Symptom-related attributional biases in schizophrenia and bipolar disorder
Bibliographic record
Abstract
INTRODUCTION: Biases in causal attributions and evidence integration have been implicated in delusions, but have not been investigated simultaneously to examine additive or multiplicative effects. It was hypothesised that paranoid delusions would correlate with self-serving and personalising biases ("defence" model of paranoia), particularly when these biases were disconfirmed. METHODS: Constrained principal component analysis was used to investigate differences between schizophrenia patients (paranoid vs. non-paranoid), bipolar disorder patients, and healthy controls, as well as to examine the extent to which psychotic symptoms could predict patterns of responding on a novel attributional bias task (Attributional Style BADE, or ASB) that requires integrating contextual information. RESULTS: Although no group differences were found, disorganisation and manic symptoms correlated with situation attributions and self-blame when such attributions were unsupported by the available evidence, and depression and anxiety correlated with other-person and self attributions (not situation attributions) when confirmed by the available evidence, regardless of diagnosis. CONCLUSIONS: While group differences accounted for little variance in responses on the ASB task, a transdiagnostic association between symptoms of psychosis and the ASB task was observed. This highlights the importance of considering symptom profiles rather than diagnostic groupings when investigating cognitive biases and related non-pharmacological treatments.
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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 | 0.001 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".