Cognitive biases in individuals with psychotic-like experiences: A systematic review and a meta-analysis
Bibliographic record
Abstract
A prior meta-analyze using behavioral tasks demonstrated that individuals with subclinical delusional ideations jump to conclusion (JTC). The major aim of our systematic review and meta-analyses was to highlight the relationship between cognitive biases and psychotic-like experiences (PLEs) when both are assessed by self-reports measures. In accordance with PRISMA guidelines, four electronic databases were searched. A total of 669 studies were identified, 39 articles met inclusion criteria for the systematic review and 27 for the random effects meta-analysis on healthy and UHR samples investigating cognitive biases (JTC, aberrant salience (ASB), attention to threat (ATB), externalizing bias (ETB), belief inflexibility (BIB), personalizing bias, aggression bias and need for closure). Effect size estimates were calculated using Pearson's correlation coefficients (r). In samples including both healthy and Ultra High Risk (UHR) individuals, positive psychotic-like experiences (PPLEs) were positively associated with ATB (rs = 0.38), ETB (rs = 0.35), BIB (rs = 0.19), JTC (rs = 0.10), and personalizing (rs = 0.24). In community samples, PPLEs were positively associated with ASB (rs = 0.62), ATB (rs = 0.34), ETB (rs = 0.36), BIB (rs = 0.18), JTC (rs = 0.11). In addition, negative PLEs were positively associated with ATB (rs = 0.28), ETB (rs = 0.37), BIB (rs = 0.19) and ASB (rs = 0.18). In UHR samples, positive associations were established between PPLEs and ATB (rs = 0.47), ETB (rs = 0.34), personalizing (rs = 0.36) and the aggression bias (rs = 0.35). Our results support cognitive models of psychosis considering the role of cognitive biases in the onset and the maintenance of psychotic symptoms. Cognitive interventions targeting cognitive biases could potentially prevent transition to psychosis in youth reporting PLEs and in UHR.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".