An Investigation of the Contribution of Cognitive Biases to Anger Outcomes and Reactive Aggression in Individuals with High Trait Anger
Bibliographic record
Abstract
High trait anger is an individual difference variable characterized by more frequent anger experiences and challenges with anger regulation. Difficulties with anger control are reported in a number of psychological conditions. However, the cognitive processes contributing to problematic anger are still poorly understood. According to the Integrative Cognitive Model of Anger and Reactive Aggression (ICM), three cognitive processes together contribute to the elicitation of anger and reactive aggression: hostile interpretation biases (HIB); cognitive control; and anger rumination. This dissertation comprises two independent studies that explored the role of two of the mechanisms proposed by the ICM (i.e., HIB and cognitive control) to anger and reactive aggression using cognitive bias modification (CBM) methodology. Both studies involved self-report and behavioural measures of cognitive biases at pre and post-training. Self-report measures were used to assess anger symptoms and reactive aggression, and all participants underwent a simulated anger provocation to evaluate their propensity for reactive aggression following training. In Study 1, a two-session interpretation bias modification program targeting hostile interpretation biases was implemented with a sample of university students reporting elevated trait anger (N = 47). Relative to the control condition, participants who underwent the training demonstrated significant increases in positive interpretation ratings and significant reductions in HIB at post-training. Contrary to hypotheses, these effects did not extend to implicit HIB ratings, attentional biases, or anger symptoms. Further, participants did not demonstrate reductions in reactive aggression. In Study 2, a two-session cognitive control training (CCT) program was delivered to a community-based sample of high trait anger adults (N = 50). At post-training, there were no significant differences between groups on any of the evaluated outcomes. Given the lack of change in hostility-primed cognitive control following the CCT training program, this study was unable to effectively evaluate the role of cognitive control as outlined in the ICM. Overall, the results of this dissertation do not provide a clear picture with respect to the validity of the ICM for high trait anger individuals.
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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 | 0.002 |
| 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".