Is the Truth in Your Words? Distinguishing Children’s Deceptive and Truthful Statements
Bibliographic record
Abstract
Children’s (N = 48) and adults’ (N = 28) truthful and deceptive statements were compared using a linguistics-based computer software program. Children (4 to 7 years of age) and adults (18 to 25 years of age) participated in a mock courtroom experiment, in which they were asked to recount either a true or fabricated event. Testimonies were then analyzed using Linguistic Inquiry and Word Count Software (LIWC; Pennebaker et al. 2007). This software has been previously used to detect adults’ deceptive statements (e.g., Bond and Lee, 2005). To date, no research has used this method on children’s narratives, nor has this software been used to compare those narratives to adult counterparts. Markers generated through the LIWC program achieved detection rates of 72.40% for samples of both children’s and adults’ narratives combined. In contrast, adult laypersons’ (N = 48) detection rates, for the same narratives (i.e., both children and adults) were close to chance. More specifically, detection rates were above chance for truth (65.00%) and below chance for lies (45.00%). Thus, the linguistic profile provided through LIWC yielded greater accuracy for evaluating the veracity of children’s and adults’ narratives compared to adult laypersons’ detection accuracy.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".