Order of presentation asymmetry in intonational contour discrimination in English.
Bibliographic record
Abstract
In the work of Hwang et al. (2007), native English speakers showed overall poor accuracy in distinguishing initially rising versus level (e.g., L*L*H- H*L-L% vs L*L*L- H*L-L%) or initially falling versus level (e.g., H*H*L- H*L-L% vs H*H*H- H*L-L%) contour contrasts on English phrases in an AX discrimination task. Results not reported in that paper found that it was easier to discriminate when a more complex F0 contour occurred second than when it occurred first. Several orders of presentation effects in the perception of intonation have been reported (e.g., L. Morton (1997); S. Lintfert (2003); Cummins et al. (2006)] but no satisfying account has been provided. This study investigated these asymmetries more systematically. The order effect was significant for falling-level contrast pairs: pairs with a more complex F0 contour last were discriminated more easily than the reverse order. Rising versus level contrasts showed a similar tendency. The results thus extend intonational discrimination asymmetries to these additional contours. They suggest that the cause of the asymmetries may depend more on F0 complexity than on F0 peak.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".