Toddlers use speech disfluencies to predict speakers’ referential intentions
Bibliographic record
Abstract
The ability to infer the referential intentions of speakers is a crucial part of learning a language. Previous research has uncovered various contextual and social cues that children may use to do this. Here we provide the first evidence that children also use speech disfluencies to infer speaker intention. Disfluencies (e.g. filled pauses 'uh' and 'um') occur in predictable locations, such as before infrequent or discourse-new words. We conducted an eye-tracking study to investigate whether young children can make use of this distributional information in order to predict a speaker's intended referent. Our results reveal that young children (ages 2;4 to 2;8) reliably attend to speech disfluencies early in lexical development and are able to use disfluencies in online comprehension to infer speaker intention in advance of object labeling. Our results from two groups of younger children (ages 1;8 to 2;2 and 1;4 to 1;8) suggest that this ability emerges around age 2.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".