Trivially informative semantic context inflates people's confidence they can perform a highly complex skill
Bibliographic record
Abstract
Some research suggests people are overconfident because of personality characteristics, lack of insight, or because overconfidence is beneficial in its own right. But other research fits with the possibility that fluent experience in the moment can rapidly drive overconfidence. For example, fluency can push people to become overconfident in their ability to throw a dart, know how rainbows form or predict the future value of a commodity. But surely there are limits to overconfidence. That is, even in the face of fluency manipulations known to increase feelings of confidence, reasonable people would reject the thought that they, for example, might be able to land a plane in an emergency. To address this question, we conducted two experiments comprising a total of 780 people. We asked some people (but not others) to watch a trivially informative video of a pilot landing a plane before they rated their confidence in their own ability to land a plane. We found watching the video inflated people's confidence that they could land a plane. Our findings extend prior work by suggesting that increased semantic context creates illusions not just of prior experience or understanding-but also of the ability to actually do something implausible.
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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 | 0.003 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".