Do Young Children Use Verbal Disfluency as a Cue to Their Own Confidence?
Bibliographic record
Abstract
Metacognitive reasoning is central to decision-making. For every decision, we can also judge our trust in that decision, or our level of confidence. The mechanisms and representations underlying reasoning about confidence remain debated. We test whether children rely on processing fluency to infer their own confidence: do decisions that come quickly and easily lead to high confidence, while decisions that are slow and effortful result in low confidence? Using children's verbal disfluency-fillers (e.g., "umm," "uhh"), hedges (e.g., "I think," "maybe"), and pauses in speech-as an observable index of processing fluency, we assess whether children's reports of confidence are a read-out of their verbal disfluency. Five-to-eight-year-olds answered semantic questions about animals and performed perceptual comparisons, then reported their confidence in their answers in a two-alternative forced-choice confidence judgment task. Verbal disfluency predicted both answer accuracy and children's reports of confidence: children produced more fillers, more hedges, and longer speech onsets during incorrect trials and during low confidence trials. But we also found a dissociation between fluency and confidence. When examining trials where accuracy and confidence diverge (i.e., correct but low confidence or incorrect but high confidence trials), we observe no reliable relationship between confidence and fillers and hedges, and children take longer to begin answering on high confidence trials. We conclude that-in 5-8-year-old-children-fluency is a reliable tracker of accuracy but not confidence, and that fluency is only predictive of metacognitive judgments in children when confidence and accuracy are aligned.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".