Functional Mechanisms of Episodic Memory Impairment in Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To achieve a better understanding of the functional mechanisms underlying episodic memory dysfunction in schizophrenia, which is a prerequisite for unravelling schizophrenia's neural correlates in neuroimaging studies and, more generally, for developing an integrated approach to the pathophysiology of schizophrenia. It is also crucial for developing cognitive remediation. METHOD: This paper reviews empirical evidence of episodic memory dysfunction in schizophrenia obtained with reference to various theoretical models of episodic memory. RESULTS: All the studies converge to show a significant impairment of the critical feature of episodic memory: conscious recollection. Schizophrenia is also associated with a defect of autobiographical memory. The episodic memory dysfunction results from a predominant failure of strategic processing at encoding, although an impairment of strategic processing at retrieval cannot be ruled out. The possibility that it is not the execution of the encoding strategies that is defective but, rather, their self-initiation by the patients is plausible. CONCLUSIONS: These findings may explain some behavioural abnormalities associated with schizophrenia, notably, inadequate functional outcomes in everyday life. They may also have implications for cognitive remediation and better social and work functioning of patients with schizophrenia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".