Children's early social interactions: Latent profiles of lying and antisocial behaviours
Bibliographic record
Abstract
Lie-telling behaviours emerge during the preschool years. While previous research has primarily examined the role of cognitive factors in children’s deception, such as executive functioning and theory of mind (e.g. Ding et al., 2015; Talwar & Lee, 2008; Williams et al., 2016), fewer studies have examined how lie-telling manifests within broader behavioural profiles during early childhood. Most research on deception profiles has focused on older children and adults (e.g. Lavoie et al., 2017) leaving limited insight into how these patterns begin to emerge in early childhood. To address this gap, the current study uses a multi-method approach with naturalistic observation in early education centres, a modified Temptation Resistance Paradigm (TRP), and a social behaviour educator-report questionnaire to examine how deception relates to antisocial behaviours in 107 preschool-aged children (Mage = 48.60 months, SD = 8.89 months, 56.1% male). Using Latent Profile Analysis, three distinct behavioural profiles were identified: (1) the Typical Behaviour Group (TBG), characterized by moderate lying, low aggression, and high social inhibition; (2) the Moderate Problem Behaviour Group (MPBG), defined by frequent lying and poor social inhibition; and (3) the Behaviourally Dysregulated Group (BDG), defined by elevated aggression, oppositionality, and social inhibition but low levels of deception. Educator ratings aligned with the behavioural observations, particularly for the BDG profile. TRP results further supported behavioural distinctions across groups. These findings demonstrate the importance of early identification of distinct behavioural patterns in children’s lie-telling, offering insights for early intervention and prevention strategies aimed at supporting positive developmental outcomes.Keywords: lie-telling, preschool children, antisocial behaviours, latent profiles, behavioural 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".