The metacognitive abilities of narcissists: Individual differences between grandiose and vulnerable subtypes
Bibliographic record
Abstract
Understanding individual differences in metacognitive ability is vital to gaining a better understanding of how we think about our own thinking. Past research has shown that individual differences in grandiose and vulnerable narcissism are related to overconfidence and self-reported metacognitive insight. Building off this work, we present results from an online study of 208 adults (recruited from the U.S. and Canada) examining the relations of trait grandiose and vulnerable narcissism with metacognitive variables related to memory and intelligence. Results indicate that while grandiose and vulnerable narcissism are similarly related to performance on tasks measuring recall, verbal intelligence, and numeracy, only grandiose narcissism was significantly related to metacognitive performance. Specifically, trait grandiose narcissism was positively associated with overconfidence (bias) for performance on memory and verbal intelligence tasks and negatively associated with the ability to discern correct from incorrect responses on the verbal intelligence task (i.e., discrimination index). Our findings suggest that the two types of narcissism differ not only dispositionally but metacognitively in important ways and provide a deeper understanding of the extent to which individual differences in personality attributes may be related to 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 | 0.003 |
| 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 | 0.001 |
| 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".