Which hand knows the “right” word? What hand selection reveals about vocabulary in pre‐and school‐aged children
Bibliographic record
Abstract
Research has shown that infants with increased right-hand selection for their first gestures perform better at an array of language tasks when they are tested later as toddlers. There is a smaller body of literature which focuses on preschoolers and how their right-handed movements relate to their speech and vocabulary development. Some research has established a connection between right-hand preference for grasping and speech production ability in preschool children, but the link to gestures is relatively unexplored in this age group. We investigated if lateralized gestures (pointing) are related to measures of language development (vocabulary) in a preschool-age sample. Specifically, typically developing children (aged 3-6) completed the Peabody Picture Vocabulary Test (PPVT) to assess receptive language. We recorded their hand preference for pointing during the PPVT and the incidence of mistakes (pointing to the wrong picture). Despite the length of the test, children were more likely to select a correct response with their right hand. This result suggests a relationship between lateralized communicative gestures (pointing) and receptive language. This study provides evidence for an intimate relationship between right-handed manual movement and language development. Implications of this finding include developing simple fine-motor tasks to detect and/or ameliorate delayed language development.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".