Losses disguised as wins in multiline slots: using an educational animation to reduce erroneous win overestimates
Bibliographic record
Abstract
Slot machines are available in several countries, with multiline games growing in popularity. Interestingly, many audiovisually reinforced small ‘wins’ in multiline games are in fact monetary losses – outcomes referred to as losses disguised as wins (LDWs). Research suggests that LDWs cause players to overestimate how many times they remember actually winning during a playing session. The study sought to replicate this finding and see if a short educational animation about LDWs could significantly reduce this LDW-triggered win overestimation effect. It employed a mixed design, with animation viewed (LDW, control) as the between-subjects factor, and game played (200 spins on a few LDW or many LDW game; game order counterbalanced) as the within-subjects factor. Fifty-four novice participants estimated how many times they won more than they wagered in each game. In the control animation group, the study replicated the LDW-triggered win overestimation effect for participants playing the many LDW game. Crucially, win overestimates were significantly reduced in this many LDW game for players exposed to the LDW animation. The study concludes that LDWs can lead novice gamblers to remember winning more often than they actually do during a playing session, but educating participants about LDWs can reduce these erroneous win overestimates.
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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 | 0.001 |
| 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) | 0.000 | 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".