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 preregistered experiments using an online slot machine simulation, we investigated the impact of near-miss outcomes on subjective ratings (motivation, valence) and two behavioral measures (speed of gambling, bet size). METHOD: = 172) measured bet size 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 Hypothesis 1. On valence ratings, near-misses were rated significantly more positively than full-misses, in the opposite direction to Hypothesis 2; this effect was confirmed in a close replication (Study 1b). In Study 2, participants gambled faster following near-misses relative to full-misses, supporting Hypothesis 3. In Study 3, participants significantly increased their bet size following near-misses relative to full-misses, supporting Hypothesis 4. 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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".