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Computational Mechanisms of Addiction: Recent Evidence and Its Relevance to Addiction Medicine

  • Neuroscience & Addiction (A Haghparast and H Ekhtiari, Section Editor)
  • Published:
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Abstract

Purpose of Review

In this article, we provide a brief review of recent computational modelling studies of substance use disorders (SUDs), with a focus on work published within the last 5 years. While reinforcement learning (RL) approaches are most prominent in recent studies, we also review work from other perspectives that focus on Bayesian (active) inference and perceptual processing.

Recent Findings

Recent work in RL shows evidence that goal-directed (model-based) planning processes are impaired in SUDs, leading to impulsive, habitual decision processes focused on short-term reward despite long-term negative consequences. Bayesian approaches offer a complementary perspective, suggesting that drug-induced overconfidence in prior expectations prevents substance users from appropriately updating their beliefs in the face of negative outcomes. Recent neurocomputational studies have shown promise in differentiating those who will and will not relapse.

Summary

Computational modelling has made progress in identifying specific prospective decision-making processes that are impaired in SUDs, but further research is necessary before these approaches can directly inform medical practice on an individualized level.

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References

Papers of particular interest, published recently, have been highlighted as: •• Of major importance

  1. Suzuki S, Kober H. Substance-related and addictive disorders. In: Butcher J, Hooley J, Kendall P, editors. APA handbook of psychopathology: psychopathology: understanding, assessing, and treating adult mental disorders. 1st ed. Washington, DC: American Psychological Association; 2018. p. 481–506.

    Chapter  Google Scholar 

  2. NIMH. National Comorbidity Survey: lifetime prevalence estimates. National Institute of Mental Health; 2007. Available from: http://www.hcp.med.harvard.edu/ncs/.

  3. Passetti F, Clark L, Mehta MA, Joyce E, King M. Neuropsychological predictors of clinical outcome in opiate addiction. Drug Alcohol Depend. 2008;94(1–3):82–91.

    Article  CAS  PubMed  Google Scholar 

  4. Verdejo-Garcia A, Chong TT, Stout JC, Yucel M, London ED. Stages of dysfunctional decision-making in addiction. Pharmacol Biochem Behav. 2018;164:99–105.

    Article  CAS  PubMed  Google Scholar 

  5. Connery HS. Medication-assisted treatment of opioid use disorder: review of the evidence and future directions. Harv Rev Psychiatry. 2015;23(2):63–75.

    Article  PubMed  Google Scholar 

  6. Hser YI, Saxon AJ, Huang D, Hasson A, Thomas C, Hillhouse M, et al. Treatment retention among patients randomized to buprenorphine/naloxone compared to methadone in a multi-site trial. Addiction. 2014;109(1):79–87.

    Article  PubMed  Google Scholar 

  7. Liu S, Dolan RJ, Heinz A. Translation of computational psychiatry in the context of addiction. JAMA Psychiatry. 2020.

  8. Huys QJ, Maia TV, Frank MJ. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci. 2016;19(3):404–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Shuvaev SA, Tran NB, Stephenson-Jones M, Li B, Koulakov AA. Neural networks with motivation. Front Syst Neurosci. 2020;14:609316.

    Article  PubMed  Google Scholar 

  10. Gutkin BS, Dehaene S, Changeux JP. A neurocomputational hypothesis for nicotine addiction. Proc Natl Acad Sci U S A. 2006;103(4):1106–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sutton R, Barto A. Reinforcement learning: an introduction. 1998.

  12. Schultz W. Dopamine reward prediction error coding. Dialogues Clin Neurosci. 2016;18(1):23–32.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Knill DC, Pouget A. The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 2004;27(12):712–9.

