Learning novel intransitive verbs from input cues: Experiments with Mandarin-learning toddlers
Bibliographic record
Abstract
In two novel verb experiments using the visual fixation paradigm, we investigated how Mandarin-learning toddlers employ distributional cues and semantic cues to categorize novel unaccusative and unergative verbs. In Experiment 1, 31-month-old (but not 19-month-old) participants were found to use the word-order cue to categorize two novel verbs VUA and VUE: after hearing “VUA-le NP” and “NP VUE-le” in the training phase, they categorized VUA as unaccusative and VUE as unergative, showing discrimination in looking times between grammatical trials “NP VUA-le” and ungrammatical trials “VUE-le NP” in the test phase. In Experiment 2, 31-month-olds used the semantic cue of telicity provided via novel events to make categorizations: watching a telic event paired with “VUA-le” and an atelic event paired with “VUE-le” led to differentiation between grammatical trials “VUA-le NP” and ungrammatical trials “VUE-le NP”. The findings provide evidence for toddlers’ ability to extract information from the input and make generalizations in verb learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".