Social Psychology in the Task Organization of Dyadic 90° Rhythmic Coordination: The Coupling Is Not What You Might Expect
Bibliographic record
Abstract
Herth, Zhu and Bingham (2021) investigated frequency scaling of a learned 90° bimanual rhythmic coordination in the context of both noninterference and correcting instructions. They found that performance of 90° coordination remained stable at high frequencies with correcting, but that performance deteriorated as frequency increased without correcting. The participants trained during learning both at performing bimanual and unimanual coordination and in both cases, they trained to the same criterion level of performance. Herth, Zhu and Bingham (submitted) then tested these same participants with frequency scaling of unimanual 90° coordination. They found in this case that the stability of performance was lost in the same way both with and without correcting. The question raised by this difference in results was whether the inability to maintain stable performance of unimanual coordination with correcting was due to the lack of kinesthetic/neural coupling or to the lack of bidirectional coupling present in bimanual coordination. To separate the two differences in coupling, frequency scaling of learned 90° coordination between two people (dyads) was tested in the current study because this was expected to isolate bidirectional coupling without the kinesthetic/neural coupling. Dyadic coordination is strictly visual. However, the results were identical to those found with unimanual coordination. The ability to correct did not yield stable performance at high frequency. An analysis was performed that showed that the coupling in the dyadic coordination was unidirectional contrary to the common expectation. Dyads were not allowed to communicate directly during learning and testing sessions, but they established the role of the coordinator and corrector vs who would just be steady state in organizing the task performance, nevertheless. Some dyads never achieved criterion performance even after over a dozen training sessions. Perchance they failed to achieve the requisite task organization. The results yielded an interesting problem in social psychology.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".