Motor Influences on Grammar in an Emergentist Model of Phonology
Bibliographic record
Abstract
Any account aiming to provide a comprehensive picture of children's acquisition of speech must consider both the development of the phonological grammar and the maturation of the structures and motor skills used to implement the sounds of a language. Much previous literature has been marked by a tendency to draw sharp demarcations between motor and grammatical influences, or to assert that all of child speech can be reduced to one or the other. This paper argues that it is neither necessary nor desirable to segregate speech‐motor development from grammatical development when modeling speech acquisition, because they are fundamentally intertwined. The paper focuses on bringing together two literatures that have evolved largely independently. The first explores how speech‐motor patterns practiced during babbling come to be disproportionately represented in the lexicon in children's earliest stages of meaningful speech. The second posits that abstract elements of phonology – segments, features, and constraints – can be understood to emerge from generalizations over stored memory traces at a more holistic level. We argue that an emergentist model of phonological learning can be enhanced by incorporating the insight that memory traces of strings that have been heard and produced are encoded more robustly than strings that have only been heard.
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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 | 0.002 |
| 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 | 0.005 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".