Bias in favour of self-selected hypotheses is associated with delusion severity in schizophrenia
Bibliographic record
Abstract
INTRODUCTION: Delusions are typically characterised by idiosyncratic, self-generated explanations used to interpret events, as opposed to the culturally normative interpretations. Thus, a bias in favour of one's own hypotheses may be a fundamental aspect of delusions. METHODS: We tested this possibility in the current study by comparing judgements of self-selected hypotheses to judgements of externally selected ones in a probabilistic reasoning task. This allowed us to equate self- and externally selected hypotheses in terms of objectively quantifiable supporting evidence. It is normal to be biased in favour of self-selected hypotheses, but we expected this bias to be exacerbated in schizophrenia patients relative to healthy and psychiatric controls, and to be correlated with the severity of delusions in the schizophrenia sample. RESULTS: As expected, all groups showed the self-selection bias. Although this bias was not increased in schizophrenia patients relative to the control groups, it was significantly correlated with the severity of delusions in the schizophrenia sample. CONCLUSIONS: These results fit with an account holding that the hypersalience of an individual's own interpretations of events, relative to culturally normative interpretations, may manifest in a self-selection bias, contributing to the delusional state in schizophrenia.
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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 | 0.002 | 0.017 |
| 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) | 0.002 | 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".