Neurocognitive predictors of confabulation in Schizophrenia
Bibliographic record
Abstract
Confabulations, or false memories, are observed in various disorders, including schizophrenia. In forensic psychiatric assessment, this is problematic, particularly when garnering a clinical history and detailed account of the index offense(s) from the individual being charged. This study sought to quantitatively synthesize the existing literature regarding the frequency of confabulations in schizophrenia and its neurocognitive correlates. The keywords “schizophrenia” and “psychosis” were systematically canvassed in combination with “confabulation,” “false memory,” and “false memories” on PsycINFO, PubMed, and Scopus. Inclusion criteria included the following: (1) Participant samples that included the patients with schizophrenia and healthy controls; (2) commercially available neuropsychological test measures were employed (i.e., no experimental paradigms were considered); (3) quantitative data (i.e., means and standard deviations) was available so that an effect size could be computed; (4) published findings in peer-reviewed academic journals and written in English. Studies examining high-risk or first-episode psychosis groups were excluded. Five studies were included in the final analysis. Effect sizes in terms of Cohen’s d were calculated for number of confabulations made. When available, the correlations between confabulation and neurocognitive variables were recorded. The findings suggest that patients with schizophrenia confabulated more than healthy controls for new information if it was related to old information. The relationship between confabulations and neurocognitive variables was inconsistent. Together, the results from this quantitative review has important implications for interviewing techniques in forensic psychiatric assessment. Specifically, the assessor should take great care not to ask leading questions or introduce unverified, contextual information into the interview, as it may result in a confabulation, rather than a more accurate account of the event.
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| 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".