The influence of a weighted perturbation on a bimanual coordination task
Bibliographic record
Abstract
Human bimanual coordination is a process explained through analysis of limb movements. Typical bimanual coordination paradigms include finger tapping (e.g., Studenka et al., 2012) or circle drawing (e.g., Studenka & Zelaznik, 2011). We were interested in a variant of finger tapping and how an introduction of a physical perturbation (weighted object) would affect coordination. We hypothesized that the perturbation would distort kinematic aspects of the movement but would not decouple overall performance due to the simplicity of the task. Additionally, we hypothesized that motor adaptation to the perturbation in one hand first, would transfer to the other hand during the second iteration of the perturbation. Twenty right-handed participants (M=10, F=10) performed shoulder flexion/extension movements to produce an in-phase coupling. Participants were asked to move 15cm and to match a 1Hz metronome for 5-second trials. Participants were randomly assigned to one of two groups: either experiencing the weight the left hand first or in the right hand first. Each group performed four blocks: 1) pre-test; 2) weight in first hand; 3) washout; 4) weight in second hand. The difference in performance between peak velocities of the hands was compared. Results showed that there was a smaller difference between peak velocities of the arms when the weight was in the left hand, and larger when the weight was in the right hand. Two different hypotheses have emerged: 1) perturbing the right hand is detrimental to coupling between right and left hand; 2) There was transference of performance from left-to-right hands, but not from right-to-left hands. Thus future research could benefit from further expanding our findings and discovering the underlying source of the performance decrement, with the potential to apply these findings to assessment of special populations. For example current pilot testing has shown amplified effects in a participant with post-concussion syndrome.
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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".