The contribution of hypersalience to the “jumping to conclusions” bias associated with delusions in schizophrenia
Bibliographic record
Abstract
BACKGROUND: Previous schizophrenia research involving the "beads task" has suggested an association between delusions and 2 reasoning biases: (1) "jumping to conclusions" (JTC), whereby early, resolute decisions are formed on the basis of little evidence and (2) over-adjustment of probability estimates following a single instance of disconfirmatory evidence. In the current study, we used a novel JTC-style paradigm to provide new information about a cognitive operation common to these 2 reasoning biases. METHODS: Using a task that required participants to rate the likelihood that a fisherman was catching a series of black or white fish from Lake A and not Lake B, and vice versa, we compared the responses of 4 groups (healthy, bipolar, nondelusional schizophrenia and delusional schizophrenia) when we manipulated 2 elements of the Bayesian formula: incoming data and prior odds. RESULTS: Regardless of our manipulations of the Bayesian formula, the delusional schizophrenia group gave significantly higher likelihood ratings for the lake that best matched the colour of the presented fish, but the ratings for the nonmatching lake did not differ from the other groups. LIMITATIONS: The limitations of this study include a small sample size for the group of severely delusional patients and a preponderance of men in the schizophrenia sample. CONCLUSION: Delusions in schizophrenia are associated with hypersalience of evidence-hypothesis matches but normal salience of nonmatches. When the colour of the incoming data is uniform (fish of only one colour), this manifests as JTC early in a series, and when the colour of incoming data varies (both black and white fish), this manifests as an overadjustment midseries. This account can provide a unifying explanation for delusion-associated performance patterns previously observed in the beads task 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 | 0.010 |
| 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) | 0.001 | 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".