Journal of Gambling Studies

, Volume 34, Issue 2, pp 321–338 | Cite as

Electroencephalographic Evidence of Abnormal Anticipatory Uncertainty Processing in Gambling Disorder Patients

  • Alberto Megías
  • Juan F. Navas
  • Ana Perandrés-Gómez
  • Antonio Maldonado
  • Andrés Catena
  • José C. Perales
Original Paper


Putting money at stake produces anticipatory uncertainty, a process that has been linked to key features of gambling. Here we examined how learning and individual differences modulate the stimulus preceding negativity (SPN, an electroencephalographic signature of perceived uncertainty of valued outcomes) in gambling disorder patients (GDPs) and healthy controls (HCs), during a non-gambling contingency learning task. Twenty-four GDPs and 26 HCs performed a causal learning task under conditions of high and medium uncertainty (HU, MU; null and positive cue-outcome contingency, respectively). Participants were asked to predict the outcome trial-by-trial, and to regularly judge the strength of the cue-outcome contingency. A pre-outcome SPN was extracted from simultaneous electroencephalographic recordings for each participant, uncertainty level, and task block. The two groups similarly learnt to predict the occurrence of the outcome in the presence/absence of the cue. In HCs, SPN amplitude decreased as the outcome became predictable in the MU condition, a decrement that was absent in the HU condition, where the outcome remained unpredictable during the task. Most importantly, GDPs’ SPN remained high and insensitive to task type and block. In GDPs, the SPN amplitude was linked to gambling preferences. When both groups were considered together, SPN amplitude was also related to impulsivity. GDPs thus showed an abnormal electrophysiological response to outcome uncertainty, not attributable to faulty contingency learning. Differences with controls were larger in frequent players of passive games, and smaller in players of more active games. Potential psychological mechanisms underlying this set of effects are discussed.


Gambling disorder Causal learning Uncertainty Stimulus preceding negativity Impulsivity Gambling modalities 



We thank Jesús Vetia for designing the graphics used for the task.


Research described in this paper has been funded by a grant to the research group from the Spanish Government (Ministerio de Economía y Competitividad, Secretaría de Estado de Invetigación, Desarrollo e Innovación; Convocatoria 2013 de Proyectos I+D de Excelencia), with Reference Number PSI2013-45055-P. JFN and APG have been awarded with individual research grants (Ministerio de Educación, Cultura y Deporte, Programa FPU, reference number FPU13/00669; and Programa de Becas de Iniciación a la Investigación para estudiantes de másteres oficiales del Plan Propio de Investigación de la Universidad de Granada, 2016, respectively). JCP is member of a RETICS (RD12/0028/0017) group, funded by the Spanish Ministerio de Sanidad y Consumo.

Compliance with Ethical Standards

Conflict of interest

The authors declare no financial interests or potential conflicts of interest.

Ethical Approval

All participants were informed about the procedures included in the studies and signed informed consent. All protocols performed in the studies involving human participants were in accordance with the ethical standards of the Ethics Committee of the University of Granada and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Supplementary material

10899_2017_9693_MOESM1_ESM.docx (14 kb)
Supplementary material 1 (DOCX 14 kb)


