Source monitoring and memory confidence in schizophrenia
Bibliographic record
Abstract
BACKGROUND: The present study attempted to extend previous research on source monitoring deficits in schizophrenia. We hypothesized that patients would show a bias to attribute self-generated words to an external source. Furthermore, it was expected that schizophrenic patients would be overconfident regarding false memory attributions. METHOD: Thirty schizophrenic and 21 healthy participants were instructed to provide a semantic association for 20 words. Subsequently, a list was read containing experimenter- and self-generated words as well as new words. The subject was required to identify each item as old/new, name the source. and state the degree of confidence for the source attribution. RESULTS: Schizophrenic patients displayed a significantly increased number of source attribution errors and were significantly more confident than controls that a false source attribution response was true. The latter bias was ameliorated by higher doses of neuroleptics. CONCLUSIONS: It is inferred that a core cognitive deficit underlying schizophrenia is a failure to distinguish false from true mnestic contents.
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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 | 0.000 |
| 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".