    Article  CAS  PubMed  Google Scholar 

  14. Gershman S, Blei D. A tutorial on Bayesian nonparametric models. J Math Psychol. 2012;56:1–12.

    Article  Google Scholar 

  15. Lee MD, Wagenmakers EJ. Bayesian cognitive modeling: a practical course: Cambridge university press; 2014.

  16. Da Costa L, Parr T, Sajid N, Veselic S, Neacsu V, Friston KJ. Active inference on discrete state-spaces: a synthesis. J Math Psychol. 2020;99:102447.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Smith R, Friston K, Whyte C. A step-by-step tutorial on active inference and its application to empirical data. PsyArXiv. 2021. https://doi.org/10.31234/osf.io/b4jm6.

  18. Berridge KC. From prediction error to incentive salience: mesolimbic computation of reward motivation. Eur J Neurosci. 2012;35(7):1124–43.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zhang J, Berridge KC, Tindell AJ, Smith KS, Aldridge JW. A neural computational model of incentive salience. PLoS Comput Biol. 2009;5(7):e1000437.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Konova AB, Louie K, Glimcher PW. The computational form of craving is a selective multiplication of economic value. Proc Natl Acad Sci U S A. 2018;115(16):4122–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Friston K. Policies and priors. In: B G, S A, editors. Computational neuroscience of drug addiction. Springer Series in Computational Neuroscience. New York, NY: Springer; 2012.

  22. Miller M, Kiverstein J, Rietveld E. Embodying addiction: a predictive processing account. Brain Cogn. 2020;138:105495.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Schwartenbeck P, FitzGerald TH, Mathys C, Dolan R, Wurst F, Kronbichler M, et al. Optimal inference with suboptimal models: addiction and active Bayesian inference. Med Hypotheses. 2015;84(2):109–17.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Smith R, Kuplicki R, Feinstein J, Forthman KL, Stewart JL, Paulus MP, et al. A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders. PLoS Comput Biol. 2020;16(12):e1008484. This study applied a Bayesian model to a heartbeat tapping task to estimate prior expectations and beliefs about interoceptive signal precision. Both healthy controls and substance users were similar at rest, but individuals with SUDs could not update beliefs about the precision (reliability) of interoceptive signals during a non-invasive interoceptive perturbation — suggesting their beliefs are insensitive to changes in physiological states.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gu X, Filbey F. A Bayesian observer model of drug craving. JAMA Psychiat. 2017;74(4):419–20.

    Article  Google Scholar 

  26. Keramati M, Gutkin B. Homeostatic reinforcement learning for integrating reward collection and physiological stability. Elife. 2014;3.

  27. Keramati M, Durand A, Girardeau P, Gutkin B, Ahmed SH. Cocaine addiction as a homeostatic reinforcement learning disorder. Psychol Rev. 2017;124(2):130–53.

    Article  PubMed  Google Scholar 

  28. Keramati M, Ahmed SH, Gutkin BS. Misdeed of the need: towards computational accounts of transition to addiction. Curr Opin Neurobiol. 2017;46:142–53.

    Article  CAS  PubMed  Google Scholar 

  29. Harle KM, Zhang S, Ma N, Yu AJ, Paulus MP. Reduced neural recruitment for Bayesian adjustment of inhibitory control in methamphetamine dependence. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(5):448–59.

    PubMed  PubMed Central  Google Scholar 

  30. Petry NM, Bickel WK, Arnett M. Shortened time horizons and insensitivity to future consequences in heroin addicts. Addiction. 1998;93(5):729–38.

    Article  CAS  PubMed  Google Scholar 

  31. Amlung M, Vedelago L, Acker J, Balodis I, MacKillop J. Steep delay discounting and addictive behavior: a meta-analysis of continuous associations. Addiction. 2017;112(1):51–62.

    Article  PubMed  Google Scholar 

  32. MacKillop J, Amlung MT, Few LR, Ray LA, Sweet LH, Munafo MR. Delayed reward discounting and addictive behavior: a meta-analysis. Psychopharmacology. 2011;216(3):305–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Dezfouli A, Piray P, Keramati MM, Ekhtiari H, Lucas C, Mokri A. A neurocomputational model for cocaine addiction. Neural Comput. 2009;21(10):2869–93.

