<i>Uh</i> and <i>euh</i> signal novelty for monolinguals and bilinguals: evidence from children and adults
Bibliographic record
Abstract
Previous research suggests that English monolingual children and adults can use speech disfluencies (e.g., uh) to predict that a speaker will name a novel object. To understand the origins of this ability, we tested 48 32-month-old children (monolingual English, monolingual French, bilingual English-French; Study 1) and 16 adults (bilingual English-French; Study 2). Our design leveraged the distinct realizations of English (uh) versus French (euh) disfluencies. In a preferential-looking paradigm, participants saw familiar-novel object pairs (e.g., doll-rel), labeled in either Fluent ("Look at the doll/rel!"), Disfluent Language-consistent ("Look at thee uh doll/rel!"), or Disfluent Language-inconsistent ("Look at thee euh doll/rel!") sentences. All participants looked more at the novel object when hearing disfluencies, irrespective of their phonetic realization. These results suggest that listeners from different language backgrounds harness disfluencies to comprehend day-to-day speech, possibly by attending to their lengthening as a signal of speaker uncertainty. Stimuli and data are available at .
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".