Advertisement

Animal Models of Gambling-Related Behaviour

  • Paul J. CockerEmail author
  • Catharine A. WinstanleyEmail author
Chapter

Abstract

Gambling is a heterogeneous and complex disorder that is phenomenologically similar to drug or alcohol addiction. The range of gambling games continues to proliferate, and the ease of access to gambling opportunities continues to increase. However, there are currently no dedicated pharmacological treatments available for gambling disorder (GD), and the understanding of the neurobiological mechanisms underlying GD is limited. Consequently, GD is increasingly recognised as a significant public health concern. Animal models may facilitate a better understanding of the neurobiological basis of GD. However, gambling is a heterogeneous disorder and may perhaps be better understood by animal paradigms aimed at modeling singular domains of dysfunction observed within human gamblers. These domains include excessive cognitive biases or beliefs, increased impulsivity, deficits in cost-benefit decision-making and augmented cue reactivity. Thus, deficits within one of these core areas could be argued to represent a precipitating vulnerability towards the development of GD. Ergo animal models that can parametrise similar deficits could be used to elucidate the neural and neurochemical systems contributing to these perturbations and may be of considerable benefit in clarifying the pathogenesis of GD. Moreover, such information could be useful in aiding the development of novel pharmacotherapies. Here, we discuss examples of animal research in each of these core domains. Initially we present data from the rodent slot machine task that suggests rats, like humans, are susceptible to the near-miss effect, a potent cognitive distortion that has been linked to the severity of GD. Secondly, we discuss several behavioural tasks designed to capture different aspects of impulsivity in rodents. We then consider tasks that measure distorted or nonoptimal decision-making, before reviewing tasks that measure augmented cue reactivity.

Notes

Acknowledgements

This work was supported by operating grants awarded to C.A.W. from the Canadian Institutes of Health Research (CIHR; MOP-89700), Ontario Problem Gambling Research Council, Parkinson Society Canada and the Natural Sciences and Engineering Council of Canada. P.J.C. was funded through a graduate student award from Parkinson Society Canada (PSC). C.A.W. received salary support through the Michael Smith Foundation for Health Research and the Canadian Institutes for Health Research (CIHR) New Investigator Program.

Conflict of interest C.A.W. has previously consulted for Shire on an unrelated matter. Neither P.J.C. nor C.A.W. has any other conflicts of interest or financial disclosures to make.

References

  1. 1.
    Wardle H, Moody A, Spence S, Orford J, Volberg R, Jotangia D, Griffths M, Hussey D, Dobbie F. British Gambling Prevalence Survey 2010. The Gambling Commission. 2010.Google Scholar
  2. 2.
    Gerstein D, Hoffman J, Larison C, Engelam L, Murphy S, Palmer A, Chuchro L, Toce M, Johnson R, Buie T, Hill MA. Gambling Impact and Behavior study. Report to the National Gambling Impact Study Commission. 1999.Google Scholar
  3. 3.
    Black DW, et al. Suicide ideations, suicide attempts, and completed suicide in persons with pathological gambling and their first-degree relatives. Suicide Life Threat Behav. 2015;45(6):700–9.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Petry NM, Kiluk BD. Suicidal ideation and suicide attempts in treatment-seeking pathological gamblers. J Nerv Ment Dis. 2002;190(7):462–9.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Hollander E, Buchalter AJ, DeCaria CM. Pathological gambling. Psychiatr Clin North Am. 2000;23(3):629–42.CrossRefGoogle Scholar
  6. 6.
    Potenza MN. Review. The neurobiology of pathological gambling and drug addiction: an overview and new findings. Philos Trans R Soc Lond Ser B Biol Sci. 2008;363(1507):3181–9.CrossRefGoogle Scholar
  7. 7.
    Potenza MN. Should addictive disorders include non-substance-related conditions? Addiction. 2006;101(Suppl 1):142–51.CrossRefGoogle Scholar
  8. 8.
    Bechara A. Risky business: emotion, decision-making, and addiction. J Gambl Stud. 2003;19(1):23–51.CrossRefGoogle Scholar
  9. 9.
    Martin RJ, et al. Disordered gambling and co-morbidity of psychiatric disorders among college students: an examination of problem drinking, anxiety and depression. J Gambl Stud. 2014;30(2):321–33.CrossRefGoogle Scholar
  10. 10.
    Petry NM. A comparison of treatment-seeking pathological gamblers based on preferred gambling activity. Addiction. 2003;98(5):645–55.CrossRefGoogle Scholar
  11. 11.
    Kirmayer LJ, Crafa D. What kind of science for psychiatry? Front Hum Neurosci. 2014;8:435.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Morris SE, Cuthbert BN. Research Domain Criteria: cognitive systems, neural circuits, and dimensions of behavior. Dialogues Clin Neurosci. 2012;14(1):29–37.PubMedPubMedCentralGoogle Scholar
  13. 13.
    Toneatto T, et al. Cognitive distortions in heavy gambling. J Gambl Stud. 1997;13(3):253–66.CrossRefGoogle Scholar
  14. 14.
    Ladouceur R, et al. Gambling – relationship between the frequency of wins and irrational thinking. J Psychol. 1988;122(4):409–14.CrossRefGoogle Scholar
  15. 15.
    Potenza MN. Impulsivity and compulsivity in pathological gambling and obsessive-compulsive disorder. Rev Bras Psiquiatr. 2007;29(2):105–6.CrossRefGoogle Scholar
  16. 16.