    Article  PubMed  Google Scholar 

  34. Ersche KD, Gillan CM, Jones PS, Williams GB, Ward LH, Luijten M, et al. Carrots and sticks fail to change behavior in cocaine addiction. Science. 2016;352(6292):1468–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ersche KD, Roiser JP, Abbott S, Craig KJ, Muller U, Suckling J, et al. Response perseveration in stimulant dependence is associated with striatal dysfunction and can be ameliorated by a D(2/3) receptor agonist. Biol Psychiatry. 2011;70(8):754–62.

    Article  CAS  PubMed  Google Scholar 

  36. Myers CE, Sheynin J, Balsdon T, Luzardo A, Beck KD, Hogarth L, et al. Probabilistic reward- and punishment-based learning in opioid addiction: experimental and computational data. Behav Brain Res. 2016;296:240–8.

    Article  PubMed  Google Scholar 

  37. Kanen JW, Ersche KD, Fineberg NA, Robbins TW, Cardinal RN. Computational modelling reveals contrasting effects on reinforcement learning and cognitive flexibility in stimulant use disorder and obsessive-compulsive disorder: remediating effects of dopaminergic D2/3 receptor agents. Psychopharmacology. 2019;236(8):2337–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Konova AB, Lopez-Guzman S, Urmanche A, Ross S, Louie K, Rotrosen J, et al. Computational markers of risky decision-making for identification of temporal windows of vulnerability to opioid use in a real-world clinical setting. JAMA Psychiatry. 2019.

  39. Genauck A, Quester S, Wustenberg T, Morsen C, Heinz A, Romanczuk-Seiferth N. Reduced loss aversion in pathological gambling and alcohol dependence is associated with differential alterations in amygdala and prefrontal functioning. Sci Rep. 2017;7(1):16306.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Fridberg DJ, Queller S, Ahn WY, Kim W, Bishara AJ, Busemeyer JR, et al. Cognitive mechanisms underlying risky decision-making in chronic cannabis users. J Math Psychol. 2010;54(1):28–38.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ahn WY, Vasilev G, Lee SH, Busemeyer JR, Kruschke JK, Bechara A, et al. Decision-making in stimulant and opiate addicts in protracted abstinence: evidence from computational modeling with pure users. Front Psychol. 2014;5:849.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Lopez-Guzman S, Konova AB, Glimcher PW. Computational psychiatry of impulsivity and risk: how risk and time preferences interact in health and disease. Philos Trans R Soc Lond B Biol Sci. 2019;374(1766):20180135.

    Article  PubMed  Google Scholar 

  43. Voon V, Morris LS, Irvine MA, Ruck C, Worbe Y, Derbyshire K, et al. Risk-taking in disorders of natural and drug rewards: neural correlates and effects of probability, valence, and magnitude. Neuropsychopharmacology. 2015;40(4):804–12.

    Article  PubMed  Google Scholar 

  44. Smith R, Kirlic N, Stewart JL, Touthang J, Kuplicki R, Khalsa SS, et al. Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modeling approach. J Psychiatry Neurosci. 2021;46(1):E74–87. This study asked healthy controls and individuals with SUDs to complete an approach-avoidance conflict task in which they decided whether to approach a negative stimulus in order to win points or to avoid a negative stimulus and receive no points. An active inference model showed that substance users had greater decision uncertainty but reduced avoidance behavior. This effect was also replicated at a 1-year follow-up (45).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Smith R, Kirlic N, Stewart J, Touthang J, Kuplicki R, McDermott T, et al. Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample. Sci Rep. 2021;11.

