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The Effectiveness of the Game of Dice Task in Predicting At-Risk and Problem Gambling Among Adolescents: The Contribution of the Neural Networks

  • Maria Anna Donati
  • Andrea Frosini
  • Viola Angela Izzo
  • Caterina Primi
Original Paper

Abstract

The Game of Dice Task (GDT; Brand et al. in Neuropsychology 19:267–277, 2005a; Psychiatry Res 133:91–99, 2005b) measures decision-making under objective risk conditions. Although disadvantageous decision-making has been shown in individuals with substance dependency, such as pathological dependency, any studies have been conducted with adolescents by using the GDT to investigate the relationship between the performance on the task and gambling behavior. Moreover, all the previous studies have considered only the GDT net score and not the single choices. In the current study, focusing on adolescents, we wanted to investigate the relationship between the sequence of the choices at the GDT and gambling behavior, measured with the SOGS-RA. To analyze the predictive power of the sequence of choices made in the GDT and problem gambling and gambling frequency, we used the Neural Networks (NNs), which are often used to find relationships between a series of input actions and the correspondent empirical outputs in order to discover behavioral patterns that may be predictive of at-risk behaviors. Results showed that neither a linear or a non-linear relationship could be detected between the GDT performance and the SOGS-RA classification both in terms of gambling problem severity and gambling frequency. Indeed, different training algorithms produced different performances of the NN on the training sets, but all of them showed a very low prediction capability on new samples. Thus, the performance at the GDT did not discriminate between adolescent gamblers with different and progressive levels of problematic gambling behavior and gambling frequency. Limitations and future studies are discussed.

Keywords

Adolescents Decision making Gambling Game of Dice Task Neural Networks SOGS-RA 

Notes

Compliance with Ethical Standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical Standards

