Impaired Evidence Integration and Delusions in Schizophrenia
Bibliographic record
Abstract
A bias against disconfirmatory evidence (BADE) appears to be related to delusions in schizophrenia. However, preliminary studies have either not used the most comprehensive version of the BADE task, not included a psychiatric control group, and/or have used difference score methodology instead of analyzing all available measures. In the current study a comprehensive version of the BADE task was administered to people with schizophrenia, bipolar disorder and a healthy control group. The BADE task required rating four interpretations of delusion-neutral scenarios three times (in sequence) as increasingly disambiguating information was presented. A principal component analysis (PCA) carried out on all measures determined that two independent cognitive processes appear to combine to determine all responses on the BADE task: Integration of Evidence and Conservatism, with only the former discriminating between the severely delusional schizophrenia group and all other groups. Thus, integration of evidence appears to be functioning sub-optimally in severely delusional schizophrenia patients, resulting in a bias against disconfirmatory evidence (BADE). The cognitive process theorized to be underlying this effect is hypersalience of evidence-hypothesis matches.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".