Disguising Losses as Wins in Multi-line Video Slot Machines
Bibliographic record
Abstract
In contrast to traditional mechanical reel slot machines, on video slot machines one can wager on 15 lines or more per spin. When a player spins and wins feedback is provided by attention-grabbing, salient visual information. Certain symbols flash, counters tally the amount won, and the symbols accounting for the win are highlighted. When players spin and lose no feedback is given. When players wager on multiple lines, however, many of their “wins” are actually less than the spin wager. We refer to these outcomes as “losses disguised as wins” or LDWs. To assess the physiological consequences of wins, LDWs and losses we had players play a real multi-line slot machine. We used heart-rate deceleration to gauge the perceptual intake of the visual information following wins, LDWs, and losses and used skin conductance responses (SCRs) to measure player's arousal for these outcomes. For 39 players, average heart-rate decelerations were significantly greater following wins than either LDWs or losses. We interpret these deceleration effects in terms of perceptual intake of the presented reinforcers – there are simply more flashing, highlighted symbols following real wins than LDWs or losses. For arousal, the average SCRs of 37 players were equivalent for wins and LDWs – with both values being significantly larger than for losses. That is despite losing money on LDWs, participants' somatic reactions to LDWs were the same as when they won. Since arousal is arguably the most significant reinforcer in slot machine play, the finding that LDWs and real wins are equally arousing suggests that playing multiple lines on slot machines may heighten arousal and possibly increase the risk of developing gambling problems.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".