Incautious Reasoning as a Pathogenetic Factor for the Development of Psychotic Symptoms in Schizophrenia
Bibliographic record
Abstract
Previous studies indicate that schizophrenia patients draw decisions more hastily than controls. The aim of the present study was to obtain convergent evidence with a new paradigm, designed after the Who Wants to Be a Millionaire television game show. Thirty-two schizophrenia patients and 38 healthy subjects were administered 20 knowledge questions, along with 4 response alternatives. Participants were required to provide probability estimates for each alternative. Whenever a subject was confident that one of the alternatives was correct or was wrong, the subject was asked to indicate this via a decision or rejection rating. Thus, probability estimates and decisions were independently assessed, allowing determination of the point at which probability estimates translate into decisions. Patients and controls gave comparable probability estimates for all alternatives. However, patients committed more erroneous responses, owing to their making decisions in the face of low subjective probability ratings and rejecting alternatives despite rather high probability ratings. The results provide further evidence for the claim that schizophrenia patients make strong judgments based on little information. We propose that a lowered threshold for accepting alternatives provides a parsimonious explanation for the data-gathering bias reported in the literature.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".