The role of input cues in acquiring unaccusative and unergative verbs: Verb learning experiments with Mandarin-speaking toddlers
Bibliographic record
Abstract
Children make use of various information in linguistic input to learn verbs, including syntactic distribution and semantic features. Within the intransitive verb class, unaccusative and unergative verbs differ in distribution with respect to word order as well as in semantic features such as telicity. Both the distributional and semantic information might act as cues for learning the two types of verbs. In this study, we investigate how Mandarin-speaking toddlers make use of these input cues to learn the unaccusative-unergative distinction. In verb learning experiments using the visual fixation procedure, 31-month-old toddlers were taught two novel verb items (V UA and V UE ) and then tested on whether they were able to distinguish them. Results show that participants learned the difference between the two novel verbs based on the word-order cue and the telicity cue separately, but not simultaneously. Our findings provide evidence for toddlers’ ability to employ distributional and semantic information in the input during verb learning, shedding light on the learning mechanisms of verb argument structure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".