Episodic Memory Bias and the Symptoms of Schizophrenia
Bibliographic record
Abstract
Much of the research on episodic memory in schizophrenia spectrum disorders has focused on memory deficits and how they relate to clinical measures such as outcome. Memory bias refers to the modulatory influence that state or trait psychopathology may exert on memory performance for specific categories of stimuli, often emotional in nature. For example, subjects suffering from depression frequently have better memory for negative stimuli than for neutral or positive ones. This dimension of memory function has received only scant attention in schizophrenia research but could provide fresh new insights into the relation between symptoms and neurocognition. This paper reviews the studies that have explored memory biases in individuals with schizophrenia. With respect to positive symptoms, we examine studies that have explored the link between persecutory delusions and memory bias for threatening information and between psychosis and a memory bias toward external source memory. Although relatively few studies have examined negative symptoms, we also review preliminary evidence indicating that flat affect and anhedonia may lead to some specific emotional memory biases. Finally, we present recent findings from our group delineating the relation between emotional valence for faces and memory bias toward novelty and familiarity, both in schizophrenia patients and in healthy control subjects. A better understanding of the biasing effects of psychopathology on memory in schizophrenia (but also on other cognitive functions, such as attention, attribution, and so forth) may provide a stronger association between positive and negative symptoms and memory function. Memory measures sensitive to such biases may turn out to be stronger predictors of clinical and functional outcome.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".