Perceptual Reorganization of Lexical Tones: Effects of Age and Experimental Procedure
Bibliographic record
Abstract
Findings on the perceptual reorganization of lexical tones are mixed. Some studies report good tone discrimination abilities for all tested age groups, others report decreased or enhanced discrimination with increasing age, and still others report U-shaped developmental curves. Since prior studies have used a wide range of contrasts and experimental procedures, it is unclear how specific task requirements interact with discrimination abilities at different ages. In the present work, we tested German and Cantonese adults on their discrimination of Cantonese lexical tones, as well as German-learning infants between 6 and 18 months of age on their discrimination of two specific Cantonese tones using two different types of experimental procedures. The adult experiment showed that German native speakers can discriminate between lexical tones, but native Cantonese speakers show significantly better performance. The results from German-learning infants suggest that 6- and 18-month-olds discriminate tones, while 9-month-olds do not, supporting a U-shaped developmental curve. Furthermore, our results revealed an effect of methodology, with good discrimination performance at 6 months after habituation but not after familiarization. These results support three main conclusions. First, habituation can be a more sensitive procedure for measuring infants' discrimination than familiarization. Second, the previous finding of a U-shaped curve in the discrimination of lexical tones is further supported. Third, discrimination abilities at 18 months appear to reflect mature perceptual sensitivity to lexical tones, since German adults also discriminated the lexical tones with high accuracy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".