Executive cognitive functioning and aggression: Is it an issue of impulsivity?
Bibliographic record
Abstract
Abstract A large body of research has documented a relation between the executive cognitive functions (ECFs) and interpersonal aggressive behavior. A predominant theory proposes that individuals with poor ECFs are more aggressive because they are unable to inhibit impulsive behaviors. However, evidence for this relationship is typically indirect. In this study, 46 healthy men and women completed measures of ECF, the Taylor Aggression Paradigm, and the Go/No‐Go discrimination task, a behavioral measure of impulsivity. Also, impulsiveness of participant responses during the aggression task was directly assessed by measuring latency of responses to provocation (“set‐time”). It was hypothesized that low‐quartile–scoring ECF men and women would perform more aggressively and more impulsively than high‐quartile peers. Consistent with expectations, results indicated that ECF was related to aggression and to impulsivity on the Go/No‐Go task. However, low‐ECF men and women did not have shorter set‐times; in fact, on this task, low‐ECF participants' behavioral decisions seemed slightly slower than those of high‐ECF participants. In light of these results, the authors speculate that a social information‐processing problem may mediate the ECF aggression relationship rather than altered impulsivity per se. Aggr. Behav. 29:15–30, 2003. © 2003 Wiley‐Liss, 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".