How many fingers am I holding up? The answer depends on children's language background
Bibliographic record
Abstract
Monolingual English-speaking preschool children tend to process number gestures as unanalyzed wholes rather than use the one-to-one (finger-to-quantity) correspondence. By school age, however, children can use the one-to-one correspondence. The purpose of the present studies was to test whether children learn one-to-one correspondence through exposure to a variety of finger configurations to convey a single quantity. In Study 1, we compared children with exposure to multiple one-to-one configurations, that is, French-English and German-English bilingual children, to English monolingual children who see consistent representations. As predicted, the bilingual children performed better in interpreting unconventional number gestures. In Study 2, we compared Chinese-English bilingual children who knew arbitrary one-handed Chinese numbers gestures for quantities 6-10 to Chinese-English bilingual children who did not know these gestures, as well as to monolingual English speakers. Chinese-English bilinguals who knew the arbitrary gestures were more likely to interpret unconventional gestures arbitrarily (i.e., influenced by the written and/or Chinese gesture forms). These children did not differ from English monolinguals in the interpretation of unconventional gestures. These results are consistent with the argument that children can become sensitive to the one-to-one correspondence in number gestures with exposure to multiple configurations for the same quantity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".