That’s thee, uuh blicket! How does disfluency affect children’s word learning?
Bibliographic record
Abstract
Disfluencies, such as ‘um’ or ‘uh’, can cause adults to attribute uncertainty to speakers, but may also facilitate speech processing. To understand how these different functions affect children’s learning, we asked whether (dis)fluency affects children’s decision to select information from speakers (an explicit behavior) and their learning of specific words (an implicit behavior). In Experiment 1a, 31 3- to 4-year-olds heard two puppets provide fluent or disfluent descriptions of familiar objects. Each puppet then labeled a different novel object with the same novel word (again, fluently or disfluently). Children more frequently endorsed the object referred to by the fluent speaker. We replicated this finding with a separate group of 4-year-olds in Experiment 1b ( N = 31) and a modified design. In Experiment 2, 62 3- to 4-year-olds were trained on new words, produced following a disfluency or not, and were subsequently tested on their recognition of the words. Children were equally accurate for the two types of words. These results suggest that while children may prefer information from fluent speakers, they learn words equally well regardless of fluency, at least in some contexts.
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.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".