When a nudge becomes invisible: How behavioral interventions prompt metacognitive miscalibration
Bibliographic record
Abstract
Nudges, subtle design changes in a choice environment, have become a popular approach to influencing consumer behavior. In the current research, we explore their potential unintended consequences. While nudges can be helpful tools, we show they can distort people’s perceptions of their own abilities. We present evidence from ten studies (N = 5,395) suggesting that consumers attribute the positive effects of nudges to themselves rather than to the nudge. Employing nudges like reminders, defaults, and decision aids, we find that consumers underestimate the extent to which their behaviors are influenced by external aids. This effect occurs because nudges create ambiguity, leading individuals to mistakenly attribute their improved outcomes to their own abilities rather than the nudge. Our findings contribute to a more nuanced understanding of the impact of nudges, highlighting their potential to shape self-perception in unintended ways.
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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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".