Under what circumstances do patients with schizophrenia jump to conclusions? A liberal acceptance account
Bibliographic record
Abstract
BACKGROUND: A consistent body of studies suggests that schizophrenia patients are extremely hasty when making decisions, and generally opt for the strongest response alternative. This pattern of results is primarily based on studies conducted with the beads task, which requires participants to determine from which of two possible jars a series of beads has been drawn. We have recently proposed a liberal acceptance (LA) bias to account for decision-making biases in schizophrenia, which claims that under heightened ambiguity the jump to conclusions (JTC) bias is abolished in schizophrenia. METHODS: A total of 37 schizophrenia patients were compared with 37 healthy controls on different versions of the beads paradigm. For the first task, participants were required to rate the probability that a bead was being drawn from one of two jars, and had to evaluate after each bead whether the amount of presented information would justify a decision. The second task was a classical draws to decision experiment with two jars. The third task confronted participants with four possible jars. If JTC was ubiquitous in schizophrenia hasty convergence on one alternative would be predicted for all three tasks. In contrast, the LA account predicts an abolishment of the JTC effect in the final task. RESULTS: Tasks 1 and 2 provide further evidence for the well-replicated JTC pattern in schizophrenia patients. In accordance with the LA hypothesis, no group differences were detected for task 3. DISCUSSION: The present results confirm that JTC is not ubiquitous in schizophrenia: in line with the LA account a JTC bias in schizophrenia occurred under low but not high ambiguity. LA may partly explain the emergence of fixed, false beliefs.
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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 | 0.003 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".