Conquer primal fear: Phonological features are innate and substance-free
Bibliographic record
Abstract
Abstract We argue that the representational primes of the human phonological faculty, the so-called distinctive features, are innate and substance-free. Our arguments for the innateness of features are built on traditional and novel logical arguments, experimental evidence accumulating over recent decades, and somewhat detailed proposals about their neurobiological reality. As symbols in the brain, features are substance-free, that is, they are devoid of articulatory and acoustic content, or even any direct reference to such phenomena. This is consistent with our substance-free conception of phonological computation, an approach that eschews functionalist notions like markedness, ease of articulation, and so on. We also outline a neural model of the phonetics-phonology interface called Cognitive Phonetics, which transduces innate features to speech articulation and from speech acoustics. These extra-grammatical transduction procedures are also part of the human biological endowment, which leaves no room for language-specific phonetics in our theory of the externalization of language. We show how Cognitive Phonetics can account for traditionally recognized intersegmental coarticulation, as well as the previously unexplored intrasegmental coarticulation, strongly suggesting that the basic units of speech production are transduced features.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.030 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".