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Part III - Levels of Analysis and Etiology

Published online by Cambridge University Press:  13 July 2020

Steve Sussman
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University of Southern California
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References

Ainslie, G. (1975). Specious reward: a behavioral theory of impulsiveness and impulse control. Psychological Bulletin, 82(4), 463496. http://doi.org/10.1037/h0076860Google Scholar
Amlung, M., Vedelago, L., Acker, J., Balodis, I. & MacKillop, J. (2017). Steep delay discounting and addictive behavior: a meta-analysis of continuous associations. Addiction, 112(1), 5162. https://doi.org/10.1111/add.13535Google Scholar
Baca, C. T. & Grant, K. J. (2007). What heroin users tell us about overdose. Journal of Addictive Diseases, 26(4), 6368. http://doi.org/10.1300/J069v26n04_08Google Scholar
Bachman, J. G., Johnston, L. D. & O’Malley, P. M. (1990). Explaining the recent decline in cocaine use among young adults: further evidence that perceived risks and disapproval lead to reduced drug use. Journal of Health and Social Behavior, 31(2), 173. http://doi.org/10.2307/2137171Google Scholar
Baxter, M. G. & Murray, E. A. (2002). The amygdala and reward. Nature Reviews Neuroscience. https://doi.org/10.1038/nrn875CrossRefGoogle Scholar
Bayer, H. M. & Glimcher, P. W. (2005). Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47(1), 129141. http://doi.org/10.1016/J.NEURON.2005.05.020Google Scholar
Bechara, A. (2004). The role of emotion in decision-making: evidence from neurological patients with orbitofrontal damage. Brain and Cognition, 55(1), 3040.Google Scholar
Bechara, A. (2005). Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nature Neuroscience, 8(11), 14581463. http://doi.org/10.1038/nn1584Google Scholar
Bechara, A. & Naqvi, N. (2004). Listening to your heart: interoceptive awareness as a gateway to feeling. Nature Neuroscience, 7(2), 102103. http://doi.org/10.1038/nn0204-102Google Scholar
Bechara, A., Damasio, H., Tranel, D. & Damasio, A. R., et al. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275(5304), 12931295. http://doi.org/10.1126/science.275.5304.1293Google Scholar
Bechara, A., Dolan, S., Denburg, N., et al. (2001). Decision-making deficits, linked to a dysfunctional ventromedial prefrontal cortex, revealed in alcohol and stimulant abusers. Neuropsychologia, 39(4), 376389. http://doi.org/10.1016/S0028-3932(00)00136-6Google Scholar
Belin, D., Jonkman, S., Dickinson, A., Robbins, T. W. & Everitt, B. J. (2009). Parallel and interactive learning processes within the basal ganglia: relevance for the understanding of addiction. Behavioural Brain Research, 199(1), 89102. http://doi.org/10.1016/J.BBR.2008.09.027CrossRefGoogle ScholarPubMed
Berridge, K. C., Robinson, T. E. & Aldridge, J. W. (2009). Dissecting components of reward: “liking,” “wanting,” and learning. Current Opinion in Pharmacology, 9(1), 6573. http://doi.org/10.1016/J.COPH.2008.12.014Google Scholar
Bingel, U., Wanigasekera, V., Wiech, K., et al. (2011). The effect of treatment expectation on drug efficacy: imaging the analgesic benefit of the opioid remifentanil. Science Translational Medicine, 3(70), 70ra14. http://doi.org/10.1126/scitranslmed.3001244CrossRefGoogle ScholarPubMed
Bjork, J. M. & Grant, S. J. (2009). Does traumatic brain injury increase risk for substance abuse? Journal of Neurotrauma, 26(7), 10771082. http://doi.org/http://dx.doi.org/10.1089/neu.2008.0849Google Scholar
