Infants’ attachment insecurity predicts attachment-relevant emotion regulation strategies in adulthood.
Bibliographic record
Abstract
Infant attachment is theorized to lay the foundation of emotion regulation across the life span. However, testing this proposition requires prospective designs examining whether attachment assessed in infancy predicts emotion regulation strategies observed in adult relationships. Using unique data from the Minnesota Longitudinal Study of Risk and Adaptation, we examined whether infant attachment assessed at 12 and 18 months in the Strange Situation were associated with attachment-relevant emotion regulation strategies coded from video-recorded conflict discussions with romantic partners at ages 20, 23, 26, and/or 35. The current research first integrated the developmental and emotion regulation literatures to identify three specific attachment-relevant emotion regulation strategies. Balanced-regulation involves being open, approach-orientated, and engaging in collaborative problem-solving. Hypo-regulation involves suppressing emotions, disengaging from close others, and engaging in superficial problem-solving. Hyper-regulation involves exaggerating emotional expressions, ruminating, and being self-focused in processing issues. Compared to stable secure infants (secure at 12 and 18 months), stable insecure infants (insecure at 12 and 18 months) displayed worse balanced-regulation and greater hypo-regulation strategies, and unstable insecure infants (insecure at 12 or 18 months) displayed greater hyper-regulation strategies, in relationship-threatening situations 20-35 years later. Conceptually replicating these results, greater friendship insecurity at age 16 predicted worse balanced-regulation and greater hypo- and hyper-regulation strategies during relationship-threatening situations in adulthood. These findings highlight that infant attachment insecurity is associated with distinct emotion regulation strategies in adulthood 20-35 years later. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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) | 0.000 | 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 | 0.001 |
| 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".