  1. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.CrossRefGoogle Scholar
  2. Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Beck depression inventory-II. San Antonio, TX: The Psychological Corporation, Academic Court.Google Scholar
  3. Berridge, K. C., & Robinson, T. E. (1995). The mind of an addicted brain: Neural sensitization of wanting versus liking. Current Directions in Psychological Science, 4(3), 71–75. doi: 10.1111/1467-8721.ep10772316.CrossRefGoogle Scholar
  4. Billieux, J., Lagrange, G., Van Der Linden, M., Lançon, C., Adida, M., & Jeanningros, R. (2012). Investigation of impulsivity in a sample of treatment-seeking pathological gamblers: A multidimensional perspective. Psychiatry Research, 198(2), 291–296. doi: 10.1016/j.psychres.2012.01.001.CrossRefPubMedGoogle Scholar
  5. Blain, B., Richard Gill, P., & Teese, R. (2015). Predicting problem gambling in australian adults using a multifaceted model of impulsivity. International Gambling Studies, 15(2), 1–17. doi: 10.1080/14459795.2015.1029960.CrossRefGoogle Scholar
  6. Blaszczynski, A., McConaghy, N., & Frankova, A. (1990). Boredom proneness in pathological gambling. Psychological Reports, 67(1), 35–42. doi: 10.2466/pr0.1990.67.1.35.CrossRefPubMedGoogle Scholar
  7. Bonnaire, C., Lejoyeux, M., & Dardennes, R. (2004). Sensation seeking in a French population of pathological gamblers: Comparison with regular and nongamblers. Psychological Reports, 94(3), 1361–1371.CrossRefPubMedGoogle Scholar
  8. Cándido, A., Orduña, E., Perales, J. C., Verdejo-García, A., & Billieux, J. (2012). Validation of a short Spanish version of the UPPS-P impulsive behaviour scale. Trastornos Adictivos, 14(3), 73–78. doi: 10.1016/S1575-0973(12)70048-X.CrossRefGoogle Scholar
  9. Catena, A., Perales, J. C., Megías, A., Cándido, A., Jara, E., & Maldonado, A. (2012). The brain network of expectancy and uncertainty processing. PLoS ONE. doi: 10.1371/journal.pone.0040252.PubMedPubMedCentralGoogle Scholar
  10. Cokely, E. T., Galesic, M., Schulz, E., Ghazal, S., & Garcia-Retamero, R. (2012). Measuring risk literacy: The Berlin numeracy test. Judgment and Decision Making, 7, 25–47.Google Scholar
  11. Coventry, K. R., & Brown, R. (1993). Sensation seeking, gambling and gambling addictions. Addiction, 88(4), 541–554. doi: 10.1111/j.1360-0443.1993.tb02061.x.CrossRefPubMedGoogle Scholar
  12. Cyders, M. A., & Smith, G. T. (2008). Clarifying the role of personality dispositions in risk for increased gambling behavior. Personality and Individual Differences, 45(6), 503–508. doi: 10.1016/j.paid.2008.06.002.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. doi: 10.1016/j.jneumeth.2003.10.009.CrossRefPubMedGoogle Scholar
  14. Dickerson, M., Hinchy, J., & Fabre, J. (1987). Chasing, arousal and sensation seeking in off-course gamblers. British Journal of Addiction, 82(6), 673–680. doi: 10.1111/j.1360-0443.1987.tb01530.x.CrossRefPubMedGoogle Scholar
  15. Echeburúa, C., Báez, E., Fernández-Montalvo, J., & Paéz, D. (1994). Cuestionario de Juego Patológico de South Oaks (SOGS): Validación española [The South Oaks Gambling Screen (SOGS): Spanish validation]. Análisis Y Modificación de Conducta, 20(74), 769–791.Google Scholar
  16. Eysenck, S. B., Pearson, P. R., Easting, G., & Allsopp, J. F. (1985). Age norms for impulsiveness, venturesomeness and empathy in adults. Personality and Individual Differences, 6(5), 613–619. doi: 10.1016/0191-8869(85)90011-X.CrossRefGoogle Scholar
  17. Fiorillo, C. D. (2011). Transient activation of midbrain dopamine neurons by reward risk. Neuroscience, 197, 162–171. doi: 10.1016/j.neuroscience.2011.09.037.CrossRefPubMedPubMedCentralGoogle Scholar
  18. Fuentemilla, L., Cucurell, D., Marco-Pallarés, J., Guitart-Masip, M., Morís, J., & Rodríguez-Fornells, A. (2013). Electrophysiological correlates of anticipating improbable but desired events. NeuroImage, 78, 135–144. doi: 10.1016/j.neuroimage.2013.03.062.CrossRefPubMedGoogle Scholar
  19. Garcia-Retamero, R., Cokely, E. T., Ghazal, S., & Joeris, A. (2016). Measuring graph literacy without a test: A brief subjective assessment. Medical Decision Making, 36, 854–867. doi: 10.1177/0272989X16655334.CrossRefPubMedGoogle Scholar
