Technological advances in the assessment of impulse control in offenders: A systematic review
Bibliographic record
Abstract
Deficits in impulse control have been linked to criminal offending, risk of recidivism, and other maladaptive behaviours relevant to the criminal justice system (e.g. substance use). Impulse control can be conceptualized as encompassing the broad domains of response inhibition and impulsive/risky decision-making. Advancements in technology have led to the development of computerized behavioural measures to assess performance in these domains, such as go/no-go and delay discounting tasks. Despite a relatively large literature examining these tasks in offenders, findings are not universally consistent. This systematic review aims to synthesize the literature using computerized neurocognitive tasks to assess two domains of impulse control in offenders: response inhibition and impulsive/risky decision-making. The review included 28 studies from diverse geographic locations, settings, and offender populations. The results largely support the general conclusion that offenders exhibit deficits in impulse control compared with non-offenders, with studies of response inhibition more consistently reporting differences than studies using impulsive and risky decision-making tasks. Findings are discussed in the context of contemporary neuroimaging research emphasizing dysfunction in prefrontal cortex as a key contributor to impulse control deficits in offenders.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".