    Rodriguez-Jimenez R, et al. Impulsivity and sustained attention in pathological gamblers: influence of childhood ADHD history. J Gambl Stud. 2006;22(4):451–61.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Lawrence AJ, et al. Problem gamblers share deficits in impulsive decision-making with alcohol-dependent individuals. Addiction. 2009;104(6):1006–15.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Dixon MR, Marley J, Jacobs EA. Delay discounting by pathological gamblers. J Appl Behav Anal. 2003;36(4):449–58.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Petry NM. Pathological gamblers, with and without substance use disorders, discount delayed rewards at high rates. J Abnorm Psychol. 2001;110(3):482–7.CrossRefGoogle Scholar
  20. 20.
    Michalczuk R, et al. Impulsivity and cognitive distortions in pathological gamblers attending the UK National Problem Gambling Clinic: a preliminary report. Psychol Med. 2011;41(12):2625–35.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Linnet J, et al. Dopamine release in ventral striatum during Iowa Gambling Task performance is associated with increased excitement levels in pathological gambling. Addiction. 2011;106(2):383–90.CrossRefGoogle Scholar
  22. 22.
    Goudriaan AE, et al. Decision making in pathological gambling: a comparison between pathological gamblers, alcohol dependents, persons with Tourette syndrome, and normal controls. Brain Res Cogn Brain Res. 2005;23(1):137–51.CrossRefGoogle Scholar
  23. 23.
    Brand M, et al. Decision-making impairments in patients with pathological gambling. Psychiatry Res. 2005;133(1):91–9.CrossRefGoogle Scholar
  24. 24.
    Kushner M, et al. Urge to gamble in a simulated gambling environment. J Gambl Stud. 2008;24(2):219–27.CrossRefGoogle Scholar
  25. 25.
    Kushner MG, et al. Urge to gamble in problem gamblers exposed to a casino environment. J Gambl Stud. 2007;23(2):121–32.CrossRefGoogle Scholar
  26. 26.
    Grant LD, Bowling AC. Gambling attitudes and beliefs predict attentional bias in non-problem gamblers. J Gambl Stud. 2015;31(4):1487–503.CrossRefGoogle Scholar
  27. 27.
    Ciccarelli M, et al. Attentional biases in problem and non-problem gamblers. J Affect Disord. 2016;198:135–41.CrossRefGoogle Scholar
  28. 28.
    Lesieur HR, Blume SB. The South Oaks Gambling Screen (SOGS): a new instrument for the identification of pathological gamblers. Am J Psychiatry. 1987;144(9):1184–8.CrossRefGoogle Scholar
  29. 29.
    Raylu N, Oei TP. The Gambling Related Cognitions Scale (GRCS): development, confirmatory factor validation and psychometric properties. Addiction. 2004;99(6):757–69.CrossRefGoogle Scholar
  30. 30.
    Steenbergh TA, et al. Development and validation of the Gamblers’ Beliefs Questionnaire. Psychol Addict Behav. 2002;16(2):143–9.CrossRefGoogle Scholar
  31. 31.
    Oei TPS, Gordon LM. Psychosocial factors related to gambling abstinence and relapse in members of gamblers anonymous. J Gambl Stud. 2008;24(1):91–105.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Cocker PJ, Winstanley CA. Irrational beliefs, biases and gambling: exploring the role of animal models in elucidating vulnerabilities for the development of pathological gambling. Behav Brain Res. 2015;279:259–73.CrossRefGoogle Scholar
  33. 33.
    Winstanley CA, Cocker PJ, Rogers RD. Dopamine modulates reward expectancy during performance of a slot machine task in rats: evidence for a ‘near-miss’ effect. Neuropsychopharmacology. 2011;36(5):913–25.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Clark L, et al. Gambling near-misses enhance motivation to gamble and recruit win-related brain circuitry. Neuron. 2009;61(3):481–90.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Walker MB. Irrational thinking among slot machine players. J Gambl Stud. 1992;8(3):245–61.CrossRefGoogle Scholar
  36. 36.
    Cote D, et al. Near wins prolong gambling on a video lottery terminal. J Gambl Stud. 2003;19(4):433–8.CrossRefGoogle Scholar
  37. 37.
    Clark L, et al. Physiological responses to near-miss outcomes and personal control during simulated gambling. J Gambl Stud. 2012;28(1):123–37.CrossRefGoogle Scholar
  38. 38.
    Murch WS, Clark L. Games in the brain: neural substrates of gambling addiction. Neuroscientist. 2016;22(5):534–45.CrossRefGoogle Scholar
  39. 39.
    Breen RB, Zimmerman M. Rapid onset of pathological gambling in machine gamblers. J Gambl Stud. 2002;18(1):31–43.CrossRefGoogle Scholar
  40. 40.
    Choliz M. Experimental analysis of the game in pathological gamblers: effect of the immediacy of the reward in slot machines. J Gambl Stud. 2010;26(2):249–56.CrossRefGoogle Scholar
  41. 41.
    Dowling N, Smith D, Thomas T. Electronic gaming machines: are they the ‘crack-cocaine’ of gambling? Addiction. 2005;100(1):33–45.CrossRefGoogle Scholar
  42. 42.
    Chase HW, Clark L. Gambling severity predicts midbrain response to near-miss outcomes. J Neurosci. 2010;30(18):6180–7.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Habib R, Dixon MR. Neurobehavioral evidence for the ‘near-miss’ effect in pathological gamblers. J Exp Anal Behav. 2010;93:313–28.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Schultz W. Predictive reward signal of dopamine neurons. J Neurophysiol. 1998;80(1):1–27.CrossRefGoogle Scholar
  45. 45.
