Skip to main content

Predictors of Return Rate Discrimination in Slot Machine Play

Journal of Gambling Studies volume 30, pages 669–683 (2014)Cite this article

Abstract

The purpose of this study was to investigate the extent to which accurate estimates of payback percentages and volatility combined with prior learning, enabled players to successfully discriminate between multi-line/multi-credit slot machines that provided differing rates of reinforcement. The aim was to determine if the capacity to discriminate structural characteristics of gaming machines influenced player choices in selecting ‘favourite’ slot machines. Slot machine gambling history, gambling beliefs and knowledge, impulsivity, illusions of control, and problem solving style were assessed in a sample of 48 first year undergraduate psychology students. Participants were subsequently exposed to a choice paradigm where they could freely select to play either of two concurrently presented PC-simulated slot machines programmed to randomly differ in expected player return rates (payback percentage) and win frequency (volatility). Results suggest that prior learning and cognitions (particularly gambler’s fallacy) but not payback, were major contributors to the ability of a player to discriminate volatility between slot machines. Participants displayed a general tendency to discriminate payback, but counter-intuitively placed more bets on the slot machine with lower payback percentage rates.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2

References

  • Abbott, M. W. (2001). Problem and non-problem gamblers in New Zealand: A report on phase two of the 1999 national prevalence survey. Report number six of the New Zealand gaming survey. Wellington: Department of Internal Affairs.

    Google Scholar 

  • Blaszczynski, A., & Nower, L. (2002). A pathways model of problem and pathological gambling. Addiction, 97, 487–499.

    PubMed  Article  Google Scholar 

  • Brandt, A. E., & Pietras, C. J. (2008). Gambling on a simulated slot machine under conditions of repeated play. The Psychological Record, 58, 405–426.

    Google Scholar 

  • Chóliz, M. (2010). Experimental analysis of the game in pathological gamblers: Effect of the immediacy of the reward in slot machines. Journal of Gambling Studies, 26, 249–256.

    PubMed  Article  Google Scholar 

  • Cokely, E. T., & Kelley, C. M. (2009). Cognitive abilities and superior decision making under risk: A protocol analysis and process model evaluation. Judgment and Decision Making, 4, 20–33.

    Google Scholar 

  • Cyders, M. A., & Smith, G. T. (2008). Clarifying the role of personality dispositions in risk for increased gambling behavior. Personality and Individual Differences, 45, 503–508.

    Google Scholar 

  • Cyders, M. A., Smith, G. T., Spillane, N. S., Fischer, S., Annus, A. M., & Peterson, C. (2007). Integration of impulsivity and positive mood to predict risky behavior: Development and validation of a measure of positive urgency. Psychological Assessment, 19, 107–118.

    Google Scholar 

  • Delfabbro, P. (2004). The stubborn logic of regular gamblers: Obstacles and dilemmas in cognitive gambling research. Journal of Gambling Studies, 20, 1–21.

    Google Scholar 

  • Delfabbro, P., Falzon, K., & Ingram, T. (2005). The effects of parameter variations in electronic gambling simulations: Results of a laboratory-based pilot intervention. Gambling Research, 17(1), 7–25.

    Google Scholar 

  • Dickerson, M. G. (1979). FI schedules and persistence at gambling in the UK betting office. Journal of Applied Behaviour Analysis, 12, 315–323.

    CAS  Article  Google Scholar 

  • Dixon, M. R., MacLin, O. H., & Daugherty, D. (2006). An evaluation of response allocations to concurrently available slot machine simulations. Behavior Research Methods, 38, 232–236.

    PubMed  Article  Google Scholar 

  • Dixon, M. R., Harrigan, K. A., Sandhu, R., Collins, K., & Fugelsang, J. A. (2010). Losses disguised as wins in modern multi-line video slot machines. Addiction, 105, 1819–1824.

    PubMed  Article  Google Scholar 

  • Dowling, N., Smith, D., & Thomas, T. (2005). Electronic gaming machines: Are they the ‘crack-cocaine’ of gambling? Addiction, 100, 33–45.

    PubMed  Article  Google Scholar 

  • Fischer, S., & Smith, G. T. (2008). Binge eating, problem drinking, and pathological gambling: Linking behavior to shared traits and social learning. Personality and Individual Differences, 44, 789–800.

    Google Scholar 

  • Frederick, S. (2005). Reflection and decision making. The Journal of Economic Perspectives, 19, 25–42.

    Google Scholar 

  • Gilliland, J. A., & Ross, N. A. (2005). Opportunities for video lottery terminal gambling in Montréal: An environmental analysis. Canadian Journal of Public Health, 96, 55–59.

    PubMed  Google Scholar 

  • Götestam, K. G., & Johansson, A. (2003). Characteristics of gambling and problematic gambling in the Norwegian context: A DSM-IV-based telephone interview study. Addictive Behaviors, 28, 189–197.

    PubMed  Article  Google Scholar 

  • Griffiths, M. (1999). Gambling technologies: Prospects for problem gambling. Journal of Gambling Studies, 15, 265–283.

    PubMed  Article  Google Scholar 

  • Habib, R., & Dixon, M. R. (2010). Neurobehavioral evidence for the “near-miss” effect in pathological gamblers. Journal of the Experimental Analysis of Behavior, 93, 313–328.

