Neurocognitive predictors of metacognition in individuals at clinical high risk for psychosis
Bibliographic record
Abstract
BACKGROUND: Metacognition refers to the ability to evaluate and control our cognitive processes. While studies have investigated metacognition in schizophrenia and clinical high risk for psychosis (CHR), less is known about the potential mechanisms which result in metacognitive deficits. AIMS: We aimed to investigate whether neurocognitive functions including attention, working memory, verbal learning and executive functions predicted the tendency to focus on one's thoughts (cognitive self-consciousness) and beliefs in the efficacy of one's cognitive skills (cognitive confidence). METHOD: Participants (130 CHR individuals) were recruited as part of the multi-site PREDICT study. They were assessed using the Metacognitions Questionnaire (MCQ) as well as measures of executive function (WCST), attention (N-Back), working memory (LNS) and verbal learning (AVLT). RESULTS: Cognitive competence was negatively correlated with N-Back while cognitive self-consciousness was positively correlated with N-Back and LNS. Linear regression analysis with N-Back, AVLT, LNS and WCST as predictors showed that neurocognition significantly predicted cognitive self-consciousness, with N-Back, LNS and WCST as significant predictors. The model accounted for 14% of the variance in cognitive self-consciousness. However, neurocognition did not result in a significant predictive model of cognitive competence. CONCLUSIONS: Neurocognition was associated with an increased focus on one's thoughts, but it was not associated with higher confidence in one's cognitive skills. Neurocognition accounted for less than one-sixth of the variance in metacognition, suggesting that interventions that target neurocognition are unlikely to improve metacognitive abilities.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".