The Effects of Sensory Feedback on Simulated Online Slot Machine Gambling
Bibliographic record
Abstract
Objective: A prominent feature of land-based and online slot machines is audiovisual feedback that accompanies winning outcomes. Prior laboratory work suggests that this design feature may influence game experience, facilitate immersion, increase arousal, and promote riskier decision making, but whether this generalizes to realistic gambling products remains unclear. In this pre-registered study, we used a realistic slot machine simulator, deployed online, to evaluate effects of win-accompanying sensory feedback (SF) on gambling experience and behavior. Method: Participants were recruited via Amazon Mechanical Turk and stratified as online active gamblers (AG) or non-gamblers (NG). Participants were randomly assigned to complete 200 spins on an online slot machine that featured either enhanced or diminished SF. Results: Enhanced SF reduced time to initiate spins (i.e. faster speed of play) but did not affect bet size or self-reported experience. Relative to NG, AG reported greater game immersion and positive affect during the game, and these variables were also predicted across all participants by greater problem gambling severity, and monetary gains in the gambling session. In addition, self-reported immersion and affect were predicted by ADHD and depressive symptoms. Conclusion: Faster gambling under SF may incur greater losses over time, raising concerns about harm potential. By contrast, game experience was influenced by monetary outcomes and personal characteristics rather than SF. Overall, these findings support the notion that both product and personal characteristics confer risk of harm.
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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 | 0.003 |
| 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) | 0.004 | 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".