  20. Grummett, T. S., Fitzgibbon, S. P., Lewis, T. W., DeLosAngeles, D., Whitham, E. M., Pope, K. J., et al. (2014). Constitutive spectral EEG peaks in the gamma range: Suppressed by sleep, reduced by mental activity and resistant to sensory stimulation. Frontiers in Human Neuroscience, 8, 927. doi: 10.3389/fnhum.2014.00927.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Joyce, C. A., Gorodnitsky, I. F., & Kutas, M. (2004). Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology, 41(2), 313–325. doi: 10.1111/j.1469-8986.2003.00141.x.CrossRefPubMedGoogle Scholar
  22. Klem, G. H., Lüders, H. O., Jasper, H. H., & Elger, C. (1999). The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. Electroencephalography and Clinical Neurophysiology. Supplement, 52, 3–6. PMID: 10590970.PubMedGoogle Scholar
  23. Kotani, Y., Kishida, S., Hiraku, S., Suda, K., Ishii, M., & Aihara, Y. (2003). Effects of information and reward on stimulus preceding negativity prior to feedback stimuli. Psychophysiology, 40(5), 818–826. doi: 10.1111/1469-8986.00082.CrossRefPubMedGoogle Scholar
  24. Kothe, C. (2013). The artifact subspace reconstruction method.
  25. Lesieur, H. R., & Blume, S. B. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144, 1184–1188. doi: 10.1176/ajp.144.9.1184.CrossRefPubMedGoogle Scholar
  26. Love, J., Selker, R., Verhagen, J., Marsman, M., Gronau, Q. F., Jamil, T., et al. (2015). JASP (Version 0.6). Computer software.
  27. MacLaren, V. V., Fugelsang, J. A., Harrigan, K. A., & Dixon, M. J. (2011). Clinical psychology review the personality of pathological gamblers: A meta-analysis. Clinical Psychology Review, 31(6), 1057–1067. doi: 10.1016/j.cpr.2011.02.002.CrossRefPubMedGoogle Scholar
  28. Masaki, H., Yamazaki, K., & Hackley, S. A. (2010). Stimulus-preceding negativity is modulated by action-outcome contingency. NeuroReport, 21(4), 277–281. doi: 10.1097/WNR.0b013e3283360bc3.CrossRefPubMedGoogle Scholar
  29. Mercer, K. B., & Eastwood, J. D. (2010). Is boredom associated with problem gambling behaviour? It depends on what you mean by ‘boredom’. International Gambling Studies, 10(1), 91–104. doi: 10.1080/14459791003754414.CrossRefGoogle Scholar
  30. Michalczuk, R., Bowden-Jones, H., Verdejo-Garcia, A., & Clark, L. (2011). Impulsivity and cognitive distortions in pathological gamblers attending the UK National Problem Gambling Clinic: A preliminary report. Psychological Medicine, 41(12), 2625–2635. doi: 10.1017/S003329171100095X.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Morey, R. D., & Rouder, J. N. (2015). No BayesFactor (Version 0.9.10-2). Computer software.
  32. Morís, J., Luque, D., & Rodríguez-Fornells, A. (2013). Learning-induced modulations of the stimulus-preceding negativity. Psychophysiology, 50(9), 931–939. doi: 10.1111/psyp.12073.CrossRefPubMedGoogle Scholar
  33. Mullen, T., Kothe, C., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., et al. (2013). Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG. In Annual international conference of the IEEE engineering in medicine and biology society. IEEE engineering in medicine and biology society. Conference (pp. 2184–2187). NIH Public Access. doi: 10.1109/EMBC.2013.6609968.
  34. Myrseth, H., Brunborg, G. S., & Eidem, M. (2010). Differences in cognitive distortions between pathological and non-pathological gamblers with preferences for chance or skill games. Journal of Gambling Studies, 26(4), 561–569. doi: 10.1007/s10899-010-9180-6.CrossRefPubMedGoogle Scholar
  35. Navas, J. F., Billieux, J., Perandrés-Gómez, A., López-Torrecillas, F., Cándido, A., & Perales, J. C. (2017a). Individual differences associated to gambling preferences and clinical status. International Gambling Studies (in press).Google Scholar
  36. Navas, J. F., Contreras-Rodríguez, O., Verdejo-Román, J., Perandrés-Gómez, A., Albein-Urios, N., Verdejo-García, A., & Perales, J. C. (2017b). Trait and neurobiological underpinnings of negative emotion regulation in gambling disorder. Addiction (in press).Google Scholar
  37. Novak, B. K., Novak, K. D., Lynam, D. R., & Foti, D. (2016). Individual differences in the time course of reward processing: Stage-specific links with depression and impulsivity. Biological Psychology, 119, 79–90. doi: 10.1016/j.biopsycho.2016.07.008.CrossRefPubMedGoogle Scholar
  38. Pedrero-Pérez, E. J., Rodríguez-Monje, M. T., Gallardo-Alonso, F., Fernández Girón, M., Pérez López, M., & Chicharro-Romero, J. (2007). Validación de un instrumento para la detección de trastornos de control de impulsos y adicciones: El MULTICAGE CAD-4 [Validation of a tool for screening of impulse control disorders and addiction: MULTICAGE CAD-4]. Trastornos Adictivos, 9(4), 269–278. doi: 10.1016/S1575-0973(07)75656-8.CrossRefGoogle Scholar