Bowden-Jones, H., McPhillips, M., Rogers, R., Hutton, S. & Joyce, E. (2005). Risk-taking on tests sensitive to ventromedial prefrontal cortex dysfunction predicts early relapse in alcohol dependency: a pilot study. The Journal of Neuropsychiatry and Clinical Neurosciences, 17(3), 417420. https://doi.org/10.1176/jnp.17.3.417CrossRefGoogle ScholarPubMed
Brody, A. L., Mandelkern, M. A., Olmstead, R. E., et al. (2009). Ventral striatal dopamine release in response to smoking a regular vs a denicotinized cigarette. Neuropsychopharmacology, 34(2), 282289. http://doi.org/10.1038/npp.2008.87Google Scholar
Carlson, R. W., Kumar, N. N., Wong-Mckinstry, E., et al. (2012). Alcohol withdrawal syndrome. Critical Care Clinics, 28(4), 549585. http://doi.org/10.1016/J.CCC.2012.07.004Google Scholar
Cicero, T. J., Ellis, M. S., Surratt, H. L. & Kurtz, S. P. (2014). The changing face of heroin use in the United States. JAMA Psychiatry, 71(7), 821. http://doi.org/10.1001/jamapsychiatry.2014.366CrossRefGoogle ScholarPubMed
Clewett, D., Luo, S., Hsu, E., et al. (2014). Increased functional coupling between the left fronto-parietal network and anterior insula predicts steeper delay discounting in smokers. Human Brain Mapping. https://doi.org/10.1002/hbm.22436Google Scholar
Compton, W. M. & Volkow, N. D. (2006). Abuse of prescription drugs and the risk of addiction. Drug and Alcohol Dependence, 83, S4S7. http://doi.org/10.1016/j.drugalcdep.2005.10.020Google Scholar
Corrigall, W. A., Coen, K. M. & Adamson, K. L. (1994). Self-administered nicotine activates the mesolimbic dopamine system through the ventral tegmental area. Brain Research, 653(1–2), 278284. http://doi.org/10.1016/0006-8993(94)90401-4Google Scholar
Craig, A. D. (2009). How do you feel – now? The anterior insula and human awareness. Nature Reviews Neuroscience, 10(1), 5970. http://doi.org/10.1038/nrn2555Google Scholar
Craig, A. D. (2010). The sentient self. Brain Structure and Function, 214(5–6), 563577. http://doi.org/10.1007/s00429-010-0248-yCrossRefGoogle ScholarPubMed
Crews, F., He, J. & Hodge, C. (2007). Adolescent cortical development: a critical period of vulnerability for addiction. Pharmacology Biochemistry and Behavior, 86(2), 189199. http://doi.org/10.1016/J.PBB.2006.12.001Google Scholar
Critchley, H. D., Wiens, S., Rotshtein, P., Öhman, A. & Dolan, R. J. (2004). Neural systems supporting interoceptive awareness. Nature Neuroscience, 7(2), 189195. http://doi.org/10.1038/nn1176Google Scholar
Daniulaityte, R., Falck, R. & Carlson, R. G. (2012). “I’m not afraid of those ones just ‘cause they’ve been prescribed”: perceptions of risk among illicit users of pharmaceutical opioids. International Journal of Drug Policy, 23(5), 374384. http://doi.org/10.1016/j.drugpo.2012.01.012Google Scholar
de la Fuente-Fernández, R., Phillips, A. G., Zamburlini, M., et al. (2002). Dopamine release in human ventral striatum and expectation of reward. Behavioural Brain Research, 136(2), 359363. http://doi.org/10.1016/S0166-4328(02)00130-4CrossRefGoogle Scholar
Di Chiara, G., Bassareo, V., Fenu, S., et al. (2004). Dopamine and drug addiction: the nucleus accumbens shell connection. Neuropharmacology, 47, 227241. http://doi.org/10.1016/J.NEUROPHARM.2004.06.032CrossRefGoogle ScholarPubMed
