Toddlers Process Common and Infrequent Childhood Mispronunciations Differently for Child and Adult Speakers
Bibliographic record
Abstract
Purpose This study examined toddlers' processing of mispronunciations based on their frequency of occurrence in child speech and the speaker who produced them. Method One hundred twenty 22-month-olds were assigned to 1 of 4 conditions. Using the intermodal preferential looking paradigm, toddlers were shown visual displays containing 1 familiar object and 1 novel object, labeled by either a child or an adult. Familiar objects were labeled correctly or with a small mispronunciation that is either common in child speech (e.g., waisin for raisin) or infrequent (e.g., rauter for water). Results A significant interaction of speaker and type of mispronunciation showed that, for the child speaker, toddlers treated common and infrequent mispronunciations similarly, with equivalently sized mispronunciation penalties relative to correctly pronounced labels. In contrast, for the adult speaker, toddlers showed a large penalty for common mispronunciations, but infrequent mispronunciations were treated equivalently to correct pronunciations. Conclusion These results both reinforce and extend previous work on toddlers' processing of mispronunciations by revealing a complex interplay of speaker, type of mispronunciation, and specific contrast in toddlers' perceptions of mispronunciations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".