Motivational decision-making and violence in forensic psychiatric inpatients: A neurobiological perspective of aggression
Bibliographic record
Abstract
This study provides initial empirical support for a novel neurobiological decision-making model proposed by Nussbaum (2005), as it applies to an aggression typology (Nussbaum, Saint-Cyr Bell, 1997). The Iowa Gambling Task (IGT; Bechara, Damasio, Damasio and Anderson, 1994) was analyzed for forensic inpatients using the traditional method of scoring, and a novel method developed by Yechiam, Buserneyer, Stout and Bechara (2005) which provides scores for three separate aspects of decision-making (attentional, learning and response-choice consistency). The Predatory aggression group had the worst performance on the IGT based on traditional scoring, indicating poor decision-making skills in the face of immediately available motivational cues. Irritable and Delusional Defensive aggression groups, likely reflecting cognitive impairment, scored well below normal at around chance levels. Further, real-life decision-making, as manifested by individual criminal histories and institutional misbehaviour, was best predicted from IGT scores based on the final three blocks of the task.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".