Deficits in Reward Decision-Making on the Iowa Gambling Task in Justice-Involved Adults
Bibliographic record
Abstract
Deficits in reward decision-making are thought to contribute to criminal offending. These impairments have been measured in laboratory studies using the Iowa Gambling Task (IGT) which assesses implicit learning of different reward/punishment contingencies. This study compared IGT performance between a sample of justice-involved individuals and community-based individuals without an offending history. Participants included 100 adults from two Canadian federal correctional institutions (34% female, Mage = 39.14 ± 9.74) and a comparison group of 89 community adults with no history of offending (39% female, Mage = 37.04 ± 10.79). Responses on the IGT were analyzed for overall net score, learning across the task, and deck switching patterns. Associations between IGT performance and sentence characteristics and static factors assessment of recidivism risk were examined for the justice-involved group. The justice-involved group performed significantly worse than community adults in terms of net score. While the community group learned the advantageous strategy across the task, justice-involved participants exhibited minimal learning. This effect was moderated by recidivism risk within the justice-involved group, with individuals at low risk, but not medium/high risk, showing improvement over the blocks of the task. Finally, the justice-involved group also made greater use of an ineffective “win-stay/lose-shift” strategy. These results suggest that, compared with community participants without history of offending, incarcerated adults tend to employ maladaptive decision-making strategies that yield worse overall outcomes and the extent of impairment is associated with recidivism risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".