Is Overconfidence a Trait? An Adversarial Collaboration
Bibliographic record
Abstract
A fundamental underlying question about the nature of overconfidence has continued to be subject to scholarly dispute: Is overconfidence a genuine psychological trait? To advance this contested research topic, we engaged in an adversarial collaboration in which two research teams agreed upon a set of critical tests and preregistered their analyses and predictions prior to data collection. Our study ( N = 942; U.S. adults from CloudConnect) leverages a methodological innovation: To measure trait overconfidence absent task-related confounds, we developed a set of novel tasks in which performance is ostensibly random. When we assess confidence this way, we find robust relationships across tasks as measured by both confirmatory factor analyses and raw correlations. This indicates that some people do believe that they are able to perform relatively well on tasks even when there is little reason for that confidence. Our results support the claim that overconfidence might be a trait.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".