Infant–parent attachment and lie-telling in young children: The Generation R Study
Bibliographic record
Abstract
Insecure-attached adults are more likely to lie. However, it is unknown whether infant-parent attachment quality relates to lie-telling in early childhood. As in adults, lie-telling in early childhood might be related to attachment insecurity. However, a competing hypothesis might be plausible; lie-telling might be related to attachment security given that lie-telling in early childhood is considered an advancement in social-cognitive development. The current study is the first to investigate the link between insecure/secure and disorganized/non-disorganized attachment and lie-telling behavior in early childhood. Because lie-telling is studied in the context of cheating behavior, the association between cheating and attachment is additionally explored. A total of 560 Dutch children (287 girls) from a longitudinal cohort study (Generation R) were included in the analyses. Attachment quality with primary caregiver (secure/insecure and disorganized/non-disorganized attachment) was assessed at 14 months of age in the Strange Situation Procedure, and cheating and lie-telling were observed in games administered at 4 years of age. The results demonstrated no relationship of attachment (in)security and (dis)organization with cheating and lie-telling. Results are interpreted in light of evidence that lie-telling in early childhood is part of normative development. Limitations are discussed, including the time lag between assessments, the fact that lie-telling was measured toward a researcher instead of a caregiver, and the conceptualization of attachment in infancy versus adulthood. Attachment quality does not affect early normative lie-telling, but how and when it may affect later lying in children remains to be explored.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".