No evidence for a differential deficit of reality monitoring in schizophrenia: A meta-analysis of the associative memory literature
Bibliographic record
Abstract
INTRODUCTION: Patients with schizophrenia exhibit deficits in memory performance, particularly when required to bind together disparate items (associative memory). Yet the literature on associative memory is decidedly mixed, with some studies showing large deficits and other showing none. METHODS: The aims of this meta-analysis were to determine an overall effect size for the associative memory deficit in patients with schizophrenia and to examine two potential moderating variables related to this impairment: the nature of the memory being tested (pair vs. source recognition) and the inclusion or exclusion of novel items as part of the recognition test. RESULTS: We found that the mean effect sizes were large for pair recognition (r=.50) and medium for source recognition (r=.29), with a significant difference between the two recognition types. Contrary to a priori hypotheses, there were no differences in the effect sizes across the various types of source memory (i.e., internal, external, or reality monitoring). There was, however, a significant difference in the effect sizes between those studies that included novel items as part of the memory test (r=.26) and those that did not (r=.44). CONCLUSION: These findings suggest that the associative memory deficit in schizophrenia is not specific to self/other distinctions, but is rather a more global effect seen across testing conditions. In addition, memory tests that do not include new items appear to maximise this effect, perhaps by removing a potential response outlet for subjects who lack confidence in the accuracy of their memory performance.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".