Laterality Across the Lifespan: The Effects of Task Complexity
Bibliographic record
Abstract
The current study aimed to build on the work of Gooderham & Bryden (2013) by comparing the effects of task complexity on hand selection in children. It was hypothesized that the increasing complexity of a task would override a biomechanically efficient movement such that participants would use only the preferred hand to complete the task, and that this tendency to switch hands would increase with age. Right-handed children aged 3-to12 years completed the newly designed Hand Selection Complexity Task (HSCT) which is an observational method designed to investigate the effects of task difficulty on hand selection in terms of Fitts’ Law. The measures allows for the proximity of reaches to remain the same, while manipulating task difficulty. Participants also completed the Long Pegboard (Bryden, et al., 1994), which measured the behavioural preference of hand selection. Finally, participants completed the Waterloo Handedness Questionnaire, to determine hand preference. Results indicated that for the HSCT the time to complete the movement increased for ID levels 4-6, which also include a greater number of errors. In addition, children tended to switch to their preferred (right) hand and with practice were able to complete the task faster. With respect to the Long Pegboard, when the task was performed with their left hand, they tended to switch to using their preferred (right) hand to complete the task. No switches were recorded when beginning the task with the right hand. The results of this study will be discussed in light current theories of hand selection.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".