A General Model of Cognitive Bias in Human Judgment and Systematic Review Specific to Forensic Mental Health
Bibliographic record
Abstract
Objectives: Cognitive biases can impact experts’ judgments and decisions. We offer a broad descriptive model of how bias affects human judgment. And although studies have explored the role of cognitive biases and debiasing techniques in forensic mental health, we conducted the first systematic review to identify, evaluate, and summarize the findings.Hypotheses: Due to the exploratory nature of this review, we did not test formal hypotheses. General research questions included the proportion of studies focusing on cognitive biases and/or debiasing, what research methods were applied, the cognitive biases and debiasing strategies empirically studied in the forensic context, how they affect forensic mental health decisions, and effect sizes.Method: A systematic search of PsycINFO and Google Scholar resulted in 22 records comprising 23 studies in the United States, Canada, Finland, Italy, the Netherlands, and the United Kingdom. We extracted data on participants, context, methods, and results.Results: Most studies focused only on cognitive biases (n=16, 69.6%), with fewer investigating ways to address them (n=7, 30.4%). Of the 17 studies that tested for biases, most found significant effects (n=10, 58.8%), four found partial effects (23.5%), and three found no effects (17.6%). Foci included general perceptions of biases, adversarial allegiance, bias blind spot, hindsight and confirmation biases, moral disengagement, primacy and recency effects, interview suggestibility, and cross-cultural, racial, and gender biases. Of the seven debiasing-related studies, nearly all (n=6) focused at least in part on the general perception of debiasing strategies, with three testing for specific effects (i.e., cognitive bias training, consider-the-opposite, and introspection caution), the first two of which yielded significant effects.Conclusions: Considerable clinical and methodological heterogeneity limited quantitative comparability. Future research could build on the existing literature to develop or adapt effective debiasing strategies in collaboration with practitioners to improve the quality of forensic mental health decisions.Public Significance Statement: Evidence of bias in forensic mental health emerged in ways consistent with what we know about human judgment broadly. We know less about how to debias judgments–an important frontier for future research. Better understanding how bias works and developing effective debiasing strategies tailored to the forensic mental health context holds promise for improving quality. Until then, we can use what we know now toward limiting bias in our work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".