Verbal Memory Errors and Symptoms in Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To extend and test hypotheses linking positive and negative symptoms to selective aspects of verbal memory in schizophrenia. BACKGROUND: Verbal memory includes the ability to discriminate and prevent the intrusion of irrelevant information into recall and recognition. This ability has been proposed as a cognitive process that differentially mediates positive and negative symptoms. METHOD: Four error discrimination and 1 general recall memory index from the California Verbal Learning Test as well as general ability (IQ) and sex were used as predictors of symptom ratings in 55 schizophrenia patients within a regression framework. RESULTS: Intrusion errors during free recall contributed significantly to the prediction equation for negative symptoms (Brief Psychiatric Rating Scale). In contrast, positive symptoms and general psychopathology were not predicted by any of the discrimination indices. However, general recall memory (California Verbal Learning Test total words trials 1-5) contributed significantly to the prediction of general psychopathology and marginally to the prediction of negative symptoms. CONCLUSIONS: Impaired recall memory predicts levels of nonspecific psychopathology in schizophrenia. Negative symptoms associate with low intrusion error rates, but there is no evidence of an association between elevated errors and positive symptoms.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".