High intra-task and low inter-task correlations of motor skills in humans creates an individualized behavioural pattern
Bibliographic record
Abstract
Our motor system allows us to generate an enormous breadth of voluntary actions, but it remains unclear whether and how much motor skill translates across tasks. For example, if an individual is good at gross motor control, are they also good at fine motor control? Previous research about the generalization across motor skills has been equivocal. Here, we compare human performance across five different motor skills. High correlation between task measures would suggest a certain level of underlying sensorimotor ability that dictates performance across all task types. Low correlation would suggest specificity in abilities across tasks. Performance on a reaching task, an object-hitting task, a bimanual coordination task, a rapid motion task and a target tracking task, was examined twice in a cohort of 25 healthy individuals. Across the cohort, we found relatively high correlations for different spatial and temporal parameters within a given task (16-53% of possible parameter pairs were significantly correlated, with significant r values ranging from 0.53 to 0.97) but relatively low correlations across different tasks (2.7-4.4% of possible parameter pairs were significantly correlated, with significant r values ranging from 0.53-0.71). We performed a cluster analysis across all individuals using 76 performance measures across all tasks for the two repeat testing sessions and demonstrated that repeat tests were commonly grouped together (16 of 25 pairs were grouped next to each other). These results highlight that individuals have different abilities across motor tasks, and that these patterns are consistent across time points.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".