Mandarin-learning 19-month-old toddlers’ sensitivity to word order cues that differentiate unaccusative and unergative verbs
Bibliographic record
Abstract
Abstract Languages employ different means to manifest the unaccusative-unergative distinction. In Mandarin Chinese, unaccusative verbs are allowed in the inversion construction “V-le NP”, while unergative verbs are not. This grammaticality contrast brings a presence/absence contrast between the two verb classes in the inversion construction in the input. Using an eye fixation task, we investigated whether Mandarin-learning 19-month-olds were sensitive to this specific input frequency contrast. We found that infants distinguished the grammatical versus ungrammatical uses of the two verb classes in the inversion construction “V-le NP” (Experiment 1). When the verb classes were in the “NP V-le” order (Experiment 2) (i.e., the same level of grammaticality), infants showed no evidence of a looking difference. These responses indicate toddlers’ sensitivity to the distribution of the two verb classes in the inversion construction. This distributional information is likely to be one of the potential cues that facilitate their acquisition of the unaccusative-unergative distinction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".