Can an observational method of assessing hand preference be used to predict language lateralisation?
Bibliographic record
Abstract
The current study investigated (a) whether or not the WatHand Cabinet Test (WHCT, Bryden, Roy, & Spence, 2007) could be used as accurately as the Waterloo Handedness Questionnaire (WHQ) to classify individuals into language lateralisation groups based on their hand preference, and (b) the relationship between direction and degree of hand preference and language lateralisation. A total of 142 participants (82 right-handers and 60 left-handers) completed the WHQ and the WHCT, and performed a fused-words dichotic listening test. Findings indicated that the WHCT was robust alternative to the WHQ in providing a measure of hand preference as there was a high correlation between the WHCT and the WHQ, and individuals were divided into similar language lateralisation groups when using either the WHCT or the WHQ as the classifying variable. More specifically, there existed a predictable pattern of language lateralisation into which members of different handedness groups fell. The same pattern exists whether handedness is defined using subjective questionnaires or more objective observational measures of hand preference.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".