Interference during the preparation of bimanual movements: The role of asymmetric starting locations, movement amplitudes, and target locations
Bibliographic record
Abstract
Asymmetric, target-directed, bimanual movements take longer to prepare than symmetric movements (Diedrichsen et al. 2006; Heuer and Klein 2006 Psychol Res). Symmetric movements have the same starting locations, movement amplitudes, and target locations. Asymmetric movements typically have the same starting locations with different amplitudes and target locations. The preparation cost for asymmetric movements may, therefore, be related to the specification of different amplitudes, target locations, or both. Two studies have investigated the effects of these parameters, but their protocols were confounded by the number of movement choices (Heuer and Klein 2006 J Mot Behav) or two different symbolic cues (Weigelt 2007). The goal of this study was to determine which parameters contribute to interference during the preparation of bimanual movements. Thirty participants performed bimanual reaching movements that varied in terms of the symmetry/asymmetry of starting locations, amplitudes, and target locations. Reaction time costs were examined by comparing movements that had one asymmetric parameter to movements with all symmetric parameters. We observed significant reaction time costs (~13ms) for movements with asymmetric amplitudes, and no significant costs for movements with symmetric amplitudes. These effects were independent of the symmetry/asymmetry of the starting and target locations. Reaction time savings were examined by comparing movements that had one symmetric parameter to movements with all asymmetric parameters. We observed significant savings (~9ms) for all movements with one symmetric parameter. Taken together, these results suggest that any one symmetric parameter is sufficient to reduce interference during preparation, and asymmetric amplitudes may result in the largest interference.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".