Neuro-cognitive systems that, when dysfunctional, increase aggression risk and the potential for translation into clinical tools
Bibliographic record
Abstract
The goal of this narrative review paper is to consider forms of neurocognitive dysfunction that increase risk for reactive and instrumental aggression. Neuro-cognitive functions that appear to mediate, inhibit or moderate reactive and instrumental aggression are identified and data on the association between perturbations of these neuro-cognitive functions and aggression risk are considered. The neuro-cognitive functions considered are: the acute threat response, emotion regulation, reinforcement-based decision-making, response control, empathy (responsiveness to distress cues) and affiliation. Their functional roles, putative neural substrates and data indicating dysfunction in aggressive populations will be considered. Moreover, brief considerations will be given regarding the impact of early life stress (abuse and neglect) may have on their development. Finally, the current situation with respect to the potential utility of neuro-cognitive indices and how such neuro-cognitive systems might be assessed is considered. • Neuro-cognitive functions that inhibit or moderate reactive and instrumental aggression are identified and data include: the acute threat response, emotion regulation, reinforcement-based decision-making, response control, empathy (responsiveness to distress cues) and affiliation. • These neuro-cognitive functions have at least partially separable neural substrates. • Consideration of how these functions may be assessed clinically is given.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".