Theory of mind, executive function, and lying in children: a meta‐analysis
Bibliographic record
Abstract
Scientific research on how children learn to tell lies has existed for more than a century. Earlier studies mainly focused on moral, social, and situational factors contributing to the development of lying. Researchers have only begun to explore the cognitive correlations of children's lying in the last two decades. Cognitive theories suggest that theory of mind (ToM) and executive function (EF) should be closely related to the development of lying since lying is, in essence, ToM and EF in action. Yet, findings from empirical studies are mixed. To address this issue, the current meta-analysis reviewed all prior literature that examined the relations between children's lying and ToM and/or between children's lying and EF. In total, 47 papers consisting of 5099 participants between 2 and 19 years of age were included, which yielded 74 effect sizes for ToM and 94 effect sizes for EF. Statistically significant but relatively small effects were found between children's lying and ToM (r = .17) and between lying and EF (r = .13). Furthermore, EF's correlation with children's initial lies was significantly smaller than its correlation with children's ability to maintain lies. This comprehensive meta-analysis provides a clear picture of the associations between children's ToM/EF and their lying behavior and confirms that ToM and EF indeed play a positive role in children's lying and its development.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".