Decreasing the preparation cost for bimanual asymmetric movements
Bibliographic record
Abstract
Movement preparation of bimanual asymmetric movements is longer than bimanual symmetric movements in choice reaction-time conditions, even when movements are directly cued (Blinch et al., 2014). This bimanual asymmetric cost may be caused by greater processing demands on response programming, but this requires further investigation. The present experiment tested the demands on response programming for bimanual movements by temporally separating the preparation of each arm. This was achieved by precuing the target of one arm before the imperative stimulus. We asked: What preparation occurred in advance for the precued arm? The answer to this question would suggest which process causes the bimanual asymmetric cost. Advance movement preparation was examined by comparing reaction times with and without a precue for the left target and by using the start-react effect. Participants made bimanual symmetric and asymmetric reaching movements in simple and 2-choice reaction-time conditions and a condition with a precue for the left target. We found a bimanual asymmetric cost in 2-choice conditions, and the asymmetric cost was significantly smaller when the left target was precued. This result suggests: 1) that the precued movement was not fully programmed but partially programmed before the imperative stimulus, and 2) that the asymmetric cost was caused by greater processing demands on response programming. The results of the startle trials also supported these two conclusions. Overall, the results support the notion that bimanual movements are not the sum of two unimanual movements; instead, the two arms of a bimanual movement are unified into a functional unit. When one target is precued, this critical unification likely occurs during response programming. Acknowledgments: We would like to thank Brendan D Cameron, Dana Maslovat, and Chris J Forgaard for their helpful comments and suggestions, Keith R Lohse for helping to calculate the within-participant confidence intervals, and JA Holmes for processing the deltoid EMG. The Natural Sciences and Engineering Research Council of Canada supported this research with a discovery grant awarded to Romeo Chua.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".