Droutman, V., Read, S. J. & Bechara, A. (2015). Revisiting the role of the insula in addiction. Trends in Cognitive Sciences, 19(7), 414420. http://doi.org/10.1016/J.TICS.2015.05.005Google Scholar
Everitt, B. J. & Robbins, T. W. (2005). Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nature Neuroscience, 8(11), 14811489. http://doi.org/10.1038/nn1579Google Scholar
Everitt, B. J., Belin, D., Economidou, D., et al. W. (2008). Review: neural mechanisms underlying the vulnerability to develop compulsive drug-seeking habits and addiction. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 363(1507), 31253135. http://doi.org/10.1098/rstb.2008.0089Google Scholar
Fein, G., Di Sclafani, V. & Meyerhoff, D. J. (2002). Prefrontal cortical volume reduction associated with frontal cortex function deficit in 6-week abstinent crack-cocaine dependent men. Drug and Alcohol Dependence, 68(1), 8793. http://doi.org/10.1016/S0376-8716(02)00110-2Google Scholar
Fleckenstein, A. E., Metzger, R. R., Wilkins, D. G., Gibb, J. W. & Hanson, G. R. (1997). Rapid and reversible effects of methamphetamine on dopamine transporters. Journal of Pharmacology and Experimental Therapeutics, 282(2), 834838. Retrieved from http://jpet.aspetjournals.org/content/282/2/834.abstractGoogle Scholar
Franklin, T. R., Acton, P. D., Maldjian, J. A., et al. (2002). Decreased gray matter concentration in the insular, orbitofrontal, cingulate, and temporal cortices of cocaine patients. Biological Psychiatry, 51(2), 134142. http://doi.org/10.1016/S0006-3223(01)01269-0Google Scholar
Fu, L. ping, Bi, G. Hua, Zou, Z. tong, et al. (2008). Impaired response inhibition function in abstinent heroin dependents: an fMRI study. Neuroscience Letters, 438(3), 322326. https://doi.org/10.1016/j.neulet.2008.04.033Google Scholar
Garavan, H. & Hester, R. (2007). The role of cognitive control in cocaine dependence. Neuropsychology Review. https://doi.org/10.1007/s11065-007-9034-xGoogle Scholar
Gardner, E. L. (2002). Addictive potential of cannabinoids: the underlying neurobiology. Chemistry and Physics of Lipids, 121, 267290. http://doi.org/10.1016/S0009-3084(02)00162-7Google Scholar
Gasquoine, P. G. (2014). Contributions of the insula to cognition and emotion. Neuropsychology Review, 24(2),7787. http://doi.org/10.1007/s11065-014-9246-9Google Scholar
Gessa, G., Melis, M., Muntoni, A. & Diana, M. (1998). Cannabinoids activate mesolimbic dopamine neurons by an action on cannabinoid CB1 receptors. European Journal of Pharmacology, 341(1), 3944. http://doi.org/10.1016/S0014-2999(97)01442-8Google Scholar
Gessa, G. L., Muntoni, F., Collu, M., Vargiu, L. & Mereu, G. (1985). Low doses of ethanol activate dopaminergic neurons in the ventral tegmental area. Brain Research, 348(1), 201203. http://doi.org/10.1016/0006-8993(85)90381-6Google Scholar
Goldman, D., Oroszi, G. & Ducci, F. (2005). The genetics of addictions: uncovering the genes. Nature Reviews Genetics, 6(7), 521532. http://doi.org/10.1038/nrg1635Google Scholar
Goldstein, R. Z. & Volkow, N. D. (2011). Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nature Reviews Neuroscience. https://doi.org/10.1038/nrn3119Google Scholar
Gomes, T., Tadrous, M., Mamdani, M. M., Paterson, J. M. & Juurlink., D. N. (2018). The burden of opioid-related mortality in the United States. JAMA Network Open, 1(2), e180217. doi:10.1001/jamanetworkopen.2018.0217Google Scholar
