A two-stage cognitive theory of the positive symptoms of psychosis. Highlighting the role of lowered decision thresholds
Bibliographic record
Abstract
OBJECTIVES: We outline a two-stage heuristic account for the pathogenesis of the positive symptoms of psychosis. METHODS: A narrative review on the empirical evidence of the liberal acceptance (LA) account of positive symptoms is presented. HYPOTHESIS: At the heart of our theory is the idea that psychosis is characterized by a lowered decision threshold, which results in the premature acceptance of hypotheses that a nonpsychotic individual would reject. Once the hypothesis is judged as valid, counterevidence is not sought anymore due to a bias against disconfirmatory evidence as well as confirmation biases, consolidating the false hypothesis. As a result of LA, confidence in errors is enhanced relative to controls. Subjective probabilities are initially low for hypotheses in individuals with delusions, and delusional ideas at stage 1 (belief formation) are often fragile. In the course of the second stage (belief maintenance), fleeting delusional ideas evolve into fixed false beliefs, particularly if the delusional idea is congruent with the emotional state and provides "meaning". LA may also contribute to hallucinations through a misattribution of (partially) normal sensory phenomena. Interventions such as metacognitive training that aim to "plant the seeds of doubt" decrease positive symptoms by encouraging individuals to seek more information and to attenuate confidence. The effect of antipsychotic medication is explained by its doubt-inducing properties. LIMITATIONS: The model needs to be confirmed by longitudinal designs that allow an examination of causal relationships. Evidence is currently weak for hallucinations. CONCLUSIONS: The theory may account for positive symptoms in a subgroup of patients. Future directions are outlined.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".