Childhood maltreatment influences parental mimicry of children's emotional facial expressions
Bibliographic record
Abstract
BACKGROUND: Childhood maltreatment can disrupt socio-emotional functioning, potentially influencing how parents respond to children's emotional facial expressions. Mimicry, an automatic reaction to others' facial expressions, is a critical mechanism for social bonding and affiliation in parent-child relationships. However, the effects of childhood maltreatment on parental mimicry remain underexplored. OBJECTIVE: This study examined the relationship between different forms of childhood maltreatment and parents' mimicry of children's emotional facial expressions. PARTICIPANTS AND SETTING: Fifty-seven parents participated in an emotion recognition task conducted either at a local community organization or at the university laboratory. METHODS: Parents' facial reactions were recorded and analyzed using FaceReader software to detect mimicry. The Childhood Trauma Questionnaire (CTQ) assessed parental history of maltreatment. A path analysis model was conducted to evaluate the associations between forms of childhood maltreatment and parental mimicry. RESULTS: A history of physical abuse predicted increased expressions of anger, while physical neglect was linked to reduced anger but heightened disgust. Emotional and sexual abuse were associated with diminished mimicry of sadness, whereas emotional neglect predicted enhanced sadness mimicry. CONCLUSIONS: Findings suggest that childhood maltreatment alters parents' facial reactions to children's emotional facial expressions, potentially impacting parental sensitivity.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".