Use of global trait cues helps to explain older adults’ decrements in detecting children’s lies
Bibliographic record
Abstract
Purpose Previous research has established that lie‐detection accuracy decreases with age; however, various mechanisms for this effect have yet to be explored, particularly when examining the detection of children’s lies. The present study investigated if younger and older adults detect children’s lies using different cues (verbal content, verbal auditory, non‐verbal, global traits) to explore if cue usage may help to explain this age‐related decline. Method A total of 100 younger (18–30 years) and 100 older adults (66–89 years) watched child interview videos (half were truth‐tellers; half were lie‐tellers coached to conceal a transgression). Participants provided veracity judgements (truth vs. lie) and described the cues that they relied on to make their judgements. Results Older adults used marginally significantly fewer verbal content and significantly more global trait cues compared to younger adults. The use of global trait cues partially mediated the age‐related decline in detection accuracy. Conclusion These results present a partial mechanism for the age‐related decline in deception detection. This can inform psychological theory on how ageing affects perceptions of child witnesses and deception detection abilities.
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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 | 0.014 |
| 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 | 0.001 |
| 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".