The Effect of Losses Disguised as Wins and Near Misses in Electronic Gaming Machines: A Systematic Review
Bibliographic record
Abstract
Near misses and losses disguised as wins have been of interest to gambling researchers and policymakers for many years (e.g., Griffiths in J Gambl Stud 9(2):101-120, 1993). This systematic literature review describes the behavioural, psychological, and psychobiological effects of near misses and losses disguised as wins (LDWs) in an effort to evaluate their precise influence on the player and to highlight areas requiring further investigation. A systematic search for relevant studies was conducted using Scopus, PubMed, PsycINFO, ProQuest Sociology databases, and the Gambling Research Exchange Ontario Knowledge Repository. A total of 51 (from an initial pool of 802) experimental peer-reviewed studies using human participants were found between 1991 and 2015. The systematic review revealed that near misses motivate continued play, but have varying effects on the emotional state or betting behaviour of the player. Near miss events were also shown to be associated with elevated skin conductance levels and diffuse activity across the brain, most consistently in areas processing reinforcement and reward. Re-examination of the studies of near misses events after classifying the type of game feedback suggested that the effectiveness of near misses is related to the phenomenology of a near miss itself rather than as a response to auditory or visual feedback provided by a slot machine. In contrast to near misses, the presence of LDWs was found to relate to an overestimation of how much a player is actually winning and was consistently viewed as an exciting event. The effect of LDWs appears to be driven by the presence of visuals and sounds most often associated with a true win. Practical implications and directions for future research are also discussed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".