Early Adversity and Reactive Aggression in Adulthood: A Moderated Mediation Analysis of Hostile Attribution Bias and Emotional Processing
Bibliographic record
Abstract
Uncovering the underlying mechanisms involved in reactive aggression is essential to better understanding and managing this harmful behavior. Extant research has provided evidence supporting the relation between adverse childhood experiences (ACEs; e.g. abuse, neglect, witnessing interpersonal violence) and reactive aggression in adulthood. Yet, critical questions about how emotional processes might interact with cognitive biases in the relation between early adversity and later reactive aggression remain largely unanswered. The rationale of the present study was to address this gap. Participants were recruited from an undergraduate participant pool and an international crowdsourcing platform to complete an online survey. Conditional process analysis was conducted to test a moderated mediation model, which hypothesized that the relation between ACEs and reactive aggression would be mediated by hostile attribution bias (HAB) and that emotional understanding (EU) and emotion management (EM) abilities would moderate this indirect effect. The hypothesized model was partially supported (N = 225), such that higher levels of ACEs predicted higher levels of reactive aggression in adulthood, and this relation was mediated by HAB. Additionally, low and average levels of EU ability interacted with ACEs and strengthened the observed effect on HAB scores. Contrary to predictions, EM ability was not a significant moderator. Findings are reviewed in the context of understanding and supporting the social information and emotional processing of adults with historical ACEs and current reactive aggression. Theoretical and practical implications of integrating cognitive and emotional processing frameworks in this area are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".