Perception and lexical representation of Mandarin tones by nonnative listeners
Bibliographic record
Abstract
Previous studies have shown that adult second language (L2) listeners often experience difficulty encoding language-specific phonological contrasts in word recognition. However, most research on L2 lexical representations has focused on consonants and vowels, and much remains unknown on how lexical tones are encoded in L2 phonological lexicon. In the current study, two experiments were conducted with twenty English learners of Mandarin and 20 Mandarin native speakers. In an ABX task, native speakers outperformed L2 listeners with higher accuracy rate and shorter response latencies. However, both groups showed poor discrimination sensitivity for pairs sharing similar tone contours (i.e., T2-T3). In a medium-lag repetition priming task, listeners were presented with a prime followed by a target that is either the same as the prime (e.g., ni2-ni2) or the other member of a minimal tone pair (e.g., ni2-ni3), eight to 20 items further down in the list. Results show that while significant facilitations in the repetition condition were observed in both groups, in the minimal-tone-pair condition (i.e., T2-T3), positive priming was observed only in the L2 group. The results of the two experiments provide insight into the interface between phonological and lexical levels in L2 spoken word recognition.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".