  46. Gowin JL, Mackey S, Paulus MP. Altered risk-related processing in substance users: imbalance of pain and gain. Drug Alcohol Depend. 2013;132(1–2):13–21.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Simons JS, Dvorak RD, Batien BD. Methamphetamine use in a rural college population: associations with marijuana use, sensitivity to punishment, and sensitivity to reward. Psychol Addict Behav. 2008;22(3):444–9.

    Article  PubMed  Google Scholar 

  48. Simons JS, Arens AM. Moderating effects of sensitivity to punishment and sensitivity to reward on associations between marijuana effect expectancies and use. Psychol Addict Behav. 2007;21(3):409–14.

    Article  PubMed  Google Scholar 

  49. Stewart JL, May AC, Poppa T, Davenport PW, Tapert SF, Paulus MP. You are the danger: attenuated insula response in methamphetamine users during aversive interoceptive decision-making. Drug Alcohol Depend. 2014;142:110–9.

    Article  CAS  PubMed  Google Scholar 

  50. Hester R, Bell RP, Foxe JJ, Garavan H. The influence of monetary punishment on cognitive control in abstinent cocaine-users. Drug Alcohol Depend. 2013;133(1):86–93.

    Article  PubMed  PubMed Central  Google Scholar 

  51. •• Smith R, Schwartenbeck P, Stewart JL, Kuplicki R, Ekhtiari H, Investigators T, et al. Imprecise action selection in substance use disorder: evidence for active learning impairments when solving the explore-exploit dilemma. Drug Alcohol Depend. 2020;215:108208. This study applied an active inference model to behavior on a three-armed bandit reward learning task. The model revealed that, compared to healthy controls, individuals with SUDs had slower learning rates for losses and faster learning rates from wins. This suggested that behavior change in SUDs was slower in response to negative consequences.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Piray P, Keramati MM, Dezfouli A, Lucas C, Mokri A. Individual differences in nucleus accumbens dopamine receptors predict development of addiction-like behavior: a computational approach. Neural Comput. 2010;22(9):2334–68.

    Article  PubMed  Google Scholar 

  53. Morris LS, Baek K, Kundu P, Harrison NA, Frank MJ, Voon V. Biases in the explore-exploit tradeoff in addictions: the role of avoidance of uncertainty. Neuropsychopharmacology. 2016;41(4):940–8.

    Article  PubMed  Google Scholar 

  54. Luscher C, Robbins TW, Everitt BJ. The transition to compulsion in addiction. Nat Rev Neurosci. 2020;21(5):247–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Sjoerds Z, de Wit S, van den Brink W, Robbins TW, Beekman AT, Penninx BW, et al. Behavioral and neuroimaging evidence for overreliance on habit learning in alcohol-dependent patients. Transl Psychiatry. 2013;3:e337.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Hogarth L. Addiction is driven by excessive goal-directed drug choice under negative affect: translational critique of habit and compulsion theory. Neuropsychopharmacology. 2020;45(5):720–35.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Luijten M, Gillan CM, de Wit S, Franken IHA, Robbins TW, Ersche KD. Goal-directed and habitual control in smokers. Nicotine Tob Res. 2020;22(2):188–95.

    Article  PubMed  Google Scholar 

  58. van Timmeren T, Quail SL, Balleine BW, Geurts DEM, Goudriaan AE, van Holst RJ. Intact corticostriatal control of goal-directed action in alcohol use disorder: a Pavlovian-to-instrumental transfer and outcome-devaluation study. Sci Rep. 2020;10(1):4949.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Lim TV, Cardinal RN, Savulich G, Jones PS, Moustafa AA, Robbins TW, et al. Impairments in reinforcement learning do not explain enhanced habit formation in cocaine use disorder. Psychopharmacology. 2019;236(8):2359–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Dolan R, Dayan P. Goals and habits in the brain. Neuron. 2013;80:312–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Daw N, Niv Y, Dayan P. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nat Neurosci. 2005;8:1704–11.

