Toward a Model of Cognitive Insight in First-Episode Psychosis: Verbal Memory and Hippocampal Structure
Bibliographic record
Abstract
Our previous work has linked verbal learning and memory with cognitive insight, but not clinical insight, in individuals with a first-episode psychosis (FEP). The current study reassessed the neurocognitive basis of cognitive and clinical insight and explored their neural basis in 61 FEP patients. Cognitive insight was measured with the Beck Cognitive Insight Scale (BCIS) and clinical insight with the Scale to assess Unawareness of Mental Disorder (SUMD). Global measures for 7 domains of cognition were examined. Hippocampi were manually segmented in to 3 parts: the body, head, and tail. Verbal learning and memory significantly correlated with the BCIS composite index. Composite index scores were significantly associated with total left hippocampal (HC) volume; partial correlations, however, revealed that this relationship was attributable largely to verbal memory performance. The BCIS self-certainty subscale significantly and inversely correlated with bilateral HC volumes, and these associations were independent of verbal learning and memory performance. The BCIS self-reflectiveness subscale significantly correlated with verbal learning and memory but not with HC volume. No significant correlations emerged between the SUMD and verbal memory or HC volume. These results strengthen our previous assertion that in individuals with an FEP cognitive insight may rely on memory whereby current experiences are appraised based on previous ones. The HC may be a viable location among others for the brain system that underlies aspects of cognitive insight in individuals with an FEP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".