Beyond 50%: providing contextual and coaching information substantially improves adults’ ability to detect children’s lies
Bibliographic record
Abstract
The present research examined how contextual/coaching information and interview format influenced adults’ ability to detect children’s lies. Participants viewed a series of child interview videos where children provided either a truthful report or a deceptive report to conceal a co-transgression; participants reported if they thought each child was lying or telling the truth. In Study 1 (N = 400), participants were assigned to one of the following conditions that varied in the type of interview shown and if context about the event in question was provided: full interview + context, recall questions + context, recognition questions + context, or full interview only (no context). Providing context (information about the potential co-transgression and coaching) significantly enhanced overall and lie accuracy, but this served the greatest benefit when provided with the recall interview, and participants held a lie bias. In Study 2 (N = 100), participants watched the full interview with simplified coaching information. Detection accuracy was reduced slightly but remained well above chance and the lie bias was eliminated. Thus, detection performance is improved when participants are given a child’s free-recall interview along with background information on the event and potential coaching, though providing specific coaching details introduces a lie bias.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".