Investigating information processing of the bimanual asymmetric cost with the response priming technique.
Bibliographic record
Abstract
Constraining the degrees of freedom simplifies the coordinative challenge of bimanual asymmetric movements. This, however, comes at the cost of increased processing demands during movement preparation, referred to as the bimanual asymmetric cost. The goal of the present study was to further investigate information processing of the bimanual asymmetric cost with the response priming technique. This technique involved precuing a movement to encourage it to be preprogrammed. A different movement is occasionally cued by the go signal, which required the preprogrammed movement to be reprogrammed. In Experiment 1, 2 preprogrammed unimanual movements were reprogrammed, or integrated, into a bimanual movement. In Experiment 2, a preprogrammed bimanual movement was reprogrammed, or de-integrated, into a unimanual movement. Both experiments revealed 2 costs when integrating or de-integrating bimanual movements. One cost was likely related to aborting 1 movement and preparing another, which is the typical reprogramming cost found in response priming experiments. The second cost was likely related to constraining the degrees of freedom of bimanual asymmetric movements, which is a bimanual asymmetric cost. Integrating 2 unimanual movements into a bimanual asymmetric movement involves constraining the degrees of freedom, and de-integrating a bimanual asymmetric movement into a unimanual movement involves unconstraining the degrees of freedom. Both reprogramming and bimanual asymmetric costs occurred in 1 of the experimental conditions, and the interesting finding was that their effects were additive. Additive costs suggest that each cost affects a different stage of movement preparation. We suggest that the bimanual asymmetric cost occurs during response selection. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".