Cognitive Phonetics: The Transduction of Distinctive Features at the Phonology-Phonetics Interface
Bibliographic record
Abstract
We propose that the interface between phonology and phonetics is mediated by a transduction process that converts elementary units of phonological computation, features, into temporally coordinated neuromuscular patterns, called ‘True Phonetic Representations’, which are directly interpretable by the motor system of speech production. Our view of the interface is constrained by substance-free generative phonological assumptions and by insights gained from psycholinguistic and phonetic models of speech production. To distinguish transduction of abstract phonological units into planned neuromuscular patterns from the biomechanics of speech production usually associated with physiological phonetics, we have termed this interface theory ‘Cognitive Phonetics’ (CP). The inner workings of CP are described in terms of Marr’s (1982/2010) tri-level approach, which we used to construct a linking hypothesis relating formal phonology to neurobiological activity. Potential neurobiological correlates supporting various parts of CP are presented. We also argue that CP augments the study of certain phonetic phenomena, most notably coarticulation, and suggest that some phenomena usually considered phonological (e.g., naturalness and gradience) receive better explanations within CP.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".