Using Icon Arrays to Communicate Gambling Information Reduces the Appeal of Scratch Card Games
Bibliographic record
Abstract
Past work has demonstrated that presenting statistical information in a foreground-background icon array can improve risk understanding, reduce decision-making biases, and decrease the salience of low-probability risks. In the present study, we assess whether presenting readily available gambling information within a foreground-background icon array influences individuals’ gambling-related judgments (e.g., their perceived likelihood of winning a prize). Across two experiments (N = 1,151), we find that using icon arrays to present gambling information reduces the appeal of scratch card games. That is, participants presented with gambling information in a foreground-background icon array, as opposed to a non-graphical numerical format, reported feeling less likely to win a prize, less excitement to play, and less urge to gamble on a scratch card game presented in a hypothetical gambling task. Overall, we conclude that presenting gambling information in an icon array format represents a simple yet promising tool for correcting gamblers’ often overly-optimistic perceptions and reducing the appeal of negative expected value scratch card games.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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".