An ERP study on hostile attribution bias in aggressive and nonaggressive individuals
Bibliographic record
Abstract
Hostile attribution bias (e.g., tendency to interpret the intention of others as hostile in ambiguous social contexts) has been associated with impulsive aggression in adults, but the results are mixed and the complete sequence of hostile inferential processes leading to aggression has not been investigated yet. The goal of this event-related brain potentials (ERPs) study was to track the neural activity associated with the violation of expectations about hostile versus nonhostile intentions in aggressive and nonaggressive individuals and examine how this neural activity relates to self-reported hostile attributional bias and impulsive aggression in real life. To this end, scenarios with a hostile versus nonhostile social context followed by a character's ambiguous aversive behavior were presented to readers, and ERPs to critical words that specified the hostile versus nonhostile intent behind the behavior were analysed. Thirty-seven aggressive and fifty nonaggressive individuals participated in the study. The presentation of a critical word that violated hostile expectation caused an N400 response that was significantly larger in aggressive than nonaggressive individuals. Results also showed an enhanced late positive potential-like component in aggressive individuals when hostile intention scenarios took place in a nonhostile context, which is associated with impulsive aggression in real life even after having controlled for the effect of self-reported hostile attributional bias. The Hostile Expectancy Violation paradigm evaluated in this study represents a promising tool to investigate the relationship between the online processing of hostile intent in others and impulsive aggression. Aggr. Behav. 43:217-229, 2017. © 2016 Wiley Periodicals, Inc.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".