Overconfidence across the psychosis continuum: a calibration approach
Bibliographic record
Abstract
INTRODUCTION: An 'overconfidence in errors' bias has been consistently observed in people with schizophrenia relative to healthy controls, however, the bias is seldom found to be associated with delusional ideation. Using a more precise confidence-accuracy calibration measure of overconfidence, the present study aimed to explore whether the overconfidence bias is greater in people with higher delusional ideation. METHODS: A sample of 25 participants with schizophrenia and 50 non-clinical controls (25 high- and 25 low-delusion-prone) completed 30 difficult trivia questions (accuracy <75%); 15 'half-scale' items required participants to indicate their level of confidence for accuracy, and the remaining 'confidence-range' items asked participants to provide lower/upper bounds in which they were 80% confident the true answer lay within. RESULTS: There was a trend towards higher overconfidence for half-scale items in the schizophrenia and high-delusion-prone groups, which reached statistical significance for confidence-range items. However, accuracy was particularly low in the two delusional groups and a significant negative correlation between clinical delusional scores and overconfidence was observed for half-scale items within the schizophrenia group. Evidence in support of an association between overconfidence and delusional ideation was therefore mixed. CONCLUSIONS: Inflated confidence-accuracy miscalibration for the two delusional groups may be better explained by their greater unawareness of their underperformance, rather than representing genuinely inflated overconfidence in errors.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".