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Nonmonetary Decision-Making Indices Discriminate Between Different Behavioral Components of Gambling

Journal of Gambling Studies volume 31, pages 1545–1560 (2015)Cite this article

Abstract

Recent research has proposed that altered reward and punishment sensitivity, heightened impulsivity, and faulty dynamic decision-making are at the core of disordered gambling. However, each of these traits and cognitive aspects dimensionally vary in the normal population, such that the link between individual differences in these dimensions and gambling use can be ultimately informative to explain disordered gambling. The main aim of the present study was to investigate the contribution of such decision-making-related indices to gambling use parameters in a community sample of college students. Assessment included punishment and reward sensitivity (as measured by the shortened Sensitivity to Punishment and Sensitivity to Reward Questionnaire), impulsivity (as measured by the UPPS-P model and a motor inhibition Go/No-go task), and dynamic decision-making [as measured by the probabilistic reversal learning task (PRLT)]. A structured interview was conducted to explore quantitative aspects of the participants gambling habits (gambling presence, gambling frequency, and average amount of money spent in gambling per unit of time). Our results showed the existence of a decision-making profile of gambling, as it naturally occurs in college students, in which sensation seeking is directly and specifically related to gambling presence (gambling, or not gambling at all), punishment sensitivity is inversely related to gambling frequency, and inflexibility in the PRLT specifically predicts the losses accrued because of gambling. These results are compatible with the idea that sensation seeking and punishment insensitivity could increase exposure to gambling activities, whereas reversal learning inflexibility, in people who already gamble, could boost the risk to accumulate losses.

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Notes

  1. For ethical reasons, when these tasks are applied to gamblers, rewards and punishments are non-monetary, and provided in the form of tokens or virtual points.

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Acknowledgments

The research presented here has been funded by Grants from the Ministerio de Ciencia e Innovación, MICINN (Spain), for the 2nd and 6th authors (ref. # PSI2009-13133), and for the 5th author (ref. # PSI2009-12217); and by a RETICS (Redes Temáticas de Investigación Cooperativa en Salud) subprogramme Grant (Ref. # RD12/0028/0017) from the Ministerio de Sanidad, Servicios Sociales e Igualdad (Spain), for the 4th and 6th authors. We specially thank AGRAJER (Granadian Association of Gamblers in Rehabilitation) for their continuous support and advice.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

Participants were informed of the aims of the study, and signed a written consent form before participation. No compensation was given for participation. Funding agencies did not have role in planning this study, or financial interest in its results. The study was carried out in agreement with the declaration of Helsinki, and was approved by the ethical committee of the University of Granada.

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Authors and Affiliations

  1. Experimental Psychology Department; Mind, Brain, and Behavior Research Center, University of Granada, 18071, Granada, Spain

    Juan F. Navas, Ana Torres, Andrés Catena & José C. Perales

  2. Personality, Assessment, and Psychological Treatment Department, University of Granada, Granada, Spain

    Raquel Vilar & Antonio Verdejo-García

  3. Monash University, Melbourne, Australia

    Antonio Verdejo-García

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  1. Juan F. Navas
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  2. Ana Torres
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  4. Antonio Verdejo-García
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  6. José C. Perales
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Correspondence to Juan F. Navas.

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Navas, J.F., Torres, A., Vilar, R. et al. Nonmonetary Decision-Making Indices Discriminate Between Different Behavioral Components of Gambling. J Gambl Stud 31, 1545–1560 (2015). https://doi.org/10.1007/s10899-014-9482-1

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Keywords

  • Gambling
  • Decision-making
  • Impulsivity
  • Reward and punishment sensitivity
  • Reversal learning