Younger and Older Adults' Lie-Detection and Credibility Judgments of Children's Coached Reports
Bibliographic record
Abstract
Previous research has examined young and middle-aged adults’ perceptions of child witnesses; however, no research to date has examined how potential older adult jurors may perceive a child witness. The present investigation examined younger (18–30 years, N = 100) and older adults’ (66–89 years, N = 100) liedetection and credibility judgments when viewing children’s truthful and dishonest reports. Participants viewed eight child interview videos where children (9–11 years of age) either provided a truthful report or a coached fabricated report to conceal a transgression. Participants provided lie-detection judgments following all eight videos and credibility assessments following the first two videos. Participants completed a General Lifespan Credibility questionnaire to assess credibility evaluations across various witness ages. Lie-detection results indicated that older adults had significantly lower discrimination scores, a stronger truth bias, and greater confidence compared to younger adults. Older adults also rated children as more competent to testify in court, credible, honest, believable, and likeable than younger adults. Participants with greater differences in their credibility evaluations for truth and lie-tellers were significantly more accurate at detecting lies. Responses to the Lifespan Credibility questionnaire revealed significant differences in younger and older adults’ credibility evaluations across the lifespan.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".