Reasoning and interpretation cognitive biases related to psychotic characteristics: An umbrella-review
Bibliographic record
Abstract
Cognitive biases have been studied in relation to schizophrenia and psychosis for over 50 years. Yet, the quality of the evidence linking cognitive biases and psychosis is not entirely clear. This umbrella-review examines the quality of the evidence and summarizes the effect sizes of the reasoning and interpretation cognitive biases studied in relation to psychotic characteristics (psychotic disorders, psychotic symptoms, psychotic-like experiences or psychosis risk). It also examines the evidence and the effects of psychological interventions for psychosis on cognitive biases. A systematic review of the literature was performed using the PRISMA guidelines and the GRADE system for 128 analyses extracted from 16 meta-analyses. Moderate to high-quality evidence with medium to large effect sizes were found for the following interpretation biases: externalization of cognitive events and self-serving bias, when people with psychotic symptoms were compared to control conditions. Regarding reasoning biases, moderate to high quality evidence with medium to large effect sizes were found for belief inflexibility when linked to delusion conviction and global severity in people with active delusions, although measures from the MADS, overlapping with symptoms, may have inflated effect sizes. Moderate quality evidence with medium to large effect sizes were found for jumping to conclusion biases when clinical samples with psychosis were compared to controls, when using data-gathering tasks. Other cognitive biases are not supported by quality evidence (e.g., personalizing bias, belief about disconfirmatory evidence), and certain measures (i.e., IPSAQ and ASQ) systematically found no effect or small effects. Psychological interventions (e.g., MCT) showed small effect sizes on cognitive biases, with moderate-high-quality evidence. This umbrella review brings a critical regard on the reasoning and interpretation biases and psychotic symptoms literature-although most biases linked to psychotic symptoms are supported by meta-analyses in some way, some have only demonstrated support with a specific population group (e.g., aberrant salience and hostility attribution in healthy individuals with psychotic-like experiences), whereas other biases are currently insufficiently supported by quality evidence. Future quality studies, particularly with clinical populations with psychotic symptoms, are still warranted to ascertain the psychosis-cognitive bias link for specific biases.
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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 | 0.034 | 0.117 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".