    Article  CAS  PubMed  Google Scholar 

  62. Nebe S, Kroemer NB, Schad DJ, Bernhardt N, Sebold M, Muller DK, et al. No association of goal-directed and habitual control with alcohol consumption in young adults. Addict Biol. 2018;23(1):379–93.

    Article  PubMed  Google Scholar 

  63. Everitt BJ, Robbins TW. Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nat Neurosci. 2005;8(11):1481–9.

    Article  CAS  PubMed  Google Scholar 

  64. Everitt BJ, Robbins TW. Drug addiction: updating actions to habits to compulsions ten years on. Annu Rev Psychol. 2016;67:23–50.

    Article  PubMed  Google Scholar 

  65. Obst E, Schad DJ, Huys QJ, Sebold M, Nebe S, Sommer C, et al. Drunk decisions: alcohol shifts choice from habitual towards goal-directed control in adolescent intermediate-risk drinkers. J Psychopharmacol. 2018;32(8):855–66.

    Article  PubMed  Google Scholar 

  66. Voon V, Derbyshire K, Ruck C, Irvine MA, Worbe Y, Enander J, et al. Disorders of compulsivity: a common bias towards learning habits. Mol Psychiatry. 2015;20(3):345–52.

    Article  CAS  PubMed  Google Scholar 

  67. Sebold M, Deserno L, Nebe S, Schad DJ, Garbusow M, Hagele C, et al. Model-based and model-free decisions in alcohol dependence. Neuropsychobiology. 2014;70(2):122–31.

    Article  CAS  PubMed  Google Scholar 

  68. Donamayor N, Strelchuk D, Baek K, Banca P, Voon V. The involuntary nature of binge drinking: goal directedness and awareness of intention. Addict Biol. 2018;23(1):515–26.

    Article  PubMed  Google Scholar 

  69. Reiter AM, Deserno L, Kallert T, Heinze HJ, Heinz A, Schlagenhauf F. Behavioral and neural signatures of reduced updating of alternative options in alcohol-dependent patients during flexible decision-making. J Neurosci. 2016;36(43):10935–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Sebold M, Nebe S, Garbusow M, Guggenmos M, Schad DJ, Beck A, et al. When habits are dangerous: alcohol expectancies and habitual decision making predict relapse in alcohol dependence. Biol Psychiatry. 2017;82(11):847–56.

    Article  PubMed  Google Scholar 

  71. Wyckmans F, Otto AR, Sebold M, Daw N, Bechara A, Saeremans M, et al. Reduced model-based decision-making in gambling disorder. Sci Rep. 2019;9(1):19625.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Sweis BM, Redish AD, Thomas MJ. Prolonged abstinence from cocaine or morphine disrupts separable valuations during decision conflict. Nat Commun. 2018;9(1):2521.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Cartoni E, Balleine B, Baldassarre G. Appetitive Pavlovian-instrumental transfer: a review. Neurosci Biobehav Rev. 2016;71:829–48.

    Article  PubMed  Google Scholar 

  74. Garbusow M, Schad DJ, Sebold M, Friedel E, Bernhardt N, Koch SP, et al. Pavlovian-to-instrumental transfer effects in the nucleus accumbens relate to relapse in alcohol dependence. Addict Biol. 2016;21(3):719–31.

    Article  CAS  PubMed  Google Scholar 

  75. Garbusow M, Schad DJ, Sommer C, Junger E, Sebold M, Friedel E, et al. Pavlovian-to-instrumental transfer in alcohol dependence: a pilot study. Neuropsychobiology. 2014;70(2):111–21.

    Article  CAS  PubMed  Google Scholar 

  76. Lamb RJ, Schindler CW, Pinkston JW. Conditioned stimuli’s role in relapse: preclinical research on Pavlovian-instrumental-transfer. Psychopharmacology. 2016;233(10):1933–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Sebold M, Schad DJ, Nebe S, Garbusow M, Junger E, Kroemer NB, et al. Don’t think, just feel the music: individuals with strong Pavlovian-to-instrumental transfer effects rely less on model-based reinforcement learning. J Cogn Neurosci. 2016;28(7):985–95.