    PubMed Central  PubMed  Article  Google Scholar 

  • Harrigan, K. A. (2007). Slot machine structural characteristics: Distorted player views of payback percentages. Journal of Gambling Issues, 20, 215–234.

    Article  Google Scholar 

  • Harrigan, K. A., & Dixon, M. (2009). PAR Sheets, probabilities, and slot machine play: Implications for problem and non-problem gambling. Journal of Gambling Studies, 23, 159–174.

    Google Scholar 

  • Harrigan, K. A., & Dixon, M. (2010). Government sanctioned “tight” and “loose” slot machines: How having multiple versions of the same slot machine game may impact problem gambling. Journal of Gambling Studies, 26(1), 159–174.

    PubMed  Article  Google Scholar 

  • Haw, J. (2008a). Random-ratio schedules of reinforcement: The role of early wins and unreinforced trials. Journal of Gambling Issues, 21, 56–67.

    Article  Google Scholar 

  • Haw, J. (2008b). The relationship between reinforcement and gaming machine choice. Journal of Gambling Studies, 24, 55–61.

    PubMed  Article  Google Scholar 

  • Le Pelley, M. E. (2004). The role of associative history in models of associative learning: A selective review and hybrid model. The Quarterly Journal of Experimental Psychology, 57B(3), 193–243.

    Article  Google Scholar 

  • Linnet, J., Thomsen, K. R., Møller, A., & Callesen, M. B. (2010). Event frequency, excitement and desire to gamble, among pathological gamblers. International Gambling Studies, 10(2), 177–188.

    Article  Google Scholar 

  • Lo, R. (2000). Optimism and illusion of control in gambling. Journal of the National Association for Gambling Studies, 12, 18–23.

    Google Scholar 

  • Loba, P., Stewart, S. H., Klein, R. M., & Blackburn, J. M. (2002). Manipulations of the features of standard video lottery terminal (VLT) games: Effects in pathological and non-pathological gamblers. Journal of Gambling Studies, 17(4), 297–320.

    Article  Google Scholar 

  • Lund, I. (2006). Gambling and problem gambling in Norway: What part does the gambling machine play? Addiction Research and Theory, 14, 475–491.

    Article  Google Scholar 

  • Morgan, M. J. (1974). Effects of random reinforcement sequences. Journal of the Experimental Analysis of Behavior, 22, 301–310.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  • Peters, H., Hunt, M., & Harper, D. (2010). An animal model of slot machine gambling: The effect of structural characteristics on response latency and persistence. Journal of Gambling Studies, 26, 521–531.

    PubMed  Article  Google Scholar 

  • Productivity Commission. (2009). Australia’s gambling industries. Canberra: Commonwealth of Australia.

    Google Scholar 

  • Skinner, B. F. (1938). The behavior of organisms. New York: Appleton-Century-Crofts.

    Google Scholar 

  • Steenbergh, T. A., Meyers, A. W., May, R. K., & Whelan, J. P. (2002). Development and validation of the Gambler’s Belief Questionnaire. Psychology of Addictive Behaviors, 16, 143–149.

    Google Scholar 

  • Turner, N., & Horbay, R. (2004). How do slot machines and other electronic gaming machines actually work? Journal of Gambling Issues, 11, 10–50.

    Google Scholar 

  • Tversky, A., & Kahneman, D. (1971). Belief in the law of small numbers. Psychological Bulletin, 76, 105–110.

    Google Scholar 

  • Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5, 207–233.

    Article  Google Scholar 

  • Volberg, R. A., & Abbott, M. W. (1994). Lifetime prevalence estimates of pathological gambling in New Zealand. International Journal of Epidemiology, 23, 976–983.

    CAS  PubMed  Article  Google Scholar 

  • Walker, M. B. (2003). “The seductiveness of poker machines”. In Proceedings of the 13th annual conference of the National Association for Gambling Studies (pp. 12–26), Canberra.

  • Weatherly, J. N., & Brandt, A. E. (2004). Participants’ sensitivity to percentage payback and credit value when playing a slot-machine simulation. Behavior and Social Issues, 13, 33–50.

    Article  Google Scholar 

  • Whiteside, S. P., & Lynam, D. R. (2001). The Five Factor Model and impulsivity: Using a structural model of personality to understand impulsivity. Personality and Individual Differences, 30, 669–689.

    Google Scholar 

  • Zlomke, K. R., & Dixon, M. R. (2006). Modification of slot-machine preferences through the use of a conditional discrimination paradigm. Journal of Applied Behavioral Analysis, 39, 351–361.

    Article  Google Scholar 

Download references

Author information

Affiliations

  1. School of Psychology (A18), The University of Sydney, Sydney, NSW, 2006, Australia

    Ewan Coates & Alex Blaszczynski

Authors
  1. Ewan Coates
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alex Blaszczynski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alex Blaszczynski.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Coates, E., Blaszczynski, A. Predictors of Return Rate Discrimination in Slot Machine Play. J Gambl Stud 30, 669–683 (2014). https://doi.org/10.1007/s10899-013-9375-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10899-013-9375-8

Keywords

  • Gaming machines
  • Slot machines
  • Payback percentage
  • Return to player
  • Volatility
  • Win frequency