Phonetic features guide phonotactic generalizations in perception.
Bibliographic record
Abstract
Although features are useful theoretical constructs (they allow parsimonious descriptions of phonological inventories, patterns, and alternations), their etology remains debated. Three hypotheses have been advanced: (1) Phonological patterns involving phonetically natural classes arise from historical changes affecting similar sounds, but features have no psychological reality; (2) features emerge in the listeners’ phonology on the basis of phonetic and phonological experience (including exposure to patterns resulting from historical changes); (3) features are abstract symbols provided by universal grammar (UG), and they are independent of phonetic implementation. To test these alternatives, French and English listeners were exposed to a constraint on obstruent voicing, and tested implicitly on their generalization of the constraint to untrained obstruents. If the historical hypothesis is true, no generalization would occur. Since voiced stops and fricatives form a phonetic natural class in French but not in English, emergent features would favor generalization only in this language. In contrast, voiced stops and fricatives are natural classes in terms of abstract phonological features in both languages; therefore, the UG hypothesis predicts both groups will generalize. Current results support the emergent hypothesis [French generalized: t(11) = 2.5, p<0.03; English did not: t(8) = 1.69, p>0.1], but additional data are being collected.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".