Perception and Representation of Lexical Tones in Native Mandarin-Learning Infants and Toddlers
Bibliographic record
Abstract
We investigated the perceptual development of lexical tones in native tone-learning infants during the first 2 years of life, focusing on two important stages of phonological acquisition: the preverbal and vocabulary explosion stages. Experiment 1 examined monolingual Mandarin-Chinese-learning 4- to 13-month-olds' discrimination of similar lexical tones in Mandarin, Tone 2 (T2, rising) vs. Tone 3 (T3, low-dipping). Infants were habituated to exemplars of one tone (either T2 or T3), and tested with new exemplars of the habituated tone vs. the contrasting tone. Results show that looking time increased for the contrasting tone, but not for new exemplars of the habituated tone, suggesting that infants discriminated the two tones as separate categories. Furthermore, infants' discrimination of the tones was comparable across ages. Experiment 2 tested whether tones are distinguished in toddlers' lexicon. Monolingual Mandarin-learning 19- to 26-month-olds were presented with pairs of objects while one was named. Targets were familiar words bearing T2 or T3, either correctly pronounced (CP) or mispronounced (MP) in tone. We found that word recognition was equally successful in CP and in MP trials when T2 was mispronounced as T3 and T3 as T2, indicating that T2 and T3 are confusable. In contrast, recognition failed when T2 and T3 words were mispronounced as Tone 4 (T4, falling), showing that T4 was represented as a distinct category. Results show that toddlers have difficulty encoding similar tones distinctly in known words. The T2-T3 contrast is particularly challenging because of Tone 3 Sandhi, which changes T3 to T2 when it precedes another T3. At the stage when toddlers track the meaning of T2 and T3 words and track the sandhi alternations, they seem to overgeneralize the two tones as variants of one functional category, reflecting perceptual organization at the level of phonemic learning.
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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 | 0.001 |
| 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 | 0.000 |
| 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".