Overconfidence across the psychosis continuum: a calibration approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it