<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 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) | 0.000 | 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 teacher head, 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".