The allure of multi‐line games in modern slot machines
Bibliographic record
Abstract
AIMS: In multi-line slot machines, players can wager on more than one line per spin. We sought to show that players preferred multi-line over single-line games, and that certain game features could cause multi-line game play to feel more rewarding. MEASUREMENT AND PARTICIPANTS: Reward was measured using post-reinforcement pauses (PRPs) following each outcome (the time between outcome delivery and the next spin). Gamblers (n = 102) played 250 spins on a 20-line game and 250 spins on a one-line game (answering questions about game experiences following each session). Playing one-line, a small credit gain (e.g. 2 cents) was a net win. In the 20-line game it was a net loss of 18 credits but was still accompanied by 'winning' sights and sounds. RESULTS: Most players (94%) preferred the 20-line game. PRPs for small credit gains (net losses) in the 20-line game were equivalent, or larger than in the one-line game where such gains were wins. The largest increase in PRP size was between the 0 and 2 credit conditions for both games. Thus 20-line players reacted as though these net losses of 18 credits were rewarding. Players' estimates of the number of true wins were accurate in the one-line game, but they significantly over-estimated the number of true wins in the 20-line game (P < 0.01). Problem gamblers felt more skilful (P < 0.01) and were more absorbed (P < 0.01) by multi-line than single-line game play. CONCLUSIONS: Multi-line games appear to be more appealing to gaming machine ('slots') players than single-line games. These games may be particularly absorbing for those with gambling problems.
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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 | 0.000 |
| 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".