  39. Perales, J. C., Catena, A., Shanks, D. R., & González, J. A. (2005). Dissociation between judgments and outcome-expectancy measures in covariation learning: A signal detection theory approach. Journal of Experimental Psychology. Learning, Memory, and Cognition, 31(5), 1105–1120. doi: 10.1037/0278-7393.31.5.1105.CrossRefPubMedGoogle Scholar
  40. Perrin, F., Pernier, J., Bertrand, O., & Echallier, J. F. (1989). Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology, 72(2), 184–187. doi: 10.1016/0013-4694(89)90180-6.CrossRefPubMedGoogle Scholar
  41. Piazza, C., Cantiani, C., Akalin-Acar, Z., Miyakoshi, M., Benasich, A. A., Reni, G., et al. (2016). ICA-derived cortical responses indexing rapid multi-feature auditory processing in six-month-old infants. NeuroImage, 133, 75–87.CrossRefPubMedGoogle Scholar
  42. Quester, S., & Romanczuk-Seiferth, N. (2015). Brain imaging in gambling disorder. Current Addiction Reports, 2(3), 220–229. doi: 10.1007/s40429-015-0063-x.CrossRefPubMedPubMedCentralGoogle Scholar
  43. Robinson, M., Anselme, P., Fischer, A. M., & Berridge, K. C. (2014). Initial uncertainty in Pavlovian reward prediction persistently elevates incentive salience and extends sign-tracking to normally unattractive cues. Behavioural Brain Research, 266, 119–130. doi: 10.1016/j.bbr.2014.03.004.CrossRefPubMedPubMedCentralGoogle Scholar
  44. Robinson, T. E., & Berridge, K. C. (1993). The neural basis of drug craving: An incentive-sensitization theory of addiction. Brain Research Reviews, 18(3), 247–291. doi: 10.1016/0165-0173(93)90013-P.CrossRefPubMedGoogle Scholar
  45. Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56(5), 356–374.CrossRefGoogle Scholar
  46. Sanz, J., Perdigón, A. L., & Vázquez, C. (2003). Adaptación española del Inventario para la Depresión de Beck-II (BDI-II): 3. Propiedades psicométricas en población general. [Spanish adaptation of the Beck Depression Inventory-II (BDI-II): 3. Psychometric features in patients with psychological disorders]. Clínica Y Salud, 14(3), 249–280.Google Scholar
  47. Schneider, W., Eschman, A., & Zuccolotto, A. (2002). E-Prime reference guide. Incorporated: Psychology Software Tools.Google Scholar
  48. Tang, A. C., Sutherland, M. T., & McKinney, C. J. (2005). Validation of SOBI components from high-density EEG. NeuroImage, 25(2), 539–553. doi: 10.1016/j.neuroimage.2004.11.027.CrossRefPubMedGoogle Scholar
  49. Toneatto, T., Blitz-Miller, T., Calderwood, K., Dragonetti, R., & Tsanos, A. (1997). Cognitive distortions in heavy gambling. Journal of Gambling Studies, 13(3), 253–266. doi: 10.1023/A:1024983300428.CrossRefPubMedGoogle Scholar
  50. van Holst, R. J., van den Brink, W., Veltman, D. J., & Goudriaan, A. E. (2010). Brain imaging studies in pathological gambling. Current Psychiatry Reports, 12(5), 418–425. doi: 10.1007/s11920-010-0141-7.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Wechsler, D. (2008). Wechsler adult intelligence scale—Fourth edition (WAIS-IV). San Antonio, TX: NCS Pearson.Google Scholar
  52. 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(4), 669–689. doi: 10.1016/S0191-8869(00)00064-7.CrossRefGoogle Scholar
  53. Winkler, I., Haufe, S., & Tangermann, M. (2011). Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behavioral and Brain Functions, 7(1), 30. doi: 10.1186/1744-9081-7-30.CrossRefPubMedPubMedCentralGoogle Scholar
  54. World Medical Asociation. (2008). Declaration of helsinki: Ethical principles for medical research involving human subjects. Seoul: World Medical Association, Inc. doi: 10.1001/jama.2013.281053.Google Scholar
  55. Zack, M., & Poulos, C. X. (2009). Parallel roles for dopamine in pathological gambling and psychostimulant addiction. Current Drug Abuse Reviews, 2(1), 11–25. doi: 10.2174/1874473710902010011.CrossRefPubMedGoogle Scholar
  56. Zuckerman, M. (2007). Sensation seeking and risky behavior. Washington, DC: American Psychological Association.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Alberto Megías
    • 1
    • 2
  • Juan F. Navas
    • 1
  • Ana Perandrés-Gómez
    • 1
  • Antonio Maldonado
    • 1
  • Andrés Catena
    • 1
  • José C. Perales
    • 1
  1. 1.Experimental Psychology Department, Mind, Brain, and Behavior Research CenterUniversidad de GranadaGranadaSpain
  2. 2.Departamento de Psicología BásicaUniversidad de MálagaMálagaSpain

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