All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

References

  1. American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: American Psychiatric Association.Google Scholar
  2. Ananda Rao, M., & Srinivas, J. (2003). Neural networks: Algorithms and applications. Pangbourne: Alpha Science International.Google Scholar
  3. Arbib, M. A. (Ed.). (2003). The handbook of brain theory and neural networks. Cambridge: MIT press.Google Scholar
  4. Azoff, E. M. (1994). Neural network time series forecasting of financial markets. Hoboken: Wiley.Google Scholar
  5. Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Oxford University Press.Google Scholar
  6. Blinn-Pike, L., Worthy, S. L., & Jonkman, J. N. (2010). Adolescent gambling: A review of an emerging field of research. Journal of Adolescent Health, 47, 223–236.  https://doi.org/10.1016/j.jadohealth.2010.05.003.CrossRefPubMedGoogle Scholar
  7. Brand, M., Franke-Sievert, C., Jacoby, G. E., Markowitsch, H. J., & Tuschen-Caffier, B. (2007a). Neuropsychological correlates of decision making in patients with bulimia nervosa. Neuropsychology, 21(6), 742.  https://doi.org/10.1037/0894-4105.21.6.742.CrossRefPubMedGoogle Scholar
  8. Brand, M., Fujiwara, E., Borsutzky, S., Kalbe, E., Kessler, J., & Markowitsch, H. J. (2005a). Decision making deficits of Korsakoff patients in a new gambling task with explicit rules: Associations with executive functions. Neuropsychology, 19, 267–277.  https://doi.org/10.1037/0894-4105.19.3.267.CrossRefPubMedGoogle Scholar
  9. Brand, M., Grabenhorst, F., Starcke, K., Vandekerckhove, M. M., & Markowitsch, H. J. (2007b). Role of the amygdala in decisions under ambiguity and decisions under risk: evidence from patients with Urbach-Wiethe disease. Neuropsychologia, 45(6), 1305–1317.  https://doi.org/10.1016/j.neuropsychologia.2006.09.021.CrossRefPubMedGoogle Scholar
  10. Brand, M., Kalbe, E., Labudda, K., Fujiwara, E., Kessler, J., & Markowitsch, H. J. (2005b). Decision-making impairments in patients with pathological gambling. Psychiatry Research, 133, 91–99.  https://doi.org/10.1016/j.psychres.2004.10.003.CrossRefPubMedGoogle Scholar
  11. Brand, M., Labudda, K., Kalbe, E., Hilker, R., Emmans, D., Fuchs, G., et al. (2004). Decision-making impairments in patients with Parkinson’s disease. Behavioural Neurology, 15, 77–85.  https://doi.org/10.1155/2004/578354.CrossRefPubMedGoogle Scholar
  12. Brand, M., Labudda, K., & Markowitsch, H. J. (2006). Neuropsychological correlates of decision-making in ambiguous and risky situations. Neural Networks, 19(8), 1266–1276.  https://doi.org/10.1016/j.neunet.2006.03.001.CrossRefPubMedGoogle Scholar
  13. Brand, M., Laier, C., Pawlikowski, M., & Markowitsch, H. J. (2009). Decision making with and without feedback: The role of intelligence, strategies, executive functions, and cognitive styles. Journal of Clinical and Experimental Neuropsychology, 31, 984–998.  https://doi.org/10.1080/13803390902776860.CrossRefPubMedGoogle Scholar
  14. Brand, M., Roth-Bauer, M., Driessen, M., & Markowitsch, H. J. (2008). Executive functions and risky decision-making in patients with opiate dependence. Drug and Alcohol Dependence, 97(1), 64–72.  https://doi.org/10.1016/j.drugalcdep.2008.03.017.CrossRefPubMedGoogle Scholar
  15. Burden, F., & Winkler, D. (2008). Bayesian regularization of neural networks. Methods in Molecular Biology, 458, 25–44.PubMedGoogle Scholar
  16. Calado, F., Alexandre, J., & Griffiths, M. D. (2016). Prevalence of adolescent problem gambling: A systematic review of recent research. Journal of Gambling Studies, 33, 397–424.  https://doi.org/10.1007/s10899-016-9627-5.CrossRefPubMedCentralGoogle Scholar
  17. Chambers, R. A., & Potenza, M. N. (2003). Neurodevelopment, impulsivity, and adolescent gambling. Journal of Gambling Studies, 19(1), 53–84.CrossRefPubMedGoogle Scholar
