Young Deceivers: Executive Functioning and Antisocial Lie‐telling in Preschool Aged Children
Bibliographic record
Abstract
The present study examined the emergence of antisocial lie‐telling in very young children. Lie‐telling was studied in relation to executive functioning skills and children's abilities to identify both truths and lies. A total of 65 children (Mage in months = 31.75, SD = 1.87) participated in a modified temptation resistance paradigm (TRP; designed to elicit spontaneous lies). Executive functioning was measured through an inhibitory control task and a forward search planning task. The Truth/Lie Identification task was administered (Lyon, Carrick, & Quas, ) to measure children's abilities to accurately distinguish truths and lies. During the TRP, a total of 89.23% children peeked at the toy when a research assistant left the room, and of those children, 29.31% lied to the research assistant. Significant differences on executive functioning measures were found between lie‐tellers and confessors, as well as for the Truth/Lie Identification task. Lie‐tellers had higher scores on measures of inhibitory control and forward search planning. Lie‐tellers also had higher accuracy on the Truth/Lie Identification task than confessors. This study provides a unique contribution to the literature by examining 2.5‐year‐old children's emerging lie‐telling abilities, a relatively understudied age during which fledgling lie‐telling emerges. Copyright © 2016 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".