Using a computer simulation of three slot machines to investigate a gambler's preference among varying densities of near-miss alternatives
Open accessAbstract
The present article describes a software program in Visual Basic .NET designed to simulate three slot machines on a computer screen. This software is described in detail regarding utility, downloading, and usage; and data are presented illustrating the software's potential for researchers interested in gambling behavior. A simulation of multiple slot machines such as this enables researchers to evaluate players' preferences across various machines. In the highlighted experiment, 18 recreational slot machine players played the software for extra course credit and a chance at cash prizes. All participants played a version of the simulation in which every 5th response on average was a win, whereas the remaining trials were a loss. However, on those loss trials, a varying distribution of almost wins or near misses (i.e., two winning symbols on the payoff line and the final winning symbol directly above or below the payoff line) were presented in percentages of 15, 30, or 45. While no preferences across the three options could be predicted on the basis of reinforcement history alone, deviations from equal choices across the games were noted and appeared to be the result of the presentations of near-miss losing trials. Implications for a greater understanding of pathological gambling are presented.
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Reinforcement algorithms assign value to near-misses even when actual reinforcement is not given on such trials.
“This idea of the value of “near-misses” has been argued for in reinforcement algorithms that assign value to near-misses even when actual reinforcement is not given on such trials (MacLin et al., 2007).”
From Rethinking motor learning and savings in adaptation paradigms: model-free memory for successful actions combines with internal models (2011) · cites this paper as MacLin et al., 2007
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Cites
(1987) · American Journal of Psychiatry · doi:10.1176/ajp.144.9.1184 · Cited by 2,948 · see citing works
Shaffer, H. J., Hall, M. N., & Bilt, J. V. (1999) · American Journal of Public Health · doi:10.2105/ajph.89.9.1369 · Cited by 1,253 · see citing works
An Outcome Study of Gamblers Anonymous
Stewart, R. M., & Brown, R. I. F. (1988) · The British Journal of Psychiatry · doi:10.1192/bjp.152.2.284 · Cited by 151 · see citing works
Near-Miss Effects on Response Latencies and Win Estimations of Slot Machine Players
Dixon, M. R., & Schreiber, J. E. (2004) · The Psychological Record · doi:10.1007/bf03395477 · Cited by 102 · see citing works
Self-citationMadden, G. J. (2000) · Digital Commons - USU (Utah State University) · doi:10.4324/9781410605061 · Cited by 57 · see citing works
An evaluation of response allocations to concurrently available slot machine simulations
Dixon, M. R., MacLin, O. H., & Daugherty, D. (2006) · Behavior Research Methods · doi:10.3758/bf03192774 · Cited by 45 · see citing works
Self-citationA computerized slot machine simulation to investigate the variables involved in gambling behavior
MacLin, O. H., Dixon, M. R., & Hayes, L. J. (1999) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03200755 · Cited by 39 · see citing works
Self-citationGambling: Behavior, Theory, Research and Application
Whyte, K. S. (2007) · PsycEXTRA Dataset · doi:10.1037/e587072007-007 · Cited by 39 · see citing works
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Research works printed but not linked: 3
- Dixon, M. R., & Delaney, J. (2006). The impact of verbal behavior on gambling behavior. In P. M. Ghezzi, C. A. Lyons, M. R. Dixon, & G. R. Wilson (Eds.), Gambling: Behavior theory, research, and ap- plication (pp. 171-189). Reno, NV: Context Press.
- Ghezzi, P. M., Lyons, C. A., & Dixon, M. R. (2000). Gambling in socioeconomic perspective. In W. K. Bickel & R. E. Vuchinich (Eds.), Reframing health behavior change with behavioral economics (pp. 313-338). Mahwah, NJ: Erlbaum.
- MacLin, O. H., Dixon, M. R., Robinson, A., & Daugherty, D. (2006). Writing a simple slot machine simulation program. In P. M. Ghezzi, C. A. Lyons, M. R. Dixon, & G. R. Wilson (Eds.), Gambling: Be- havior theory, research, and application (pp. 127-154). Reno, NV: Context Press.
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Huang, V. S., Haith, A., Mazzoni, P., & Krakauer, J. W. (2011) · Neuron · doi:10.1016/j.neuron.2011.04.012 · Cited by 472 · see citing works
- Authors
- MacLin, O. H., Dixon, M. R., Daugherty, D., & Small, S. L.
- Affiliations
- University of Northern Iowa (US)
- Southern Illinois University Carbondale (US)
- Corresponding author
- Dixon, Mark R.
- ORCID
- Dixon, Mark R.
- Venue
- Behavior Research Methods
- Published
- 2007-05
- Volume
- 39
- Issue
- 2
- Pages
- 237-241
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W1992432206
- Cited by
- 65
- References
- 8
- Citations by year
- 2026: 1 · 2025: 4 · 2022: 2 · 2021: 2 · 2020: 4 · 2019: 4 · 2018: 1 · 2017: 7 · 2016: 5 · 2015: 5 · 2014: 9 · 2013: 4 · 2012: 5
- FWCI
- 2.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods (publishedVersion)
- PubMed (publishedVersion)
- UNI ScholarWorks (University of Northern Iowa) (submittedVersion)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Gambling Behavior and Treatments
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Keywords
- slot machines, gambling behavior, player preferences, reinforcement history, gambling addiction, computer simulation, gambling machines
- MeSH terms
- Adolescent, Adult, Choice Behavior, Computer Simulation, Extinction, Psychological, Gambling, Games, Experimental, Humans
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