Task demands affect manual asymmetries in pegboard performance
Bibliographic record
Abstract
Previous work by Bryden and Roy (1999) showed a larger performance difference between the hands for placing pegs into holes on the Grooved Pegboard test than for removing pegs from the holes. The authors argued that these data provided evidence of the importance of task demands in manual asymmetries. However the study failed to control for the differing starting positions of the pegs. To clarify this possible confound, the purpose of the current investigation was to determine the influence of starting position on the between-hand performance differences on the Grooved Pegboard Test. To do so, both the start and end positions were manipulated, such that participants moved the pegs from the receptacle to another receptacle or to a set of holes, or participants moved the pegs from a set of holes to a receptacle or another set of holes. A total of 30 right-handed individuals (as classified using the Waterloo Handedness Questionnaire) participated in the experiment, completing five trials with each hand for each of the four conditions. While no significant effects of start position were found, a significant interaction between hand and end position, F(1.29) = 30.85, P<.001, was found for the time to complete the task, where larger differences between the hands, fovouring the right hand, were seen for placing pegs into the holes as opposed to the receptacles. This effect was also found when the data were expressed using a laterality quotient. The results are discussed in terms of the influence of task complexity on manual asymmetries.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".