The use of disfluency cues in spoken language processing: Insights from aging.
Bibliographic record
Abstract
Past research suggests listeners treat disfluencies as informative cues during spoken language processing. For example, studies have shown that child and younger adult listeners use filled pauses to rapidly anticipate discourse-new objects. The present study explores whether older adults show a similar pattern, or if this ability is reduced in light of age-related declines in language and cognitive abilities. The study also examines whether the processing of disfluencies differs depending on the talker's age. Stereotyped ideas about older adults' speech could lead listeners to treat disfluencies as uninformative, similar to the way in which listeners react to disfluencies produced by non-native speakers or individuals with a cognitive disorder. Experiment 1 used eye tracking to capture younger and older listeners' real-time reactions to filled pauses produced by younger and older talkers. On critical trials, participants followed fluent or disfluent instructions referring to either discourse-given or discourse-new objects. Younger and older listeners treated filled pauses produced by both younger and older talkers as cues for reference to discourse-new objects despite holding stereotypes regarding older adults' speech. Experiment 2 further explored listeners' biased judgments of talkers' fluency, using auditory materials from Experiment 1. Speech produced by an older talker was rated as more disfluent and slower than a younger talker even though these features were matched across recordings. Together, the findings demonstrate (a) older listeners' effective use of disfluency cues in real-time processing and (b) that listeners treat both older and younger talkers' disfluencies as informative despite biased perceptions regarding older talkers' speech. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".