Cognitive neuroscience and criminal justice: The contribution of neuropsychology to the assessment of criminal responsibility
Bibliographic record
Abstract
The article explores the still underutilized potential of forensic neuropsychology in the assessment of criminal responsibility within Italian legal proceedings. Unlike the civil and medico-legal contexts, where neuropsychological testing is widely adopted, the penal system continues to rely almost exclusively on clinical interviews, which are highly subjective and show low inter-rater reliability. Through the use of neuropsychological tests, neuroimaging techniques, and genetic data, experts can obtain more accurate and reliable information regarding a defendant’s capacity to understand and will at the time of the offense. These capacities may be impaired by neurological or psychiatric conditions, potentially diminishing or even nullifying an individual’s accountability for the crime committed. Furthermore, the neuroscientific approach allows for more effective detection of malingering, thereby reducing the risk of judicial errors. The article concludes by advocating for a broader application of neuropsychology in Italian criminal proceedings, highlighting how its use – already well established in countries such as the United States and Canada – could enhance the accuracy of forensic evaluations and contribute to more equitable outcomes in criminal trials.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".