    Robinson TE, Berridge KC. The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev. 1993;18(3):247–91.CrossRefGoogle Scholar
  46. 46.
    Potenza MN. How central is dopamine to pathological gambling or gambling disorder? Front Behav Neurosci. 2013;7:206.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Zack M, Poulos CX. Amphetamine primes motivation to gamble and gambling-related semantic networks in problem gamblers. Neuropsychopharmacology. 2004;29(1):195–207.CrossRefGoogle Scholar
  48. 48.
    Noble EP. Addiction and its reward process through polymorphisms of the D-2 dopamine receptor gene: a review. Eur Psychiatry. 2000;15(2):79–89.CrossRefGoogle Scholar
  49. 49.
    Comings DE, et al. A study of the dopamine D2 receptor gene in pathological gambling. Pharmacogenetics. 1996;6(3):223–34.CrossRefGoogle Scholar
  50. 50.
    Blum K, et al. The D2 dopamine receptor gene as a determinant of reward deficiency syndrome. J R Soc Med. 1996;89:396–400.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Comings DE, et al. The additive effect of neurotransmitter genes in pathological gambling. Clin Genet. 2001;60(2):107–16.CrossRefGoogle Scholar
  52. 52.
    Comings DE, et al. Studies of the 48 bp repeat polymorphism of the DRD4 gene in impulsive, compulsive, addictive behaviors: Tourette syndrome, ADHD, pathological gambling, and substance abuse. Am J Med Genet. 1999;88(4):358–68.CrossRefGoogle Scholar
  53. 53.
    Dodd ML, et al. Pathological gambling caused by drugs used to treat Parkinson disease. Arch Neurol. 2005;62(9):1377–81.CrossRefGoogle Scholar
  54. 54.
    Kimber TE, Thompson PD, Kiley MA. Resolution of dopamine dysregulation syndrome following cessation of dopamine agonist therapy in Parkinson’s disease. J Clin Neurosci. 2008;15(2):205–8.CrossRefGoogle Scholar
  55. 55.
    Cocker PJ, et al. A selective role for dopamine D(4) receptors in modulating reward expectancy in a rodent slot machine task. Biol Psychiatry. 2014;75(10):817–24.CrossRefGoogle Scholar
  56. 56.
    Van Craenenbroeck K, Rondou P, Haegeman G. The dopamine D4 receptor: biochemical and signalling properties. Cell Mol Life Sci. 2010;67(12):1971–86.CrossRefGoogle Scholar
  57. 57.
    Cocker PJ, et al. Activation of dopamine D4 receptors within the anterior cingulate cortex enhances the erroneous expectation of reward on a rat slot machine task. Neuropharmacology. 2016;105:186–95.CrossRefGoogle Scholar
  58. 58.
    Cocker PJ, et al. The agranular and granular insula differentially contribute to gambling-like behavior on a rat slot machine task: effects of inactivation and local infusion of a dopamine D4 agonist on reward expectancy. Psychopharmacology (Berl). 2016;233(17):3135–47.CrossRefGoogle Scholar
  59. 59.
    Weintraub D, Potenza MN. Impulse control disorders in Parkinson’s disease. Curr Neurol Neurosci Rep. 2006;6(4):302–6.CrossRefGoogle Scholar
  60. 60.
    Voon V, Potenza MN, Thomsen T. Medication-related impulse control and repetitive behaviors in Parkinson’s disease. Curr Opin Neurol. 2007;20(4):484–92.CrossRefGoogle Scholar
  61. 61.
    Clark CA, Dagher A. The role of dopamine in risk taking: a specific look at Parkinson’s disease and gambling. Front Behav Neurosci. 2014;8:196.PubMedPubMedCentralGoogle Scholar
  62. 62.
    Cocker PJ, Tremblay M, Kaur S, Winstanley CA. Chronic administration of the dopamine D2/3 agonist ropinirole invigorates performance of a rodent slot machine task, potentially indicative of a less distractable or compulsive-like gambling behaviour. Psychopharmacology. 2017;234:137–53.CrossRefGoogle Scholar
  63. 63.
    Cocker PJ, Lin MY, Tremblay M, Kaur S, Winstanley CA. The ß-adrenoceptor blocker propranolol ameliorates compulsive-like gambling behaviour in a rodent slot machine task: implications for iatrogenic gambling disorder. Eur J Neurosci. 2018.Google Scholar
  64. 64.
    Carlezon WA Jr, Duman RS, Nestler EJ. The many faces of CREB. Trends Neurosci. 2005;28(8):436–45.CrossRefGoogle Scholar
  65. 65.
    Beaulieu JM, Gainetdinov RR, Caron MG. The Akt-GSK-3 signaling cascade in the actions of doparnine. Trends Pharmacol Sci. 2007;28(4):166–72.CrossRefGoogle Scholar
  66. 66.
    Li YC, Gao WJ. GSK-3 beta activity and hyperdopamine-dependent behaviors. Neurosci Biobehav Rev. 2011;35(3):645–54.CrossRefGoogle Scholar
  67. 67.
    Self DW, et al. Involvement of cAMP-dependent protein kinase in the nucleus accumbens in cocaine self-administration and relapse of cocaine-seeking behavior. J Neurosci. 1998;18(5):1848–59.CrossRefGoogle Scholar
  68. 68.
    Nestler EJ, Carlezon WA Jr. The mesolimbic dopamine reward circuit in depression. Biol Psychiatry. 2006;59(12):1151–9.CrossRefGoogle Scholar
  69. 69.
