement whose amplitude fell in between the potential response alternatives. Thus, the present study found behavioural evidence of default movement preparation as a strategy under conditions of uncertainty regarding response amplitude. Acknowledgments: This study was supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) awarded to I.M.F. and an NSERC Undergraduate Student Research Award (USRA) awarded to C.J.F.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".