Bridging Neuropsychology and Forensic Psychology: Executive Function Overlaps With the Central Eight Risk and Need Factors
Bibliographic record
Abstract
Recent research expanded theoretical frameworks of criminality to include biosocial perspectives. This article advances the biosocial integration into traditional criminological theories by focusing on the potential contribution of executive function (EF) to Andrews and Bonta's risk-need-responsivity (RNR) model. EF encompasses a collection of abilities critical to adaptive human functioning, many of which seem to underlie criminogenic risk and need factors. Although the assessment of EF can be elusive, research suggests that offenders with antisocial personality disorder (ASPD) experience EF deficits. Theoretical analysis on neuropsychological and forensic concepts suggests that unitary and discrete EF domains underlie the "Central Eight" criminogenic factors that are related to criminal behavior and, by extension, the RNR model of forensic assessment and treatment. Research and conceptual limitations of the current neuropsychological and forensic literature are discussed along with the limits of our theoretical analysis. A call for more theoretical and applied forensic neuropsychological research is presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".