Goudriaan, A. E., De Ruiter, M. B., Van Den Brink, W., Oosterlaan, J. & Veltman, D. J. (2010). Brain activation patterns associated with cue reactivity and craving in abstinent problem gamblers, heavy smokers and healthy controls: an fMRI study. Addiction Biology, 15(4), 491503. https://doi.org/10.1111/j.1369-1600.2010.00242.xGoogle Scholar
Graham, D. P. & Cardon, A. L. (2008). An update on substance use and treatment following traumatic brain injury. Annals of the New York Academy of Sciences, 1141, 148162. http://doi.org/10.1196/annals.1441.029Google Scholar
Grant, S., Contoreggi, C. & London, E. D. (2000). Drug abusers show impaired performance in a laboratory test of decision making. Neuropsychologia. https://doi.org/10.1016/S0028-3932(99)00158-XGoogle Scholar
Grau, L. E., Dasgupta, N., Harvey, A. P., et al. (2007). Illicit use of opioids: is OxyContin® a “gateway drug”? American Journal on Addictions, 16(3), 166173. http://doi.org/10.1080/10550490701375293Google Scholar
Gray, M. A. & Critchley, H. D. (2007). Interoceptive basis to craving. Neuron, 54(2), 183186. http://doi.org/10.1016/j.neuron.2007.03.024Google Scholar
Harlow, K. C. (1990). Patterns of rates of mortality from narcotics and cocaine overdose in Texas, 1976–87. Public Health Reports (Washington, D.C. : 1974), 105(5), 455462. Retrieved from www.ncbi.nlm.nih.gov/pubmed/2120721Google Scholar
Hasin, D. S., Saha, T. D., Kerridge, B. T., et al. (2015). Prevalence of marijuana use disorders in the United States between 2001–2002 and 2012–2013. JAMA Psychiatry, 72(12), 1235. http://doi.org/10.1001/jamapsychiatry.2015.1858Google Scholar
Hassan, S. F., Wearne, T. A., Cornish, J. L. & Goodchild, A. K. (2016). Effects of acute and chronic systemic methamphetamine on respiratory, cardiovascular and metabolic function, and cardiorespiratory reflexes. The Journal of Physiology, 594(3), 763780. http://doi.org/10.1113/JP271257Google Scholar
Heinz, A., Siessmeier, T., Wrase, J., et al. Correlation between dopamine D2 receptors in the ventral striatum and central processing of alcohol cues and craving. American Journal of Psychiatry, 161(10), 1783–1789. http://doi.org/10.1176/ajp.161.10.1783Google Scholar
Herz, A. (1997). Endogenous opioid systems and alcohol addiction. Psychopharmacology, 129(2), 99111. http://doi.org/10.1007/s002130050169Google Scholar
Hester, R. & Garavan, H. (2004). Executive dysfunction in cocaine addiction: evidence for discordant frontal, cingulate, and cerebellar activity. Journal of Neuroscience, 24(49), 1101711022. https://doi.org/10.1523/JNEUROSCI.3321-04.2004Google Scholar
Hill, J. C. & Toffolon, G. (1990). Effect of alcohol on sensory and sensorimotor visual functions. Journal of Studies on Alcohol, 51(2), 108113. http://doi.org/10.15288/jsa.1990.51.108Google Scholar
Hinson, J. M., Jameson, T. L. & Whitney, P. (2003). Impulsive decision making and working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(2), 298306. http://doi.org/10.1037/0278-7393.29.2.298Google ScholarPubMed
Huang, Z.-L., Qu, W.-M., Eguchi, N., et al. (2005). Adenosine A2A, but not A1, receptors mediate the arousal effect of caffeine. Nature Neuroscience, 8(7), 858859. http://doi.org/10.1038/nn1491Google Scholar
Ito, R., Dalley, J. W., Robbins, T. W. & Everitt, B. J. (2002). Dopamine release in the dorsal striatum during cocaine-seeking behavior under the control of a drug-associated cue. The Journal of Neuroscience, 22(14), 6247 LP-6253. Retrieved from www.jneurosci.org/content/22/14/6247.abstractGoogle Scholar
