Different sides of the same coin? Intercorrelations of cognitive biases in schizophrenia
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".