Domain-specific processing of Mandarin tone.
Bibliographic record
Abstract
Lexical tone has generally been found to be processed predominantly in the left hemisphere. However, given that tone is carried by a syllable or a word with segmental information and distinctive meaning, the processing of tone may not be easily disentangled from that of the phonetic segments and word meaning [P. Wong, Brain Res. Bull. 59, 83–95 (2002)]. Indeed, previous research has not examined the lateralization of tone independent of segmental and lexical semantic information. The present study explores how syllable-based tonal processing in Mandarin Chinese interacts with these different linguistic domains. Using dichotic listening, native Mandarin participants were presented with monosyllabic tonal stimuli constructed with the following different linguistic attributes: (1) real Mandarin words with tonal, segmental phonetic, and lexical semantic information; (2) Mandarin nonwords with tonal and segmental, but no semantic information; (3) nonwords with non-Mandarin segments (i.e., no native segmental or semantic information); and (4) hums of tones (acoustic pitch information) without any segmental or semantic components. Results from these conditions show significant differences in lateralization patterns and are discussed in terms of the integration of acoustic as well as pre- and post-lexical linguistic domains in lexical tone processing. [Work supported by NSERC.]
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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) | 0.004 | 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".