Using hand performance measures to predict handedness
Bibliographic record
Abstract
Handedness is defined by the individual's preference to use one hand predominately for unimanual tasks and the ability to perform these tasks more efficiently with one hand (Corey, Hurley, & Foundas, 2001). It is important to use performance variables to measure handedness because they are more objective than traditional hand preference questionnaires (Bryden, Pryde, & Roy, 2000a). The current study develops a predictive model of handedness as measured by the Waterloo Handedness Questionnaire (WHQ) using several performance indicators of handedness. A total of 120 individuals (60 right-handers and 60 left-handers) were asked to complete four performance-based tasks: the Grooved Pegboard (GP), the Annett pegboard (AP), finger tapping (FT), and grip strength (GS) as well as an observational measure of preference, the Wathand Box Test (WBT). Backward linear regression analysis showed that the Wathand Box measure and the laterality quotients for several performance measures (GP place, AP, and FT) combined to act as the most accurate predictors of hand preference. The predictive model of handedness developed is as follows: WHQ = -2.760- - 0.667(GP place) + 0.809(FT) + 0.234(WBT) - 0.748(AP) with an explained variance of 0.836. These results illustrate, as Corey et al. (2001) suggested, that the best predictive model of handedness combines preference measures and several performance measures that tap into different elements of motor performance. By developing this model, it is possible to get an accurate measure of handedness using objective measures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".