Infants’ learning of novel words in a stochastic environment.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it