Infants’ learning of novel words in a stochastic environment.
Bibliographic record
Abstract
In everyday word learning words are only sometimes heard in the presence of their referent, making the acquisition of novel words a particularly challenging task. The current study investigated whether children (18-month-olds who are novice word learners) can track the statistics of co-occurrence between words and objects to learn novel mappings in a stochastic environment. Infants were briefly trained on novel word-novel object pairs with variable degrees of co-occurrence: Words were either paired reliably with 1 referent or stochastically paired with 2 different referents with varying probabilities. Infants were sensitive to the co-occurrence statistics between words and referents, tracking not just the strongest available contingency but also low-frequency information. The statistical strength of the word-referent mapping may also modulate real-time online lexical processing in infants. Infants are thus able to track stochastic relationships between words and referents in the process of learning novel words.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".