Overconfidently Underthinking: Narcissism negatively predicts cognitive reflection
Bibliographic record
Abstract
There exists a large body of work examining individual differences in the propensity to engage in reflective thinking processes. However, there is a distinct lack of empirical research examining the role of dispositional factors in these differences and understanding these associations could provide valuable insight into decision-making. Here we examine whether individual differences in cognitive reflection are related to narcissism (excessive self-focused attention) and impulsiveness (trait-based lack of inhibitory control). Participants across three studies completed measures of narcissism, impulsiveness and cognitive reflection. Results indicate that grandiose and vulnerable narcissists differ in their performance on problem-solving tasks (i.e., CRT) and preferences for intuitive thinking, as well as the degree to which they reflect on and understand their own thoughts and enjoy cognitively effortful activities. Additionally, though impulsiveness was significantly related to self-report measures of cognitive reflection (i.e., metacognitive reflection, metacognitive insight, and Need for Cognition), it showed no association with a behavioural measure of cognitive reflection (i.e., CRT scores). Our results suggest that certain individual differences in dispositional and personality characteristics may play important roles in the extent to which individuals engage in certain forms of reflective thinking.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".