    Miller JS, Tallarida RJ, Unterwald EM. Cocaine-induced hyperactivity and sensitization are dependent on GSK3. Neuropharmacology. 2009;56(8):1116–23.CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Enman NM, Unterwald EM. Inhibition of GSK3 attenuates amphetamine-induced hyperactivity and sensitization in the mouse. Behav Brain Res. 2012;231(1):217–25.CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Kabitzke PA, Silva L, Wiedenmayer C. Norepinephrine mediates contextual fear learning and hippocampal pCREB in juvenile rats exposed to predator odor. Neurobiol Learn Mem. 2011;96(2):166–72.CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Beaulieu JM, et al. An Akt/beta-arrestin 2/PP2A signaling complex mediates dopaminergic neurotransmission and behavior. Cell. 2005;122(2):261–73.CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Rafa D, et al. Effects of optimism on gambling in the rat slot machine task. Behav Brain Res. 2016;300:97–105.CrossRefGoogle Scholar
  74. 74.
    Winstanley CA, Eagle DM, Robbins TW. Behavioral models of impulsivity in relation to ADHD: translation between clinical and preclinical studies. Clin Psychol Rev. 2006;26(4):379–95.CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Verdejo-Garcia A, Lawrence AJ, Clark L. Impulsivity as a vulnerability marker for substance-use disorders: review of findings from high-risk research, problem gamblers and genetic association studies. Neurosci Biobehav Rev. 2008;32(4):777–810.CrossRefGoogle Scholar
  76. 76.
    Chamberlain SR, Sahakian BJ. The neuropsychiatry of impulsivity. Curr Opin Psychiatry. 2007;20(3):255–61.CrossRefGoogle Scholar
  77. 77.
    Moeller FG, et al. Psychiatric aspects of impulsivity. Am J Psychiatry. 2001;158(11):1783–93.CrossRefGoogle Scholar
  78. 78.
    Gullo MJ, Loxton NJ, Dawe S. Impulsivity: four ways five factors are not basic to addiction. Addict Behav. 2014;39(11):1547–56.CrossRefGoogle Scholar
  79. 79.
    Hodgins DC, Holub A. Components of impulsivity in gambling disorder. Int J Ment Health Addict. 2015;13(6):699–711.CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Winstanley CA, et al. Insight into the relationship between impulsivity and substance abuse from studies using animal models. Alcohol Clin Exp Res. 2010;34(8):1306–18.PubMedPubMedCentralGoogle Scholar
  81. 81.
    Young JW, et al. Reverse translation of the rodent 5C-CPT reveals that the impaired attention of people with schizophrenia is similar to scopolamine-induced deficits in mice. Transl Psychiatry. 2013;3:e324.CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Voon V, et al. Measuring “waiting” impulsivity in substance addictions and binge eating disorder in a novel analogue of rodent serial reaction time task. Biol Psychiatry. 2014;75(2):148–55.CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Robbins TW. The 5-choice serial reaction time task: behavioural pharmacology and functional neurochemistry. Psychopharmacology. 2002;163(3–4):362–80.CrossRefGoogle Scholar
  84. 84.
    Cole BJ, Robbins TW. Amphetamine impairs the discriminative performance of rats with dorsal noradrenergic bundle lesions on a 5-choice serial reaction time task: new evidence for central dopaminergic-noradrenergic interactions. Psychopharmacology. 1987;91(4):458–66.CrossRefGoogle Scholar
  85. 85.
    Sulzer D, et al. Mechanisms of neurotransmitter release by amphetamines: a review. Prog Neurobiol. 2005;75(6):406–33.CrossRefGoogle Scholar
  86. 86.
    Winstanley CA. The utility of rat models of impulsivity in developing pharmacotherapies for impulse control disorders. Br J Pharmacol. 2011;164(4):1301–21.CrossRefPubMedPubMedCentralGoogle Scholar
  87. 87.
    Muir JL, Everitt BJ, Robbins TW. The cerebral cortex of the rat and visual attentional function: dissociable effects of mediofrontal, cingulate, anterior dorsolateral, and parietal cortex lesions on a five-choice serial reaction time task. Cereb Cortex. 1996;6(3):470–81.CrossRefGoogle Scholar
  88. 88.
    Chudasama Y, Robbins TW. Dissociable contributions of the orbitofrontal and infralimbic cortex to pavlovian autoshaping and discrimination reversal learning: further evidence for the functional heterogeneity of the rodent frontal cortex. J Neurosci. 2003;23(25):8771–80.CrossRefGoogle Scholar
  89. 89.
    Christakou A, Robbins TW, Everitt BJ. Prefrontal cortical-ventral striatal interactions involved in affective modulation of attentional performance: implications for corticostriatal circuit function. J Neurosci. 2004;24(4):773–80.CrossRefGoogle Scholar
  90. 90.
    Eagle DM, et al. Differential effects of modafinil and methylphenidate on stop-signal reaction time task performance in the rat, and interactions with the dopamine receptor antagonist cis-flupenthixol. Psychopharmacology. 2007;192(2):193–206.CrossRefGoogle Scholar
  91. 91.
    Harrison AA, Everitt BJ, Robbins TW. Central serotonin depletion impairs both the acquisition and performance of a symmetrically reinforced go/no-go conditional visual discrimination. Behav Brain Res. 1999;100(1–2):99–112.CrossRefGoogle Scholar
  92. 92.
