Measuring Gamblers’ Behaviour to Show That Negative Sounds Can Reveal the True Nature of Losses Disguised as Wins in Multiline Slot Machines
Bibliographic record
Abstract
Losses disguised as wins (LDWs) are slot machine outcomes where players gain fewer credits than they wager. Despite being losses, slot machines celebrate LDWs with positive sounds and animations, leading gamblers to respond to them as wins. It is unknown how manipulating the sound following LDWs may influence gamblers' behaviour. In Experiment 1, participants played two conditions on a realistic slot machine simulator: a (standard) positive sound condition (LDWs paired with positive sound, losses paired with silence), and a negative sound condition (LDWs and losses paired with negative sound). We measured participants' behavioural responses [post-reinforcement pauses (PRPs)], win estimates, and subjective experience. In the negative sound condition, participants behaviourally responded to LDWs in a more loss-like and less win-like fashion, as measured by PRPs. Win estimates were reduced, and subjective experience was significantly impacted, but only when the negative sound condition was played second. In Experiment 2, we employed a much more subtle manipulation, pairing only LDWs with negative sound, and observed similar effects. Through these two experiments, we show that pairing LDWs with negative sound is an effective way to modify players' responses to LDWs, causing them to respond to them more like the losses they are, rather than the wins they seem.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".