Losses disguised as wins in modern multi‐line video slot machines
Bibliographic record
Abstract
AIMS: Players can wager on multiple lines of modern slot machines. When they spin and fail to gain any credits, the machine goes into a state of relative quiet. By contrast, when they spin and win, these spins are accompanied by reinforcing sights and sounds. Such reinforcement also occurs when the amount won is less than the spin wager. We sought to show that these 'losses disguised as wins', or LDWs, would be as arousing as wins, and more arousing than regular losses. MEASUREMENT AND PARTICIPANTS: We measured skin conductance response (SCR) amplitudes and heart-rate changes following wins, LDWs and losses for 40 novices playing a multi-line slot machine. FINDINGS: SCR amplitudes were similar for wins and LDWs-both were significantly larger than for regular losses. CONCLUSIONS: For novice players, the reinforcing sights and sounds of the slot machine triggered arousal on wins, where the number of credits gained was greater than the spin wager, but also on 'losses disguised as wins' where the amount 'won' was less than the spin wager. Despite the fact that players lost money on these spins, these outcomes were more arousing than regular losses where no credits were gained. Although these findings involve novice players, the heightened arousal associated with these losses may have implications for the development of problem gambling, as arousal has been viewed as a key reinforcer in gambling behaviour.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".