Footedness and spatial ability: Does a relationship exist?
Bibliographic record
Abstract
Research in the area of limb preference and brain laterality has focused on handedness, rather than lower limb preference, despite the fact that footedness has been shown to be a better predictor of language lateralization (Elias & Bryden, 1998). While the findings are inconsistent, some work has found that increased spatial ability is seen in those who are ambidextrous (Burnett, et al., 1982). How footedness may relate to spatial ability is not known. In the current experiment, 38 individuals (aged 18 to 23 years) completed six spatial tests, the Waterloo Handedness Questionnaire (WHQ), and the Waterloo Footedness Questionnaire (WFQ). No relationship between spatial performance and handedness was found. With respect to footedness, some relationships were found. Overall, it was found that scores on the WFQ were significantly correlated with only performance on the Hidden Figures task. When the WHQ was used to divide individuals into footedness groups, a gender by footedness AVOVA revealed significant effects of footedness on both the Card Rotation task and the Maze Tracing task. Here, left-footedness was associated with worse performance on both of these tasks. Interestingly, dividing the sample into strong versus weak footedness showed that weak footedness was associated with worse performance on all spatial tasks. The results provide moderate support the notion that footedness may be a better predictor of the lateralization of spatial abilities compared to handedness.Acknowledgments: NSERC(PJB)
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".