Memory and metamemory in schizophrenia: a liberal acceptance account of psychosis
Bibliographic record
Abstract
BACKGROUND: In previous studies we suggested that liberal acceptance (LA) represents a fundamental cognitive bias in schizophrenia and may explain why patients are more willing to accept weak response alternatives and display overconfidence in incorrect responses. The aim of the present study was to test a central assumption of the LA account: false alarms in schizophrenia should be particularly increased when the distractor-target resemblance is weak relative to a control group. METHOD: Sixty-eight schizophrenia patients were compared to 25 healthy controls on a visual memory task. At encoding, participants studied eight complex displays, each consisting of a unique pairing of four stimulus attributes: symbol, shape, position and colour. At recognition, studied items were presented along with distractors that resembled the targets to varying degrees (i.e. the match between distractors and targets ranged from one to three attributes). Participants were required to make old/new judgements graded for confidence. RESULTS: The hypotheses were confirmed: false recognition was increased for patients compared to controls for weakly and moderately related distractors only, whereas strong lure items induced similar levels of false recognition for both groups. In accordance with prior research, patients displayed a significantly reduced confidence gap and enhanced knowledge corruption compared to controls. Finally, higher neuroleptic dosage was related to a decreased number of high-confident ratings. CONCLUSIONS: These data assert that LA is a core mechanism contributing to both enhanced acceptance of weakly supported response alternatives and metamemory deficits, and this may be linked to the emergence of positive symptomatology.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".