    Chamberlain SR, et al. Neurochemical modulation of response inhibition and probabilistic learning in humans. Science. 2006;311(5762):861–3.CrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Eagle DM, et al. Stop-signal reaction-time task performance: role of prefrontal cortex and subthalamic nucleus. Cereb Cortex. 2008;18(1):178–88.CrossRefGoogle Scholar
  94. 94.
    Eichenbaum H, Shedlack KJ, Eckmann KW. Thalamocortical mechanisms in odor-guided behavior. I. Effects of lesions of the mediodorsal thalamic nucleus and frontal cortex on olfactory discrimination in the rat. Brain Behav Evol. 1980;17(4):255–75.CrossRefGoogle Scholar
  95. 95.
    Schoenbaum G, et al. Orbitofrontal lesions in rats impair reversal but not acquisition of go, no-go odor discriminations. Neuroreport. 2002;13(6):885–90.CrossRefGoogle Scholar
  96. 96.
    Eagle DM, Bari A, Robbins TW. The neuropsychopharmacology of action inhibition: cross-species translation of the stop-signal and go/no-go tasks. Psychopharmacology. 2008;199(3):439–56.CrossRefGoogle Scholar
  97. 97.
    Alessi SM, Petry NM. Pathological gambling severity is associated with impulsivity in a delay discounting procedure. Behav Process. 2003;64(3):345–54.CrossRefGoogle Scholar
  98. 98.
    Ainslie G. Specious reward: a behavioral theory of impulsiveness and impulse control. Psychol Bull. 1975;82(4):463–96.CrossRefGoogle Scholar
  99. 99.
    Evenden JL, Ryan CN. The pharmacology of impulsive behaviour in rats: the effects of drugs on response choice with varying delays of reinforcement. Psychopharmacology. 1996;128(2):161–70.CrossRefGoogle Scholar
  100. 100.
    van Gaalen MM, et al. Critical involvement of dopaminergic neurotransmission in impulsive decision making. Biol Psychiatry. 2006;60(1):66–73.CrossRefGoogle Scholar
  101. 101.
    Winstanley CA, et al. Interactions between serotonin and dopamine in the control of impulsive choice in rats: therapeutic implications for impulse control disorders. Neuropsychopharmacology. 2005;30(4):669–82.CrossRefGoogle Scholar
  102. 102.
    Winstanley CA, et al. DeltaFosB induction in orbitofrontal cortex mediates tolerance to cocaine-induced cognitive dysfunction. J Neurosci. 2007;27(39):10497–507.CrossRefGoogle Scholar
  103. 103.
    Winstanley CA, et al. Global 5-HT depletion attenuates the ability of amphetamine to decrease impulsive choice on a delay-discounting task in rats. Psychopharmacology. 2003;170(3):320–31.CrossRefGoogle Scholar
  104. 104.
    Cardinal RN, et al. Impulsive choice induced in rats by lesions of the nucleus accumbens core. Science. 2001;292(5526):2499–501.CrossRefGoogle Scholar
  105. 105.
    Winstanley CA, et al. Contrasting roles of basolateral amygdala and orbitofrontal cortex in impulsive choice. J Neurosci. 2004;24(20):4718–22.CrossRefGoogle Scholar
  106. 106.
    Zeeb FD, Floresco SB, Winstanley CA. Contributions of the orbitofrontal cortex to impulsive choice: interactions with basal levels of impulsivity, dopamine signalling, and reward-related cues. Psychopharmacology. 2010;211(1):87–98.CrossRefGoogle Scholar
  107. 107.
    Floresco SB, et al. Cortico-limbic-striatal circuits subserving different forms of cost-benefit decision making. Cogn Affect Behav Neurosci. 2008;8(4):375–89.CrossRefGoogle Scholar
  108. 108.
    Abela AR, et al. Inhibitory control deficits in rats with ventral hippocampal lesions. Cereb Cortex. 2013;23(6):1396–409.CrossRefGoogle Scholar
  109. 109.
    Abela AR, Chudasama Y. Dissociable contributions of the ventral hippocampus and orbitofrontal cortex to decision-making with a delayed or uncertain outcome. Eur J Neurosci. 2013;37(4):640–7.CrossRefGoogle Scholar
  110. 110.
    Fineberg NA, et al. Probing compulsive and impulsive behaviors, from animal models to endophenotypes: a narrative review. Neuropsychopharmacology. 2010;35(3):591–604.CrossRefGoogle Scholar
  111. 111.
    Fontenelle LF, Mendlowicz MV, Versiani M. Impulse control disorders in patients with obsessive-compulsive disorder. Psychiatry Clin Neurosci. 2005;59(1):30–7.CrossRefGoogle Scholar
  112. 112.
    Blaszczynski A. Pathological gambling and obsessive-compulsive spectrum disorders. Psychol Rep. 1999;84(1):107–13.CrossRefGoogle Scholar
  113. 113.
    Scherrer JF, et al. Associations between obsessive-compulsive classes and pathological gambling in a national cohort of male twins. JAMA Psychiatry. 2015;72(4):342–9.CrossRefGoogle Scholar
  114. 114.
    Zohar J, Insel TR. Obsessive-compulsive disorder: psychobiological approaches to diagnosis, treatment, and pathophysiology. Biol Psychiatry. 1987;22(6):667–87.CrossRefGoogle Scholar
  115. 115.
    Joel D. Current animal models of obsessive compulsive disorder: a critical review. Prog Neuropsychopharmacol Biol Psychiatry. 2006;30(3):374–88.CrossRefGoogle Scholar
  116. 116.
