Examining the roles of regularity and lexical class in 18--26-month-olds' representations of how words sound
Bibliographic record
Abstract
By around 12 months, infants have well-specified phonetic representations for the nouns they understand, for instance looking less at a car upon hearing ‘cur’ than ‘car’ (Swingley & Aslin, 2002). Here we test whether such high-fidelity representations extend to irregular nouns, and regular and irregular verbs. A corpus analysis confirms the intuition that irregular verbs are far more common than irregular nouns in speech to young children. Two eyetracking experiments then test whether toddlers are sensitive to mispronunciation inregular and irregular nouns (Experiment 1) and verbs (Experiment 2). For nouns, we find both a mispronunciation and regularity effect in 18-month-olds. For verbs, in Experiment 2a, we find only a regularity effect and no mispronunciation effect in 18-month-olds, though toddlers’ poor comprehension overall limits interpretation. Finally, in Experiment 2b we find a mispronunciation effect and no regularity effect in 26-month-olds. Implications for wordform representations, lexical class, and learning are discussed.
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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 | 0.002 |
| 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) | 0.002 | 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".