  18. Chan, V. K. (2010). Using neural networks to model the behavior and decisions of gamblers, in particular, cyber-gamblers. Journal of Gambling Studies, 26(1), 35–52.  https://doi.org/10.1007/s10899-009-9139-7.CrossRefPubMedGoogle Scholar
  19. Chiesi, F., Donati, M. A., Galli, S., & Primi, C. (2013). The suitability of the SOGS-RA as screening tool: Item response theory-based evidence. Psychology of Addictive Behaviors, 27, 287–293.  https://doi.org/10.1037/a0029987.CrossRefPubMedGoogle Scholar
  20. Chiu, J., & Storm, L. (2010). Personality, perceived luck and gambling attitudes as predictors of gambling involvement. Journal of Gambling Studies, 26, 205–227.  https://doi.org/10.1007/s10899-009-9160-x.CrossRefPubMedGoogle Scholar
  21. Colasante, E., Gori, M., Bastiani, L., Scalese, M., Siciliano, V., & Molinaro, S. (2014). Italian adolescent gambling behaviour: Psychometric evaluation of the South Oaks Gambling Screen-Revised for Adolescents (SOGS-RA) among a sample of Italian students. Journal of Gambling Studies, 30, 789–801.  https://doi.org/10.1007/s10899-013-9385-6.CrossRefPubMedGoogle Scholar
  22. Defoe, I. N., Dubas, J. S., Figner, B., & van Aken, M. A. (2015). A meta-analysis on age differences in risky decision making: Adolescents versus children and adults. Psychological Bulletin.  https://doi.org/10.1037/a0038088.PubMedGoogle Scholar
  23. Delazer, M., Sinz, H., Zamarian, L., & Benke, T. (2007). Decision-making with explicit and stable rules in mild Alzheimer’s disease. Neuropsychologia, 45(8), 1632–1641.  https://doi.org/10.1016/j.neuropsychologia.2007.01.006.CrossRefPubMedGoogle Scholar
  24. Derevensky, J. L., Sklar, A., Gupta, R., & Messerlian, C. (2010). An empirical study examining the impact of gambling advertisements on adolescent gambling attitudes and behaviors. International Journal of Mental Health and Addiction, 8, 21–34.  https://doi.org/10.1007/s11469-009-9211-7.CrossRefGoogle Scholar
  25. Donati, M. A., Chiesi, F., Izzo, V. A., & Primi, C. (2017). Gender invariance of the gambling behavior scale for adolescents (GBS-A): An analysis of differential item functioning using item response theory. Frontiers in Psychology, 8, 940.  https://doi.org/10.3389/fpsyg.2017.00940.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Donati, M. A., Chiesi, F., & Primi, C. (2013). A model to explain at risk/problem gambling among male and female adolescents: Gender similarities and differences. Journal of Adolescence, 36, 129–137.  https://doi.org/10.1016/j.adolescence.2012.10.001.CrossRefPubMedGoogle Scholar
  27. Donati, M. A., Panno, A., Chiesi, F., & Primi, C. (2014). A mediation model to explain decision making under conditions of risk among adolescents: The role of fluid intelligence and probabilistic reasoning. Journal of Clinical and Experimental Neuropsychology, 36(6), 588–595.  https://doi.org/10.1080/13803395.2014.918091.CrossRefPubMedGoogle Scholar
  28. Drechsler, R., Rizzo, P., & Steinhausen, H. C. (2008). Decision-making on an explicit risk taking task in preadolescents with attention-deficit/hyperactivity disorder. Journal of Neural Transmission, 115, 201–209.  https://doi.org/10.1007/s00702-007-0814-5.CrossRefPubMedGoogle Scholar
  29. Edgren, R., Castrén, S., Mäkelä, M., Pörtfors, P., Alho, H., & Salonen, A. H. (2016). Reliability of instruments measuring at-risk and problem gambling among young individuals: A systematic review covering years 2009–2015. Journal of Adolescent Health, 58, 600–615.  https://doi.org/10.1016/j.jadohealth.2016.03.007.CrossRefPubMedGoogle Scholar
  30. Fahlman, S. E. (1989). An empirical study of learning speed in back-propagation networks. Project report CMU-CS- 88-162. Computer Science Department, Carnegie Mellon University.Google Scholar
  31. Foresee, F. D., & Hagan, M. T. (1997). Gauss-Newton approximation to Bayesian regularization. In Proceedings of the 1997 international joint conference on neural networks (pp. 1930–1935).Google Scholar
