Effective connectivity and criminal sentencing decisions: dynamic causal models in laypersons and legal experts
Bibliographic record
Abstract
This magnetic resonance imaging study is designed to obtain relevant implications for criminal justice and explores the effective connectivity underlying expertise. Laypersons and experts considered sentences for remorseful and remorseless defendants, respectively, with and without mitigation, in hypothetical murder cases. Two groups revealed no differential activation. However, dynamic causal modeling analysis found distinct patterns of connectivity associated with subjects' expertise and mitigating factors. In sentencing for remorseful defendants, laypersons showed increased strength in all bidirectional connections among activated regions of Brodmann area (BA) 32, BA23, the right posterior insula, and the precuneus. In contrast, legal experts sentenced based on mitigation reasoning, showed increased strength only in the bidirectional connection between the insula and the precuneus. When sentencing for remorseless ones without mitigation, both laypersons and experts increased the connection strength, but with reverse directionality, between regions; legal experts strengthened connectivity from BA10 to other regions, that is, the right anterior insula and BA23, but the directionality was reversed in laypersons. In addition, the strength of connection to BA32 and BA10 was correlated with changes in punishments by mitigating factors. This is a crucial result that establishes the validity of the connectivity estimates, which were uninformed by the independent (behavioral) differences in the severity of punishment.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".