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 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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".