    Joel D, Doljansky J. Selective alleviation of compulsive lever-pressing in rats by D1, but not D2, blockade: possible implications for the involvement of D1 receptors in obsessive-compulsive disorder. Neuropsychopharmacology. 2003;28(1):77–85.CrossRefGoogle Scholar
  117. 117.
    Joel D, et al. Role of the orbital cortex and of the serotonergic system in a rat model of obsessive compulsive disorder. Neuroscience. 2005;130(1):25–36.CrossRefGoogle Scholar
  118. 118.
    Eagle DM, et al. The dopamine D2/D3 receptor agonist quinpirole increases checking-like behaviour in an operant observing response task with uncertain reinforcement: a novel possible model of OCD. Behav Brain Res. 2014;264:207–29.CrossRefPubMedPubMedCentralGoogle Scholar
  119. 119.
    Diskin KM, Hodgins DC. Narrowing of attention and dissociation in pathological video lottery gamblers. J Gambl Stud. 1999;15(1):17–28.CrossRefGoogle Scholar
  120. 120.
    Schüll ND. Addiction by design : machine gambling in Las Vegas. Princeton, NJ: Princeton University Press; 2012. xi, 442pGoogle Scholar
  121. 121.
    Rachlin H. Why do people gamble and keep gambling despite heavy losses. Psychol Sci. 1990;1(5):294–7.CrossRefGoogle Scholar
  122. 122.
    Cavedini P, et al. Frontal lobe dysfunction in pathological gambling patients. Biol Psychiatry. 2002;51(4):334–41.CrossRefPubMedPubMedCentralGoogle Scholar
  123. 123.
    de Visser L, et al. Rodent versions of the iowa gambling task: opportunities and challenges for the understanding of decision-making. Front Neurosci. 2011;5:109.PubMedPubMedCentralGoogle Scholar
  124. 124.
    Zeeb FD, Robbins TW, Winstanley CA. Serotonergic and dopaminergic modulation of gambling behavior as assessed using a novel rat gambling task. Neuropsychopharmacology. 2009;34(10):2329–43.CrossRefGoogle Scholar
  125. 125.
    Bechara A, et al. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition. 1994;50(1–3):7–15.CrossRefGoogle Scholar
  126. 126.
    Bechara A, Tranel D, Damasio H. Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain. 2000;123(Pt 11):2189–202.CrossRefGoogle Scholar
  127. 127.
    Petry NM. Substance abuse, pathological gambling, and impulsiveness. Drug Alcohol Depend. 2001;63(1):29–38.CrossRefPubMedPubMedCentralGoogle Scholar
  128. 128.
    Grant S, Contoreggi C, London ED. Drug abusers show impaired performance in a laboratory test of decision making. Neuropsychologia. 2000;38(8):1180–7.CrossRefGoogle Scholar
  129. 129.
    Bechara A, et al. Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making. J Neurosci. 1999;19(13):5473–81.CrossRefGoogle Scholar
  130. 130.
    Jentsch JD, Taylor JR. Impulsivity resulting from frontostriatal dysfunction in drug abuse: implications for the control of behavior by reward-related stimuli. Psychopharmacology. 1999;146(4):373–90.CrossRefGoogle Scholar
  131. 131.
    Bechara A. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat Neurosci. 2005;8(11):1458–63.CrossRefGoogle Scholar
  132. 132.
    Paine TA, et al. Medial prefrontal cortex lesions impair decision-making on a rodent gambling task: reversal by D1 receptor antagonist administration. Behav Brain Res. 2013;243:247–54.CrossRefPubMedPubMedCentralGoogle Scholar
  133. 133.
    Zeeb FD, Winstanley CA. Lesions of the basolateral amygdala and orbitofrontal cortex differentially affect acquisition and performance of a rodent gambling task. J Neurosci. 2011;31(6):2197–204.CrossRefGoogle Scholar
  134. 134.
    Zeeb FD, Winstanley CA. Functional disconnection of the orbitofrontal cortex and basolateral amygdala impairs acquisition of a rat gambling task and disrupts animals’ ability to alter decision-making behavior after reinforcer devaluation. J Neurosci. 2013;33(15):6434–43.CrossRefGoogle Scholar
  135. 135.
    Pushparaj A, et al. Differential involvement of the agranular vs granular insular cortex in the acquisition and performance of choice behavior in a rodent gambling task. Neuropsychopharmacology. 2015;40(12):2832–42.CrossRefPubMedPubMedCentralGoogle Scholar
  136. 136.
    Baarendse PJ, Vanderschuren LJ. Dissociable effects of monoamine reuptake inhibitors on distinct forms of impulsive behavior in rats. Psychopharmacology. 2012;219(2):313–26.CrossRefGoogle Scholar
  137. 137.
    Baarendse PJ, Winstanley CA, Vanderschuren LJ. Simultaneous blockade of dopamine and noradrenaline reuptake promotes disadvantageous decision making in a rat gambling task. Psychopharmacology. 2013;225(3):719–31.CrossRefGoogle Scholar
  138. 138.
    Zeeb FD, Wong AC, Winstanley CA. Differential effects of environmental enrichment, social-housing, and isolation-rearing on a rat gambling task: dissociations between impulsive action and risky decision-making. Psychopharmacology. 2013;225(2):381–95.CrossRefGoogle Scholar
  139. 139.
    Denk F, et al. Differential involvement of serotonin and dopamine systems in cost-benefit decisions about delay or effort. Psychopharmacology. 2005;179(3):587–96.CrossRefGoogle Scholar
  140. 140.
