Intimate Partner Violence and Perception of Partner Hostility During Conflict Among Young Adult Couples
Bibliographic record
Abstract
Romantic partners can be both accurate and biased in their perception of each other’s hostile behaviors. In perpetrators and victims of intimate partner violence (IPV), documented deficits in social cognition and hostile attributions could contribute to greater biases. The current study used the truth and bias model to examine accuracy and bias in perception of the partner’s hostility during a conflict discussion among young adult couples, and the role of IPV perpetration and victimization in this perception. Young adult couples ( n = 178) engaged in a video-recorded conflict discussion. Using a video-recall task, participants rated their own and their partner's hostility every 30 s of the discussion. Results of truth and bias analyses revealed that individuals accurately tracked fluctuations in their partner hostility (i.e., tracking accuracy) during the conflict discussion, but perceived their partner as more hostile when they themselves felt more hostile (i.e., projection). Regarding the role of IPV perpetration, physically violent individuals showed greater projection and sexually violent individuals overestimated (i.e., directional bias) their partner’s hostility during the conflict discussion compared to nonviolent individuals. Regarding IPV victimization, individuals who experienced higher levels of psychological IPV overestimated their partner’s hostility and showed greater tracking accuracy compared with individuals who experienced lower levels of psychological IPV. Victims of physical IPV showed greater tracking accuracy and lower projection than nonvictims. Victims of sexual IPV underestimated their partner’s hostility and evidenced poorer tracking accuracy than nonvictims. These findings contribute to understand social information processing during conflict among young adult couples, according to their experience of violence.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".