Bibliographic record
Abstract
Using more than one limb to perform functional, goal-directed actions is arguably one of the most important abilities that human beings possess. In many everyday tasks, the hands, in particular, must be used to accomplish all manner of goals. From buttoning a shirt to opening a jam jar and driving to work, good bimanual coordination is of great utility. In addition to the tasks mentioned above, there are also other tasks involving the functional use of more than one limb, including walking or cycling and typing a report. With a little thought, it becomes apparent that there is at least one important difference between these categories of coordination tasks. On one hand, in some tasks the effectors must perform markedly different motor outputs that are bound together in some functionally defined and usually object-oriented manner (e.g., buttoning a shirt) yet, in others, the effectors produce very similar motor outputs but in a specific temporal order, which may or may not repeat itself periodically (e.g., walking and cycling compared to typing or drumming). In this short article, I will argue that the second category of coordination task and, in particular, cyclical coordination, has been studied extensively and, at least at the level of behavior, is relatively well understood. In contrast the former category of bimanual task is seldom studied and, even at the descriptive level, is rather poorly understood. One of the reasons for this may be the complexity of such tasks and the technical difficulties involved in attempting to study them. By highlighting some key studies, I hope to illustrate that such tasks can be fruitfully studied in the laboratory. Last, since the neural control processes underlying both classes of coordination task are not yet well known, I aim to draw attention to the potential value of the interventional technique of Transcranial Magnetic Stimulation (TMS) as a tool for investigating the functions of brain regions contributing to bimanual coordination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".