    Salamone JD, et al. Nucleus accumbens dopamine depletions make animals highly sensitive to high fixed ratio requirements but do not impair primary food reinforcement. Neuroscience. 2001;105(4):863–70.CrossRefGoogle Scholar
  141. 141.
    Nowend KL, et al. D1 or D2 antagonism in nucleus accumbens core or dorsomedial shell suppresses lever pressing for food but leads to compensatory increases in chow consumption. Pharmacol Biochem Behav. 2001;69(3–4):373–82.CrossRefGoogle Scholar
  142. 142.
    Floresco SB, Tse MT, Ghods-Sharifi S. Dopaminergic and glutamatergic regulation of effort- and delay-based decision making. Neuropsychopharmacology. 2008;33(8):1966–79.CrossRefGoogle Scholar
  143. 143.
    Hosking JG, Floresco SB, Winstanley CA. Dopamine antagonism decreases willingness to expend physical, but not cognitive, effort: a comparison of two rodent cost/benefit decision-making tasks. Neuropsychopharmacology. 2015;40(4):1005–15.CrossRefGoogle Scholar
  144. 144.
    Cardinal RN, Howes NJ. Effects of lesions of the nucleus accumbens core on choice between small certain rewards and large uncertain rewards in rats. BMC Neurosci. 2005;6:37.CrossRefPubMedPubMedCentralGoogle Scholar
  145. 145.
    St Onge JR, Floresco SB. Dopaminergic modulation of risk-based decision making. Neuropsychopharmacology. 2009;34(3):681–97.CrossRefGoogle Scholar
  146. 146.
    Simon NW, et al. Balancing risk and reward: a rat model of risky decision making. Neuropsychopharmacology. 2009;34(10):2208–17.CrossRefPubMedPubMedCentralGoogle Scholar
  147. 147.
    Simon NW, et al. Dopaminergic modulation of risky decision-making. J Neurosci. 2011;31(48):17460–70.CrossRefPubMedPubMedCentralGoogle Scholar
  148. 148.
    Cocker PJ, et al. Irrational choice under uncertainty correlates with lower striatal D(2/3) receptor binding in rats. J Neurosci. 2012;32(44):15450–7.CrossRefGoogle Scholar
  149. 149.
    Barrus MM, et al. Inactivation of the orbitofrontal cortex reduces irrational choice on a rodent Betting Task. Neuroscience. 2017;345:38–48.CrossRefGoogle Scholar
  150. 150.
    Tremblay M, et al. Dissociable effects of basolateral amygdala lesions on decision making biases in rats when loss or gain is emphasized. Cogn Affect Behav Neurosci. 2014;14(4):1184–95.CrossRefGoogle Scholar
  151. 151.
    Volkow ND, et al. Imaging dopamine’s role in drug abuse and addiction. Neuropharmacology. 2009;56(Suppl 1):3–8.CrossRefGoogle Scholar
  152. 152.
    Dalley JW, et al. Nucleus Accumbens D2/3 receptors predict trait impulsivity and cocaine reinforcement. Science. 2007;315(5816):1267–70.CrossRefPubMedPubMedCentralGoogle Scholar
  153. 153.
    Tremblay M, et al. Chronic D2/3 agonist ropinirole treatment increases preference for uncertainty in rats regardless of baseline choice patterns. Eur J Neurosci. 2017;45(1):159–66.CrossRefGoogle Scholar
  154. 154.
    Robinson TE, Berridge KC. Addiction. Annu Rev Psychol. 2003;54:25–53.CrossRefGoogle Scholar
  155. 155.
    Robinson TE, Berridge KC. Incentive-sensitization and addiction. Addiction. 2001;96(1):103–14.CrossRefGoogle Scholar
  156. 156.
    Field M, Cox WM. Attentional bias in addictive behaviors: a review of its development, causes, and consequences. Drug Alcohol Depend. 2008;97(1–2):1–20.CrossRefGoogle Scholar
  157. 157.
    Loba P, et al. Manipulations of the features of standard video lottery terminal (VLT) games: effects in pathological and non-pathological gamblers. J Gambl Stud. 2001;17(4):297–320.CrossRefGoogle Scholar
  158. 158.
    Honsi A, et al. Attentional bias in problem gambling: a systematic review. J Gambl Stud. 2013;29(3):359–75.CrossRefGoogle Scholar
  159. 159.
    Barrus MM, Cherkasova M, Winstanley CA. Skewed by cues? The motivational role of audiovisual stimuli in modelling substance use and gambling disorders. Curr Top Behav Neurosci. 2016;27:507–29.CrossRefGoogle Scholar
  160. 160.
    Barrus MM, Winstanley CA. Dopamine D3 receptors modulate the ability of win-paired cues to increase risky choice in a rat gambling task. J Neurosci. 2016;36(3):785–94.CrossRefGoogle Scholar
  161. 161.
    Di Ciano P, et al. The impact of selective dopamine D2, D3 and D4 ligands on the rat gambling task. PLoS One. 2015;10(9):e0136267.CrossRefPubMedPubMedCentralGoogle Scholar
  162. 162.
    Heidbreder CA, Newman AH. Current perspectives on selective dopamine D(3) receptor antagonists as pharmacotherapeutics for addictions and related disorders. Ann N Y Acad Sci. 2010;1187:4–34.CrossRefPubMedPubMedCentralGoogle Scholar
  163. 163.
    Le Foll B, Goldberg SR, Sokoloff P. The dopamine D3 receptor and drug dependence: effects on reward or beyond? Neuropharmacology. 2005;49(4):525–41.CrossRefGoogle Scholar
  164. 164.
