Constructing a Proto-Lexicon: An Integrative View of Infant Language Development
Bibliographic record
Abstract
Infants begin learning the phonological structure of their native language remarkably early and use this information to extract word-sized chunks from the speech signal. While acquiring the language-specific segmentation strategies appropriate for their native language, infants are simultaneously beginning to form word–object pairings and learning which sound contrasts are meaningful in the native language. They are also working out how to assign words to word classes, paying attention to the use and placement of function words, and learning how speakers of the language string words together to form sensible grammatical utterances. Amazingly, infants tackle all of these tasks simultaneously, with success in each of these domains dependent on success in the others. This review focuses on infants' discovery of word forms in speech, their construction of a proto-lexicon, and the development of linguistic knowledge during their first year and a half of life. By discussing the development of lexical knowledge in relation to other aspects of linguistic development, I demonstrate the advantages of an integrative approach to understanding early language acquisition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".