20-Month-Old infants’ Use of Noun and Verb Morphosyntactic Cues in Novel Word Learning in Dynamic Events
Bibliographic record
Abstract
Using a habituation paradigm with a three-switch design, the present study investigated whether 20-month-old French-learning infants use noun and verb morphosyntactic cues to learn novel words in dynamic events differentially when both the agent and the action interpretations are possible. Of particular interest was whether infants’ initial interpretation of novel nouns referring to novel animate objects in dynamic events includes not only the novel agents but also their actions. The following two contrastive hypotheses were specifically tested: (1) infants map novel verbs to the novel actions only and novel nouns to the novel agents only. Alternatively, (2) they map novel verbs to the novel actions only but novel nouns to both the novel agents and their actions. Infants watched dynamic events in which novel agents performed novel intransitive actions, while hearing novel words in a noun phrase or a verb sentence. When novel words were preceded by a pronoun “il”, infants were able to map novel verbs to the actions but not to the agents. However, when novel words were preceded by a determiner “un”, they mapped the novel nouns to both the agents and their actions. Two follow-up noun experiments showed that they mapped the novel nouns onto the agents and their actions, even when additional noun morphosyntactic cues were given. These findings demonstrate that 20-month-old infants are able to use noun and verb morphosyntactic cues to learn novel words in dynamic events differentially and provide some evidence to support that infants’ initial representations of novel nouns referring to novel animate objects in dynamic events include both the agents and their actions.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".