Does the visual salience of credit card features affect choice?
Bibliographic record
Abstract
Abstract Regulators require lenders to display a subset of credit card features in summary tables before customers finalize a credit card choice. Some jurisdictions require some features to be displayed more prominently than others to help ensure that consumers are made aware of them. This approach could lead to untoward effects on choice, such that relevant but nonprominent product features do not factor in as significantly. To test this possibility, we instructed a random sample of 1615 adults to choose between two hypothetical credit cards whose features were shown side by side in tables. The sample was instructed to select the card that would result in the lowest financial charges, given a hypothetical scenario. Critically, we randomly varied whether the annual interest rates and fees were made visually salient by making one, both, or neither brighter than other features. The findings show that even among credit-savvy individuals, choice tends strongly toward the product that outperforms the other on a salient feature. As a result, we encourage regulators to consider not only whether a key feature should be made more salient, but also the guidelines regarding when a key feature should be displayed prominently during credit card acquisition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".