Metacognitive knowledge and experience across multiple cognitive domains in euthymic bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Metacognitive knowledge (MK; general awareness of cognitive functioning) and metacognitive experience (ME; awareness of cognitive performance on a specific cognitive task) represent two facets of metacognition that are critical for daily functioning, but are understudied in bipolar disorder. This study was conducted to evaluate MK and ME across multiple cognitive domains in individuals diagnosed with bipolar disorder and unaffected volunteers, and to investigate the association between metacognition and quality of life (QoL). METHODS: Fifty-seven euthymic participants with bipolar disorder and 55 demographically similar unaffected volunteers provided prediction and postdiction ratings of cognitive task performance across multiple cognitive domains. Self-ratings were compared to objective task performance, and indices of MK and ME accuracy were generated and compared between groups. Participants rated QoL on the Quality of Life in Bipolar Disorder Scale (QoL.BD). RESULTS: Metacognitive inaccuracies in both MK and ME were observed in participants with bipolar disorder, but only in select cognitive domains. Furthermore, most metacognitive inaccuracies involved underestimation of cognitive ability. Metacognitive indices were minimally associated with medication variables and mood symptoms, but several indices were related to QoL. CONCLUSIONS: Individuals with bipolar disorder demonstrate inaccuracies in rating their cognitive functioning and in rating their online cognitive task performance, but only on select cognitive functions. The tendency to underestimate performance may reflect a negative information processing bias characteristic of mood disorders. Metacognitive variables were also predictive of QoL, indicating that further understanding of cognitive self-appraisals in persons with bipolar disorder has significant clinical relevance.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".