Lexical and grammatical development: a behavioural genetic perspective
Bibliographic record
Abstract
The relation of lexical and grammatical knowledge is at the core of many controversies in linguistics and psycholinguistics. Recent empirical findings that the two are highly correlated in early language development have further energized the theoretical debate. Behavioural genetics provides an illuminating new tool to explore this question, by addressing the question of whether the empirical correlation simply reflects the fact that environments which facilitate one aspect of language growth also facilitate the other, or whether the same underlying acquisition mechanisms, influenced by the same genes, are responsible for the correlation. We explored this issue in a study of 2898 pairs of two-year-old twins born in England and Wales. Language development was assessed by their parents using an adapted version of the MacArthur Communicative Development Inventory which assesses vocabulary and grammar. Moderate heritabilities were found for both. As in previous studies, measures of vocabulary and sentence complexity were substantially correlated (r = 0.66). Behaviour-genetic modelling of the relation of vocabulary and grammar produced an estimated value of 0.61 for the genetic correlation, a measure of the overlap of the genetic effects that contribute to the two aspects of language development. In contrast, a measure of nonverbal cognitive development, the PARCA, was only weakly correlated at both the phenotypic level and at the level of genetic correlations with the language measures. Thus, although the distinction between verbal and nonverbal skills has a genetic basis underlying the phenotypic dissociation, there is little evidence either genetically or phenotypically for a dissociation between vocabulary and grammar within language.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".