    Article  PubMed  Google Scholar 

  78. Daw N, Gershman S, Seymour B, Dayan P, Dolan R. Model-based influences on humans’ choices and striatal prediction errors. Neuron. 2011;69:1204–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Redish AD. Addiction as a computational process gone awry. Science. 2004;306(5703):1944–7.

    Article  CAS  PubMed  Google Scholar 

  80. Panlilio LV, Thorndike EB, Schindler CW. Blocking of conditioning to a cocaine-paired stimulus: testing the hypothesis that cocaine perpetually produces a signal of larger-than-expected reward. Pharmacol Biochem Behav. 2007;86(4):774–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Chiu PH, Lohrenz TM, Montague PR. Smokers’ brains compute, but ignore, a fictive error signal in a sequential investment task. Nat Neurosci. 2008;11(4):514–20.

    Article  CAS  PubMed  Google Scholar 

  82. Deserno L, Beck A, Huys QJ, Lorenz RC, Buchert R, Buchholz HG, et al. Chronic alcohol intake abolishes the relationship between dopamine synthesis capacity and learning signals in the ventral striatum. Eur J Neurosci. 2015;41(4):477–86.

    Article  PubMed  Google Scholar 

  83. Gradin VB, Baldacchino A, Balfour D, Matthews K, Steele JD. Abnormal brain activity during a reward and loss task in opiate-dependent patients receiving methadone maintenance therapy. Neuropsychopharmacology. 2014;39(4):885–94.

    Article  CAS  PubMed  Google Scholar 

  84. Park SQ, Kahnt T, Beck A, Cohen MX, Dolan RJ, Wrase J, et al. Prefrontal cortex fails to learn from reward prediction errors in alcohol dependence. J Neurosci. 2010;30(22):7749–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Rose EJ, Ross TJ, Salmeron BJ, Lee M, Shakleya DM, Huestis M, et al. Chronic exposure to nicotine is associated with reduced reward-related activity in the striatum but not the midbrain. Biol Psychiatry. 2012;71(3):206–13.

    Article  CAS  PubMed  Google Scholar 

  86. Rose EJ, Salmeron BJ, Ross TJ, Waltz J, Schweitzer JB, McClure SM, et al. Temporal difference error prediction signal dysregulation in cocaine dependence. Neuropsychopharmacology. 2014;39(7):1732–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Tanabe J, Reynolds J, Krmpotich T, Claus E, Thompson LL, Du YP, et al. Reduced neural tracking of prediction error in substance-dependent individuals. Am J Psychiatry. 2013;170(11):1356–63.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Huys QJM, Deserno L, Obermayer K, Schlagenhauf F, Heinz A. Model-free temporal-difference learning and dopamine in alcohol dependence: examining concepts from theory and animals in human imaging. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(5):401–10.

    PubMed  Google Scholar 

  89. Baker TE, Zeighami Y, Dagher A, Holroyd CB. Smoking decisions: altered reinforcement learning signals induced by nicotine state. Nicotine Tob Res. 2020;22(2):164–71.

    PubMed  Google Scholar 

  90. Wang JM, Zhu L, Brown VM, De La Garza R 3rd, Newton T, King-Casas B, et al. In cocaine dependence, neural prediction errors during loss avoidance are increased with cocaine deprivation and predict drug use. Biol Psychiatry CognNeurosci Neuroimaging. 2019;4(3):291–9.

  91. Zhukovsky P, Puaud M, Jupp B, Sala-Bayo J, Alsio J, Xia J, et al. Withdrawal from escalated cocaine self-administration impairs reversal learning by disrupting the effects of negative feedback on reward exploitation: a behavioral and computational analysis. Neuropsychopharmacology. 2019;44(13):2163–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Beylergil SB, Beck A, Deserno L, Lorenz RC, Rapp MA, Schlagenhauf F, et al. Dorsolateral prefrontal cortex contributes to the impaired behavioral adaptation in alcohol dependence. Neuroimage Clin. 2017;15:80–94.

