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Journal of Gambling Studies

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25.07.2018 | Original Paper

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

verfasst von: Maria Anna Donati, Andrea Frosini, Viola Angela Izzo, Caterina Primi

Erschienen in: Journal of Gambling Studies | Ausgabe 1/2019

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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.

American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: American Psychiatric Association.

Ananda Rao, M., & Srinivas, J. (2003). Neural networks: Algorithms and applications. Pangbourne: Alpha Science International.

Arbib, M. A. (Ed.). (2003). The handbook of brain theory and neural networks. Cambridge: MIT press.

Azoff, E. M. (1994). Neural network time series forecasting of financial markets. Hoboken: Wiley.

Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Oxford University Press.

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

Burden, F., & Winkler, D. (2008). Bayesian regularization of neural networks. Methods in Molecular Biology, 458, 25–44.PubMed

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.CrossRefPubMedCentral

Chambers, R. A., & Potenza, M. N. (2003). Neurodevelopment, impulsivity, and adolescent gambling. Journal of Gambling Studies, 19(1), 53–84.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRef

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.

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).

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.CrossRefPubMed

Howland, P. (2001). Toward an ethnography of lotto. International Gambling Studies, 1(1), 8–25.CrossRef

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRef

MacKay, D. J. (1992). Bayesian interpolation. Neural Computation, 4(3), 415–447.CrossRef

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.CrossRefPubMedPubMedCentral

Nikolaev, N. Y., & Iba, H. (2006). Adaptive learning of polynomial networks: Genetic programming, backpropagation and Bayesian methods. New York, NY: Springer.

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.CrossRefPubMed

Petry, N. M. (2006). Should the scope of addictive behaviors be broadened to include pathological gambling? Addiction, 101(s1), 152–160.CrossRefPubMed

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.CrossRef

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.

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.

Rumelhart, D. E., McClelland, J., & The PDP Research Group (Eds.). (1986). Parallel distributed processing. Cambridge, MA: The MIT press.

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

Titterington, D. M. (2004). Bayesian methods for neural networks and related models. Statistical Science, 19(1), 128–139.CrossRef

Toneatto, T. (1999). Cognitive psychopathology of problem gambling. Substance Use and Misuse, 34(11), 1593–1604.CrossRefPubMed

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.PubMed

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRef

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.CrossRefPubMed

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.CrossRef

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.CrossRefPubMed

Titel: The Effectiveness of the Game of Dice Task in Predicting At-Risk and Problem Gambling Among Adolescents: The Contribution of the Neural Networks
verfasst von: Maria Anna Donati
Andrea Frosini
Viola Angela Izzo
Caterina Primi
Publikationsdatum: 25.07.2018
Verlag: Springer US
Erschienen in: Journal of Gambling Studies / Ausgabe 1/2019
Elektronische ISSN: 1573-3602
DOI: https://doi.org/10.1007/s10899-018-9796-5

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