  32. Hewig, J., Kretschmer, N., Trippe, R. H., Hecht, H., Coles, M. G., Holroyd, C. B., et al. (2010). Hypersensitivity to reward in problem gamblers. Biological Psychiatry, 67(8), 781–783.  https://doi.org/10.1016/j.biopsych.2009.11.009.CrossRefPubMedGoogle Scholar
  33. Howland, P. (2001). Toward an ethnography of lotto. International Gambling Studies, 1(1), 8–25.CrossRefGoogle Scholar
  34. Joukhador, J., Blaszczynski, A., & Maccallum, F. (2004). Superstitious beliefs in gambling among problem and non-problem gamblers: Preliminary data. Journal of Gambling Studies, 20(2), 171–180.  https://doi.org/10.1023/B:JOGS.0000022308.27774.2b.CrossRefPubMedGoogle Scholar
  35. Ladouceur, R., Bouchard, C., Rhéaume, N., Jacques, C., Ferland, F., Leblond, J., et al. (2000). Is the SOGS an accurate measure of pathological gambling among children, adolescents and adults? Journal of Gambling Studies, 16(1), 1–24.CrossRefPubMedGoogle Scholar
  36. Langhinrichsen-Rohling, J., Rohling, M. L., Rohde, P., & Seeley, J. R. (2004). The SOGS-RA vs. the MAGS-7: Prevalence estimates and classification congruence. Journal of Gambling Studies, 20(3), 259–281.CrossRefPubMedGoogle Scholar
  37. Levin, I. P., & Hart, S. S. (2003). Risk preferences in young children: Early evidence of individual differences in reaction to potential gains and losses. Journal of Behavioral Decision Making, 16(5), 397–413.CrossRefGoogle Scholar
  38. MacKay, D. J. (1992). Bayesian interpolation. Neural Computation, 4(3), 415–447.CrossRefGoogle Scholar
  39. Maslowsky, J., Keating, D. P., Monk, C. S., & Schulenberg, J. (2011). Planned versus unplanned risks: Neurocognitive predictors of subtypes of adolescents’ risk behavior. International Journal of Behavioral Development, 35(2), 152–160.  https://doi.org/10.1177/0165025410378069.CrossRefPubMedPubMedCentralGoogle Scholar
  40. Nikolaev, N. Y., & Iba, H. (2006). Adaptive learning of polynomial networks: Genetic programming, backpropagation and Bayesian methods. New York, NY: Springer.Google Scholar
  41. Pawlikowski, M., & Brand, M. (2011). Excessive internet gaming and decision making: Do excessive World of Warcraft players have problems in decision making under risky conditions? Psychiatry Research, 188, 428–433.  https://doi.org/10.1016/j.psychres.2011.05.017.CrossRefPubMedGoogle Scholar
  42. Petry, N. M. (2006). Should the scope of addictive behaviors be broadened to include pathological gambling? Addiction, 101(s1), 152–160.CrossRefPubMedGoogle Scholar
  43. Potenza, M. N., Wareham, J. D., Steinberg, M. A., Rugle, L., Cavallo, D. A., Krishnan-Sarin, S., et al. (2011). Correlates of at-risk/problem internet gambling in adolescents. Journal of American Academy of Child and Adolescent Psychiatry, 50, 150–159.  https://doi.org/10.1016/j.jaac.2010.11.006.CrossRefGoogle Scholar
  44. Primi, C., Donati, M. A., & Chiesi, F. (2015). Gambling behavior scale for adolescents. Scala per la Misura del Comportamento di Gioco D’azzardo Negli Adolescenti [Gambling behavior scale for adolescents. A scale to assess gambling behavior among adolescents]. Florence: Hogrefe Editore.Google Scholar
  45. Primi, C., Donati, M. A., Chiesi, F., & Panno, A. (2016). Decision making under risk in adolescents: Further evidence of the role of probabilistic reasoning. In M. E. Toplak & J. A. Weller (Eds.), Individual differences in judgment and decision making from a developmental context (pp. 48–66). Hove: Psychology Press.Google Scholar
  46. Rumelhart, D. E., McClelland, J., & The PDP Research Group (Eds.). (1986). Parallel distributed processing. Cambridge, MA: The MIT press.Google Scholar
  47. Schiebener, J., & Brand, M. (2015). Decision making under objective risk conditions–a review of cognitive and emotional correlates, strategies, feedback processing, and external influences. Neuropsychology Review, 25(2), 171–198.  https://doi.org/10.1007/s11065-015-9285-x.CrossRefPubMedGoogle Scholar
