Different sides of the same coin? Intercorrelations of cognitive biases in schizophrenia
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: A number of cognitive biases have been associated with delusions in schizophrenia. It is yet unresolved whether these biases are independent or represent different sides of the same coin. METHODS: A total of 56 patients with schizophrenia underwent a comprehensive cognitive battery encompassing paradigms tapping cognitive biases with special relevance to schizophrenia (e.g., jumping to conclusions, bias against disconfirmatory evidence), motivational factors (self-esteem and need for closure), and neuropsychological parameters. Psychopathology was assessed using the Positive and Negative Syndrome Scale (PANSS). RESULTS: Core parameters of the cognitive bias instruments were submitted to a principal component analysis which yielded four independent components: jumping to conclusions, personalising attributional style, inflexibility, and low self-esteem. CONCLUSIONS: The study lends tentative support for the claim that candidate cognitive mechanisms for delusions only partially overlap, and thus encourage current approaches to target these biases independently via (meta)cognitive training.
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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 | 0.004 |
| 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 | 0.001 |
| 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