Jumping to conclusions in delusional and non‐delusional schizophrenic patients
Bibliographic record
Abstract
OBJECTIVE: Several studies have provided evidence for the claim that a subgroup of (schizophrenic) patients with current delusions share a jumping to conclusions (JTC) bias. The primary aim of the present study was to investigate whether currently deluded and non-deluded schizophrenic patients perform differently on three tasks tapping probabilistic reasoning. METHOD: Probabilistic reasoning was assessed in 31 schizophrenic patients, 28 psychiatric controls, and 17 healthy controls. In addition to the traditional draws to decision procedure, we employed two tasks for which participants had to judge, at each stage, the likelihood that beads come from either container (graded estimates procedure). Reaction times were recorded for the graded estimates procedure. RESULTS: A JTC bias was displayed by 42% of the schizophrenic patients in the draws to decision condition, while 7% of the psychiatric patients and none of the healthy controls reached a decision after only one bead. A similar pattern of results was observed for the graded estimates procedure. This bias was more pronounced in deluded schizophrenic patients, although currently non-deluded patients also showed evidence for earlier decisions. A bias to over-adjust when confronted with potentially disconfirmatory evidence was confined to deluded schizophrenic participants. There was also evidence for an increase in JTC in the deluded group over the course of the tasks. No substantial group differences occurred with respect to reaction time parameters indicating that results are not attributable to impulsivity. DISCUSSION: The findings provide further evidence for state and trait characteristics of abnormal reasoning in paranoid schizophrenia. Results are discussed in light of several competing explanations for JTC 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 | 0.001 | 0.005 |
| 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".