Janes, A. C., Pizzagalli, D. A., Richardt, S., et al. (2010). Neural substrates of attentional bias for smoking-related Cues: an fMRI study. Neuropsychopharmacology. https://doi.org/10.1038/npp.2010.103Google Scholar
Jarmolowicz, D. P. & Schneider, T. D. (2020). Behavioral economics and addictive disorders. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions. Cambridge, UK: Cambridge University Press, pp. 1222.Google Scholar
Johnson, M. W., Bickel, W. K. & Kirshenbaum, A. P. (2004). Substitutes for tobacco smoking: A behavioral economic analysis of nicotine gum, denicotinized cigarettes, and nicotine-containing cigarettes. Drug and Alcohol Dependence, 74(3), 253264. http://doi.org/10.1016/j.drugalcdep.2003.12.012CrossRefGoogle ScholarPubMed
Kaasinen, V., Aalto, S., Nagren, K. & Rinne, J. O. (2004). Expectation of caffeine induces dopaminergic responses in humans. European Journal of Neuroscience, 19(8), 23522356. http://doi.org/10.1111/j.1460-9568.2004.03310.xGoogle Scholar
Kaufman, J. N., Ross, T. J., Stein, E. A. & Garavan, H. (2003). Cingulate hypoactivity in cocaine users during a GO-NOGO task as revealed by event-related functional magnetic resonance imaging. Journal of Neuroscience, 23(21), 78397843. https://doi.org/10.1523/jneurosci.23-21-07839.2003CrossRefGoogle ScholarPubMed
Khantzian, E. J. (1987). The self-medication hypothesis of addictive disorders: focus on heroin and cocaine dependence. In The Cocaine Crisis. Boston, MA: Springer US, pp. 6574. http://doi.org/10.1007/978-1-4613-1837-8_7Google Scholar
Kirby, K. N., Petry, N. M. & Bickel, W. K. (1999). Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls. Journal of Experimental Psychology: General, 128(1), 7887. http://doi.org/10.1037/0096-3445.128.1.78Google Scholar
Koneru, A., Satyanarayana, S. & Rizwan, S. (2009). Endogenous opioids: their physiological role and receptors. Global Journal of Pharmacology, 3(3), 149153. Retrieved from https://pdfs.semanticscholar.org/e83a/851842f363f7e7f561c5ca465df9578d6bbc.pdfGoogle Scholar
Koob, G. F. & Volkow, N. D. (2010). Neurocircuitry of addiction. Neuropsychopharmacology, 35(1), 217238. http://doi.org/10.1038/npp.2009.110Google Scholar
Kringelbach, M. L. (2005). The human orbitofrontal cortex: linking reward to hedonic experience. Nature Reviews Neuroscience, 6(9), 691702. http://doi.org/10.1038/nrn1747Google Scholar
Krishnan-Sarin, S., Reynolds, B., Duhig, A. M., et al. (2007). Behavioral impulsivity predicts treatment outcome in a smoking cessation program for adolescent smokers. Drug and Alcohol Dependence, 88(1), 7982. https://doi.org/10.1016/j.drugalcdep.2006.09.006Google Scholar
Lin, S.-K., Pan, W. H. T. & Yeh, P.-H. (2007). Prefrontal dopamine efflux during exposure to drug-associated contextual cues in rats with prior repeated methamphetamine. Brain Research Bulletin, 71(4), 365371. http://doi.org/10.1016/J.BRAINRESBULL.2006.10.001Google Scholar
Marinkovic, K., Halgren, E. & Maltzman, I. (2004). Effects of alcohol on verbal processing: An event-related potential study. Alcoholism: Clinical & Experimental Research, 28(3), 415423. http://doi.org/10.1097/01.ALC.0000117828.88597.80Google Scholar
Mechtcheriakov, S., Brenneis, C., Egger, K., et al. (2007). A widespread distinct pattern of cerebral atrophy in patients with alcohol addiction revealed by voxel-based morphometry. Journal of Neurology, Neurosurgery, and Psychiatry, 78(6), 610614. http://doi.org/10.1136/jnnp.2006.095869Google Scholar
