Children's Acquisition of the English Past‐Tense: Evidence for a Single‐Route Account From Novel Verb Production Data
Bibliographic record
Abstract
This study adjudicates between two opposing accounts of morphological productivity, using English past-tense as its test case. The single-route model (e.g., Bybee & Moder, ) posits that both regular and irregular past-tense forms are generated by analogy across stored exemplars in associative memory. In contrast, the dual-route model (e.g., Prasada & Pinker, ) posits that regular inflection requires use of a formal "add -ed" rule that does not require analogy across regular past-tense forms. Children (aged 3-4; 5-6; 6-7; 9-10) saw animations of an animal performing a novel action described with a novel verb (e.g., gezz; chake). Past-tense forms of novel verbs were elicited by prompting the child to describe what the animal "did yesterday." Collapsing across age group (since no interaction was observed), the likelihood of a verb being produced in regular past-tense form (e.g., gezzed; chaked) was positively associated with the verb's similarity to existing regular verbs, consistent with the single-route model only. Results indicate that children's acquisition of the English past-tense is best explained by a single-route analogical mechanism that does not incorporate a role for formal rules.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".