    Xi ZX, et al. Blockade of mesolimbic dopamine D3 receptors inhibits stress-induced reinstatement of cocaine-seeking in rats. Psychopharmacology. 2004;176(1):57–65.CrossRefPubMedPubMedCentralGoogle Scholar
  165. 165.
    Higley AE, et al. Dopamine D(3) receptor antagonist SB-277011A inhibits methamphetamine self-administration and methamphetamine-induced reinstatement of drug-seeking in rats. Eur J Pharmacol. 2011;659(2–3):187–92.CrossRefPubMedPubMedCentralGoogle Scholar
  166. 166.
    Berridge KC, Robinson TE. What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Res Brain Res Rev. 1998;28(3):309–69.CrossRefGoogle Scholar
  167. 167.
    Flagel SB, et al. Individual differences in the propensity to approach signals vs goals promote different adaptations in the dopamine system of rats. Psychopharmacology. 2007;191(3):599–607.CrossRefGoogle Scholar
  168. 168.
    Flagel SB, et al. A selective role for dopamine in stimulus-reward learning. Nature. 2011;469(7328):53–7.CrossRefGoogle Scholar
  169. 169.
    Di Ciano P, et al. Differential involvement of NMDA, AMPA/kainate, and dopamine receptors in the nucleus accumbens core in the acquisition and performance of pavlovian approach behavior. J Neurosci. 2001;21(23):9471–7.CrossRefGoogle Scholar
  170. 170.
    Berridge KC, Robinson TE. Parsing reward. Trends Neurosci. 2003;26(9):507–13.CrossRefGoogle Scholar
  171. 171.
    Dickinson A, Smith J, Mirenowicz J. Dissociation of Pavlovian and instrumental incentive learning under dopamine antagonists. Behav Neurosci. 2000;114(3):468–83.CrossRefGoogle Scholar
  172. 172.
    Wolterink G, et al. Relative roles of ventral striatal D1 and D2 dopamine receptors in responding with conditioned reinforcement. Psychopharmacology. 1993;110(3):355–64.CrossRefGoogle Scholar
  173. 173.
    Beninger RJ, Ranaldi R. The effects of amphetamine, apomorphine, Skf-38393, quinpirole and bromocriptine on responding for conditioned reward in rats. Behav Pharmacol. 1992;3(2):155–63.CrossRefGoogle Scholar
  174. 174.
    Beninger RJ, Phillips AG. The effects of pimozide during pairing on the transfer of classical conditioning to an operant discrimination. Pharmacol Biochem Behav. 1981;14(1):101–5.CrossRefGoogle Scholar
  175. 175.
    Fletcher PJ, Higgins GA. Differential effects of ondansetron and alpha-flupenthixol on responding for conditioned reward. Psychopharmacology. 1997;134(1):64–72.CrossRefGoogle Scholar
  176. 176.
    Sutton MA, Rolfe NG, Beninger RJ. Biphasic effects of 7-OH-DPAT on the acquisition of responding for conditioned reward in rats. Pharmacol Biochem Behav. 2001;69(1–2):195–200.CrossRefGoogle Scholar
  177. 177.
    Day JJ, et al. Nucleus accumbens neurons encode Pavlovian approach behaviors: evidence from an autoshaping paradigm. Eur J Neurosci. 2006;23(5):1341–51.CrossRefGoogle Scholar
  178. 178.
    Hall J, et al. Involvement of the central nucleus of the amygdala and nucleus accumbens core in mediating Pavlovian influences on instrumental behaviour. Eur J Neurosci. 2001;13(10):1984–92.CrossRefGoogle Scholar
  179. 179.
    Taylor JR, Robbins TW. Enhanced behavioural control by conditioned reinforcers following microinjections of d-amphetamine into the nucleus accumbens. Psychopharmacology. 1984;84(3):405–12.CrossRefGoogle Scholar
  180. 180.
    Everitt BJ, Wolf ME. Psychomotor stimulant addiction: a neural systems perspective. J Neurosci. 2002;22(9):3312–20.CrossRefGoogle Scholar
  181. 181.
    Cocker PJ, Vonder Haar C, Winstanley CA. Elucidating the role of D4 receptors in mediating attributions of salience to incentive stimuli on Pavlovian conditioned approach and conditioned reinforcement paradigms. Behav Brain Res. 2016;312:55–63.CrossRefGoogle Scholar
  182. 182.
    Fraser KM, et al. Examining the role of dopamine D2 and D3 receptors in Pavlovian conditioned approach behaviors. Behav Brain Res. 2016;305:87–99.CrossRefPubMedPubMedCentralGoogle Scholar
  183. 183.
    Zeeb FD, et al. Low impulsive action, but not impulsive choice, predicts greater conditioned reinforcer salience and augmented nucleus accumbens dopamine release. Neuropsychopharmacology. 2016;41(8):2091–100.CrossRefPubMedPubMedCentralGoogle Scholar
  184. 184.
    Yang Y, et al. Positive association between trait impulsivity and high gambling-related cognitive biases among college students. Psychiatry Res. 2016;243:71–4.CrossRefGoogle Scholar
  185. 185.
    Marsden CA. Dopamine: the rewarding years. Br J Pharmacol. 2006;147(Suppl 1):S136–44.PubMedPubMedCentralGoogle Scholar
  186. 186.
    Jaber M, et al. Dopamine receptors and brain function. Neuropharmacology. 1996;35(11):1503–19.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of British ColumbiaVancouverCanada
  2. 2.Department of PsychologyUniversity of CambridgeCambridgeUK

Personalised recommendations