  48. Schiebener, J., García-Arias, M., García-Villamisar, D., Cabanyes-Truffino, J., & Brand, M. (2015). Developmental changes in decision making under risk: The role of executive functions and reasoning abilities in 8-to 19-year-old decision makers. Child Neuropsychology, 21(6), 759–778.  https://doi.org/10.1080/09297049.2014.934216.CrossRefPubMedGoogle Scholar
  49. Schiebener, J., Zamarian, L., Delazer, M., & Brand, M. (2011). Executive functions, categorization of probabilities, and learning from feedback: What does really matter for decision making under explicit risk conditions? Journal of Clinical and Experimental Neuropsychology, 33(9), 1025–1039.  https://doi.org/10.1080/13803395.2011.595702.CrossRefPubMedGoogle Scholar
  50. Schonberg, T., Fox, C. R., & Poldrack, R. A. (2011). Mind the gap: Bridging economic and naturalistic risk-taking with cognitive neuroscience. Trends in Cognitive Sciences, 15(1), 11–19.  https://doi.org/10.1016/j.tics.2010.10.002.CrossRefPubMedGoogle Scholar
  51. Svaldi, J., Brand, M., & Tuschen-Caffier, B. (2010). Decision-making impairments in women with binge eating disorder. Appetite, 54(1), 84–92.  https://doi.org/10.1016/j.appet.2009.09.010.CrossRefPubMedGoogle Scholar
  52. Titterington, D. M. (2004). Bayesian methods for neural networks and related models. Statistical Science, 19(1), 128–139.CrossRefGoogle Scholar
  53. Toneatto, T. (1999). Cognitive psychopathology of problem gambling. Substance Use and Misuse, 34(11), 1593–1604.CrossRefPubMedGoogle Scholar
  54. Volberg, R. A., Gupta, R., Griffiths, M. D., Ólason, D. T., & Delfabbro, P. (2010). An international perspective on youth gambling prevalence studies. International Journal of Adolescent Medicine and Health, 22, 3–38.PubMedGoogle Scholar
  55. Weller, J. A., Levin, I. P., Shiv, B., & Bechara, A. (2007). Neural correlates of adaptive decision making for risky gains and losses. Psychological Science, 18(11), 958–964.  https://doi.org/10.1111/j.1467-9280.2007.02009.x.CrossRefPubMedGoogle Scholar
  56. Welte, J. W., Barnes, G. M., Tidwell, M. C. O., & Hoffman, J. H. (2009). The association of form of gambling with problem gambling among American youth. Psychology of Addictive Behaviors, 23, 105–112.  https://doi.org/10.1037/a0013536.CrossRefPubMedPubMedCentralGoogle Scholar
  57. Welte, J. W., Barnes, G. M., Wieczorek, W. F., Tidwell, M. C. O., & Parker, J. C. (2004). Risk factors for pathological gambling. Addictive Behaviors, 29(2), 323–335.  https://doi.org/10.1016/j.addbeh.2003.08.007.CrossRefPubMedGoogle Scholar
  58. Wickwire, E. M., Whelan, J. P., Meyers, A. W., & Murray, D. M. (2007). Environmental correlates of gambling behavior in urban adolescents. Journal of Abnormal Psychology, 35, 179–190.  https://doi.org/10.1007/s10802-006-9065-4.Google Scholar
  59. Wiebe, J. M. D., Cox, B. J., & Mehmel, B. G. (2000). The South Oaks gambling screen revised for adolescents (SOGS-RA): Further psychometric findings from a community sample. Journal of Gambling Studies, 16, 275–288.  https://doi.org/10.1023/A:1009489132628.CrossRefPubMedGoogle Scholar
  60. Winters, K. C., Stinchfield, R. D., & Fulkerson, J. (1993). Toward the development of an adolescent gambling problem severity scale. Journal of Gambling Studies, 9, 63–84.  https://doi.org/10.1007/BF01019925.CrossRefGoogle Scholar
  61. Winters, K. C., Stinchfield, R., & Kim, L. (1995). Monitoring adolescent gambling in Minnesota. Journal of Gambling Studies, 11, 165–183.  https://doi.org/10.1007/BF02107113.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Maria Anna Donati
    • 1
  • Andrea Frosini
    • 2
  • Viola Angela Izzo
    • 1
  • Caterina Primi
    • 1
  1. 1.NEUROFARBA Department, Section of PsychologyUniversity of FlorenceFlorenceItaly
  2. 2.Department of Computer ScienceUniversity of FlorenceFlorenceItaly

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