Decision making under uncertainty and mood induction: further evidence for liberal acceptance in schizophrenia
Bibliographic record
Abstract
BACKGROUND: Cognitive biases, especially jumping to conclusions (JTC), are ascribed a vital role in the pathogenesis of schizophrenia. This study set out to explore motivational factors for JTC using a newly developed paradigm. METHOD: Twenty-seven schizophrenia patients and 32 healthy controls were shown 15 classical paintings, divided into three blocks. Four alternative titles (one correct and three lure titles) had to be appraised according to plausibility (0-10). Optionally, participants could decide for one option and reject one or more alternatives. In random order across blocks, anxiety-evoking music, happy music or no music was played in the background. RESULTS: Patients with schizophrenia, particularly those with delusions, made more decisions than healthy subjects. In line with the liberal acceptance (LA) account of schizophrenia, the decision threshold was significantly lowered in patients relative to controls. Patients were also more prone than healthy controls to making a decision when the distance between the first and second best alternative was close. Furthermore, implausible alternatives were judged as significantly more plausible by patients. Anxiety-evoking music resulted in more decisions in currently deluded patients relative to non-deluded patients and healthy controls. CONCLUSIONS: The results confirm predictions derived from the LA account and assert that schizophrenia patients decide hastily under conditions of continued uncertainty. The fact that mood induction did not exert an overall effect could be due to the explicit nature of the manipulation, which might have evoked strategies to counteract their influence.
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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 | 0.011 |
| 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) | 0.003 | 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".