Acquisition of categorical perception of Mandarin tone
Bibliographic record
Abstract
This experiment examined the effect of motor repetition on the learning of Mandarin pitch categories by non-native speakers. In Mandarin, there are four tonal categories, which refer to pitch contours that discriminate words. Differentiating tonal categories is both essential and arduous for non-native speakers to learn. Native speakers do this effortlessly because they have a categorical perception (CP) effect for tones, i.e. they perceive items within a tonal category as more similar to each other and items between categories as more different. Non-native speakers do not have this effect, and this experiment attempted to induce the CP effect for Mandarin tones via a training task, which was the experimental manipulation: Participants either repeated a tone stimulus before categorizing it or listened to a stimulus and categorized it without repetition. Discrimination between and within tonal categories was measured before and after training. All participants demonstrated increased between-category and within-category discrimination after training, except for learners who repeated stimuli in the training phase. They demonstrated a decrease in within-category discrimination, showing a weak CP effect that could be stronger with more training. Implications of these results on auditory category learning and language education will be 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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".