When it is apt to adapt: Flexible reasoning guides children’s use of talker identity and disfluency cues
Bibliographic record
Abstract
An eye-tracking methodology was used to examine whether children flexibly engage two voice-based cues, talker identity and disfluency, during language processing. Across two experiments, 5-year-olds (N = 58) were introduced to two characters with distinct color preferences. These characters then used fluent or disfluent instructions to refer to an object in a display containing items bearing either talker-preferred or talker-dispreferred colors. As the utterance began to unfold, the 5-year-olds anticipated that talkers would refer to talker-preferred objects. When children then encountered a disfluency in the unfolding description, they reduced their expectation that a talker was about to refer to a preferred object. The talker preference-related predictions, but not the disfluency-related predictions, were attenuated during the second half of the experiment as evidence accrued that talkers referred to dispreferred objects with equal frequency. In Experiment 2, the equivocal nature of talkers' referencing was made more apparent by removing neutral filler trials, where objects' colors were not associated with talker preferences. In this case, children ceased making all talker-related predictions during the latter half of the experiment. Taken together, the results provide insights into children's use of talker-specific cues and demonstrate that flexible and adaptive forms of reasoning account for the ways in which children draw on paralinguistic information during real-time processing.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".