Degree versus direction: A comparison of four handedness classification schemes through the investigation of lateralised semantic priming
Bibliographic record
Abstract
Four classification systems were examined using lateralised semantic priming in order to investigate whether degree or direction of handedness better captures the pattern of lateralised semantic priming. A total of 85 participants completed a lateralised semantic priming task and three handedness questionnaires. The classification systems tested were: (1) the traditional right- vs left-handed (RHs vs LHs); (2) a four-factor model of strong and weak right- and left-handers (SRHs, WRHs, SLHs, WLHs); (3) strong- vs mixed-handed (SHs vs MHs); and (4) a three-factor model of consistent left- (CLHs), inconsistent left- (ILHs), and consistent right-handers (CRHs). Mixed-factorial ANOVAs demonstrated significant visual field (VF) by handedness interactions for all but the third model. Results show that LHs, SLHs, CLHs, and ILHs responded faster to LVF targets, whereas RHs, SRHs, and CRHs responded faster to RVF targets; no significant VF by handedness interaction was found between SHs and MHs. The three-factor model better captures handedness group divergence on lateralised semantic priming by incorporating the direction of handedness as well as the degree. These findings help explain some of the variance in language lateralisation, demonstrating that direction of handedness is as important as degree. The need for greater consideration of handedness subgroups in laterality research is highlighted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".