How fathers and mothers make their children laugh: Associations with the security of parent-child attachment relationships
Bibliographic record
Abstract
OBJECTIVES: Bowlby (1969/1982) described an infant's smile and pleasant vocalizations as attachment behaviors. However, most research on the formation of attachment relationships centers on the role of the caregiver's response to child distress, neglecting the role of behaviors that promote proximity in a positive context. This study aimed to 1) explore fathers' and mothers' laughter-eliciting behaviors and children's laughter using a newly developed coding instrument and 2) explore associations between these behaviors, children's laughter, and child attachment security. DESIGN: A sample of 144 English- and French-speaking families, with 3- to 5-year-old children, participated in a Laughing Task and a separation-reunion procedure. RESULTS: Distinct but similar factorial structures emerged for mothers and fathers, both including a Touch and a Movement/Sound factor. Parental strategies were significantly associated with preschoolers' laughter with both mothers and fathers. The path analysis results indicated that both laughter-eliciting strategies used by fathers were positively associated with children's laughter which was also positively associated with child attachment security. As for mother-child dyads, only the Touch factor was significantly associated with children's laughter. Furthermore, child attachment security was not associated with children's laughter, but was significantly associated with the Movement/Sound factor. CONCLUSIONS: Although fathers and mothers tend to use comparable strategies with similar success in making children laugh, the significance of this relational dynamic may differ in its contribution to the development of attachment security within parent-child dyads.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".