Learning to contend with accents in infancy: Benefits of brief speaker exposure.
Bibliographic record
Abstract
Although adults rapidly adjust to accented speakers' pronunciation of words, young children appear to struggle when confronted with unfamiliar variants of their native language (e.g., American English-learning 15-month-olds cannot recognize familiar words spoken in Jamaican English; Best et al., 2009). It is currently unclear, however, why this is the case, or how infants overcome this apparent inability. Here, we begin to address these crucial questions. Experiments 1 and 2 confirm with a new population that infants are initially unable to recognize familiar words produced in unfamiliar accents. That is, Canadian English-learning infants cannot recognize familiar words spoken in Australian English until they near their second birthday. However, Experiments 3 and 4 show that this early inability to recognize accented words can readily be overcome when infants are exposed to a story read in the unfamiliar accent prior to test. Importantly, this adaptation only occurs when the story is highly familiar, consistent with the idea that top-down lexical feedback may enable the adaptation process. We conclude that infants, like adults, have the cognitive capacity to rapidly deduce the mapping between their own and an unfamiliar variant of their native language. Thus, the essential machinery underlying spoken language communication is in place much earlier than previous studies have suggested.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".