The neurocognition of conduct disorder behaviors: specificity to physical aggression and theft after controlling for ADHD symptoms
Bibliographic record
Abstract
There is growing evidence that among the different conduct disorder (CD) behaviors, physical aggression, but not theft, links to low neurocognitive abilities. Specifically, physical aggression has consistently been found to be negatively related to neurocognitive abilities, whereas theft has been shown to be either positively or not related to neurocognition. The specificity of these links needs further examination because attention deficit hyperactivity disorder (ADHD) links to both physical aggression and neurocognitive variation. The development of self-reported physical aggression and theft, from age 11 to 17 years, was studied in a prospective at-risk male cohort via a dual process latent growth curve model. Seven neurocognitive tests at age 20 were regressed on the growth parameters of physical aggression and theft. The links between neurocognition and the growth parameters of physical aggression and theft were adjusted for ADHD symptoms at ages 11 and 15 (parent, child and teacher reports). Results indicated that verbal abilities were negatively related to physical aggression while they were positively associated with theft. However, inductive reasoning was negatively associated with increases in theft across adolescence. Symptoms of ADHD accounted for part of the neurocognitive test links with physical aggression but did not account for the associations with theft. These differences emphasize the importance of examining specific CD behaviors to better understand their neurodevelopmental mechanisms. They also suggest that youth who engage in different levels of physical aggression or theft behaviors may require different preventive and corrective interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".