Confidence in Errors as a Possible Basis for Delusions in Schizophrenia
Bibliographic record
Abstract
In two previous studies, it was observed that schizophrenic patients display increased confidence in memory errors compared with controls. The patient group displayed an increased proportion of errors in their knowledge system, quantified as the percentage of high-confident responses that are errors. The latter phenomenon has been termed knowledge corruption and is put forward as a risk factor for the emergence of delusions. In the present study, knowledge corruption was analyzed separately for different aspects of memory errors. A source-monitoring task was used, for which participants (30 schizophrenic patients with past or current paranoid ideas and 15 healthy controls) were asked to provide associates for each of 20 prime words. Later, participants were required to recognize studied words among distractor words, judge the original source, and provide a confidence rating for the most recent decision. Schizophrenic patients displayed greater confidence in memory errors compared with controls. Knowledge corruption was observed to be significantly greater in schizophrenic patients relative to controls for false-positive and false-negative judgments. It is proposed that reliance on false knowledge represents a candidate mechanism for the emergence of fixed false beliefs (i.e., delusions).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".