    Article  PubMed  PubMed Central  Google Scholar 

  93. Ognibene D, Fiore VG, Gu X. Addiction beyond pharmacological effects: the role of environment complexity and bounded rationality. Neural Netw. 2019;116:269–78.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Schad DJ, Rapp MA, Garbusow M, Nebe S, Sebold M, Obst E, et al. Dissociating neural learning signals in human sign- and goal-trackers. Nat Hum Behav. 2020;4(2):201–14.

    Article  PubMed  Google Scholar 

  95. Sarter M, Phillips KB. The neuroscience of cognitive-motivational styles: sign- and goal-trackers as animal models. Behav Neurosci. 2018;132(1):1–12.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Grosskurth ED, Bach DR, Economides M, Huys QJM, Holper L. No substantial change in the balance between model-free and model-based control via training on the two-step task. PLoS Comput Biol. 2019;15(11):e1007443.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Redish AD, Jensen S, Johnson A. A unified framework for addiction: vulnerabilities in the decision process. Behav Brain Sci. 2008;31(4):415–37; discussion 37-87.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Brown VM, Chen J, Gillan CM, Price RB. Improving the reliability of computational analyses: model-based planning and its relationship with compulsivity. Biol Psychiatry Cogn Neurosci Neuroimaging. 2020;5(6):601–9.

    PubMed  PubMed Central  Google Scholar 

  99. Heinz A, Deserno L, Zimmermann US, Smolka MN, Beck A, Schlagenhauf F. Targeted intervention: computational approaches to elucidate and predict relapse in alcoholism. Neuroimage. 2017;151:33–44.

    Article  PubMed  Google Scholar 

  100. Fiore VG, Ognibene D, Adinoff B, Gu X. A multilevel computational characterization of endophenotypes in addiction. eNeuro. 2018;5(4).

  101. •• Harle KM, Yu AJ, Paulus MP. Bayesian computational markers of relapse in methamphetamine dependence. Neuroimage Clin. 2019;22:101794. Using a Bayesian model, this study found that sustained abstinence was successfully predicted by differences in neural responses associated with prediction errors. Those who later relapsed showed weaker neural responses associated with prediction errors than those who did not. This suggests that individuals at risk for relapse may have greater difficulties updating beliefs.

    Article  PubMed  PubMed Central  Google Scholar 

  102. Yu JC, Fiore VG, Briggs RW, Braud J, Rubia K, Adinoff B, et al. An insula-driven network computes decision uncertainty and promotes abstinence in chronic cocaine users. Eur J Neurosci. 2020;52(12):4923–36.

    Article  PubMed  Google Scholar 

  103. Konova AB, Lopez-Guzman S, Urmanche A, Ross S, Louie K, Rotrosen J, et al. Computational markers of risky decision-making for identification of temporal windows of vulnerability to opioid use in a real-world clinical setting. JAMA Psychiat. 2020;77(4):368–77.

    Article  Google Scholar 

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Funding

This study is supported by the William K. Warren Foundation (RS and ST), the Stewart G. Wolf scholarship (RS), the National Institute of General Medical Sciences (P20GM121312 to RS), and the German Research Foundation (DFG BI 2188–1/2 to EB).

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  1. Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA

    Ryan Smith & Samuel Taylor

  2. Wellcome Centre for Human Neuroimaging, University College London, London, UK

    Edda Bilek

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  1. Ryan Smith
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Correspondence to Ryan Smith.

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Smith, R., Taylor, S. & Bilek, E. Computational Mechanisms of Addiction: Recent Evidence and Its Relevance to Addiction Medicine. Curr Addict Rep 8, 509–519 (2021). https://doi.org/10.1007/s40429-021-00399-z

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