Are aggressive people able to integrate mitigating information into their hostile intent attribution? An ERP study.
Bibliographic record
Abstract
Current data on the nature of aggressive individuals’ difficulties in reappraising their spontaneous hostile intent attribution are contradictory: they are impulsive and don’t seek out for additional nonhostile cues vs. they pay attention to nonhostile cues but fail to integrate them into their hostile schemas. To better understand the nature of aggressive people’s reappraisal difficulties, we developed an event-related brain potential (ERP) protocol inspired by Zaki’s (2013) cue integration model. The objective of this study was to track the neural activity associated with the violation of expectations about hostile vs. nonhostile intentions in aggressive and nonaggressive individuals when facing conflicting contextual and behavioral cues in a given social situation. We hypothesized that aggressive individuals do not integrate nonhostile contextual information and, therefore, overestimate the behavioral hostile cues. Our sample consisted of women from the community (n=23) and a prison (n=20). Taken together, the results suggest that aggressive individuals demonstrate an impulsivity in their decision-making about other people’s intentions. This would be the case, not because they fail to seek out mitigating information, but rather because they fail to complete the inferential processes about the hostile and nonhostile information before making a judgement about the other’s intent. In contrast with aggressive individuals, non-aggressive people would be able to make a decision when facing conflicting information about the other’s mental state by privileging contextual cues in order to attenuate their attribution of hostile intention based on the behavior of others.
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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 | 0.002 |
| 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) | 0.002 | 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".