The Near-Miss Effect in Online Slot Machine Gambling: a Series of Conceptual Replications
Bibliographic record
Abstract
Objective: Near-misses are a structural characteristic of gambling products that can be engineered within modern digital games. Over a series of pre-registered experiments using an online slot machine simulation, we investigated the impact of near-miss outcomes, on subjective ratings (motivation, valence) and two behavioural measures (speed of gambling, bet size).Method: Participants were recruited using Prolific and gambled on an online 3-reel slot machine simulator that delivered a 1 in 3 rate of X-X-O near-misses. Study 1 measured trial-by-trial subjective ratings of valence and motivation (Study 1a, n = 169; Study 1b, n = 148). Study 2 measured spin initiation latencies (n = 170) as a function of the previous trial outcome. Study 3 measured bet size (n = 172) as a function of the previous trial outcome.Results: In Study 1a, near-misses increased the motivation to continue gambling relative to full-misses, supporting H1. On valence ratings, near-misses were rated significantly more positively from full-misses, in the opposite direction to H2; this effect was confirmed in a close replication (Study 1b). In Study 2, participants gambled faster following near-misses relative to full-misses, supporting H3. In Study 3, participants significantly increased their bet size following near-misses relative to full-misses, supporting H4.Conclusion: Across all dependent variables, near-miss outcomes yielded statistically significant differences from objectively-equivalent full-miss outcomes, corroborating the ‘near miss effect’ across both subjective and behavioral measures, and in the environment of online gambling. The unexpected findings on valence ratings are considered in terms of boundary conditions for the near-miss effect, and competing theoretical accounts based on frustration/regret, goal generalization, and skill acquisition.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".