Five-Year-Olds’ and Adults’ Use of Paralinguistic Cues to Overcome Referential Uncertainty
Bibliographic record
Abstract
An eye-tracking methodology was used to explore adults’ and children’s use of two utterance-based cues to overcome referential uncertainty in real time. Participants were first introduced to two characters with distinct color preferences. These characters then produced fluent (“Look! Look at the blicket.”) or disfluent (“Look! Look at thee, uh, blicket.”) instructions referring to novel objects in a display containing both talker-preferred and talker-dispreferred colored items. Adults (Expt 1, n = 24) directed a greater proportion of looks to talker-preferred objects during the initial portion of the utterance (“Look! Look at…”), reflecting the use of indexical cues for talker identity. However, they immediately reduced consideration of an object bearing the talker’s preferred color when the talker was disfluent, suggesting they infer disfluency would be more likely as a talker describes dispreferred objects. Like adults, 5-year-olds (Expt 2, n = 27) directed more attention to talker-preferred objects during the initial portion of the utterance. Children’s initial predictions, however, were not modulated when disfluency was encountered. Together, these results demonstrate that adults, but not 5-year-olds, can act on information from two talker-produced cues within an utterance, talker preference and speech disfluencies, to establish reference.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".