Use of a naturalistic reaching task to measure hand preference in children
Bibliographic record
Abstract
Handedness can be assessed using performance-based observational measures such as the Preferential Reaching paradigm that requires participants to reach and pick up an object in a specific location in working space. Our previous research (Bryden & Roy, 2006) has shown that as children get older they rely increasingly on their preferred hand, regardless of the position in working space, and the difficulty of the task. In an attempt to develop a naturalistic reaching task where children would be unaware that hand preference was being assessed, we modified the LEGO© task developed by Gonzales & Goodale (2009). Children ages 3 to 8 were asked reproduce a 3D model, constructed from either LEGO©, DUPLO© or MegaBlox© pieces, using pieces readily available across working space. Hand preference, using other traditional measures (WHQ and WHCT) task also obtained. Preliminary analysis showed that the preferred hand was used more often with increasing age. Looking specifically at hand use when reaching to each space, all age groups used their preferred hand more than the non-preferred hand when reaching to the midline and the ipsilateral space. Children aged 3-6 used the non-preferred hand more than the preferred hand when reaching to the contralateral space. Interestingly, the 7-8 year old group avoided both using their non-preferred hand and reaching into contralateral space. Such findings using a naturalistic reaching task confirm and extend the findings of Bryden & Roy (2006). Acknowledgments: Funding from NSERC Discovery grant
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".