Belief inflexibility in schizophrenia
Bibliographic record
Abstract
BACKGROUND: Previous studies using delusion-neutral material have demonstrated that patients with schizophrenia, particularly those with delusions, display a bias against disconfirmatory evidence (BADE). In the current study we investigated the moderating impact of belief strength on this effect. METHODS: Thirty-three schizophrenia patients, 18 patients with obsessive compulsive disorder, and 25 healthy control participants, were consecutively presented with delusion-neutral statements that provided increasingly detailed information about a scenario. They were asked to re-rate the plausibility of four descriptions of the scenario. The correct ("true") interpretation appeared poor on the first statement and then increasingly gained plausibility, whereas "lure" interpretations appeared plausible initially to varying degrees, but became implausible once all information was presented. RESULTS: Schizophrenia patients displayed a BADE for strong beliefs, in that they were biased against revising their ratings of lure items in light of new disconfirming evidence compared to the mixed control group. However, like controls, patients with schizophrenia were willing to revise weak beliefs. CONCLUSION: This confirms that schizophrenia patients are generally impaired in their ability to integrate disconfirmatory evidence, even for material that does not touch on delusional themes. This response pattern was more pronounced for strong beliefs, and this may contribute to the fixation of false ideas (i.e., delusions).
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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 | 0.003 | 0.024 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".