Melis, M., Pistis, M., Perra, S., et al. (2004). Endocannabinoids mediate presynaptic inhibition of glutamatergic transmission in rat ventral tegmental area dopamine neurons through activation of CB1 receptors. The Journal of Neuroscience:  The Official Journal of the Society for Neuroscience, 24(1), 5362. http://doi.org/10.1523/JNEUROSCI.4503-03.2004Google Scholar
Miech, R. A., Chilcoat, H. & Harder, V. S. (2005). The increase in the association of education and cocaine use over the 1980s and 1990s: Evidence for a “historical period” effect. Drug and Alcohol Dependence, 79(3), 311320. http://doi.org/10.1016/J.DRUGALCDEP.2005.01.022Google Scholar
Molina-Luna, K., Pekanovic, A., Röhrich, S., et al. (2009). Dopamine in motor cortex is necessary for skill learning and synaptic plasticity. PLoS ONE, 4(9), e7082. http://doi.org/10.1371/journal.pone.0007082Google Scholar
Monterosso, J. R., Ainslie, G., Xu, J., et al. (2007). Frontoparietal cortical activity of methamphetamine-dependent and comparison subjects performing a delay discounting task. Human Brain Mapping, 28(5), 383393. http://doi.org/10.1002/hbm.20281Google Scholar
Monterosso, J. R., Aron, A. R., Cordova, X., Xu, J. & London, E. D. (2005). Deficits in response inhibition associated with chronic methamphetamine abuse. Drug and Alcohol Dependence. https://doi.org/10.1016/j.drugalcdep.2005.02.002Google Scholar
Naqvi, N. H. & Bechara, A. (2005). The airway sensory impact of nicotine contributes to the conditioned reinforcing effects of individual puffs from cigarettes. Pharmacology Biochemistry and Behavior, 81(4), 821829. http://doi.org/10.1016/j.pbb.2005.06.005Google Scholar
Naqvi, N. H. & Bechara, A. (2009). The hidden island of addiction: the insula. Trends in Neurosciences, 32(1), 5667. http://doi.org/10.1016/j.tins.2008.09.009Google Scholar
Naqvi, N. H. & Bechara, A. (2010). The insula and drug addiction: an interoceptive view of pleasure, urges, and decision-making. Brain Structure and Function, 214(5–6), 435450. http://doi.org/10.1007/s00429-010-0268-7Google Scholar
Naqvi, N. H., Rudrauf, D., Damasio, H. & Bechara, A. (2007). Damage to the insula disrupts addiction to cigarette smoking. Science (New York, N.Y.), 315(5811), 531534. http://doi.org/10.1126/science.1135926Google Scholar
Nash, J. F. & Yamamoto, B. K. (1992). Methamphetamine neurotoxicity and striatal glutamate release: comparison to 3, 4-methylenedioxymethamphetamine. Brain Research, 581(2), 237243. http://doi.org/10.1016/0006-8993(92)90713-JCrossRefGoogle Scholar
Nestler, E. J. (2005). The neurobiology of cocaine addiction. Science & Practice Perspectives, 3(1), 410. Retrieved from www.ncbi.nlm.nih.gov/pubmed/18552739Google Scholar
Noël, X., Brevers, D. & Bechara, A. (2013). A triadic neurocognitive approach to addiction for clinical interventions. Frontiers in Psychiatry, 4, 179. http://doi.org/10.3389/fpsyt.2013.00179CrossRefGoogle ScholarPubMed
Oswald, L. M., Wong, D. F., McCaul, M., et al. (2005). Relationships among ventral striatal dopamine release, cortisol secretion and subjective responses to amphetamine. Neuropsychopharmacology, 30(4), 821832. http://doi.org/10.1038/sj.npp.1300667Google Scholar
Paraskevaides, T., Morgan, C. J. A., Leitz, J. R., et al. (2010). Drinking and future thinking: acute effects of alcohol on prospective memory and future simulation. Psychopharmacology, 208(2), 301308. http://doi.org/10.1007/s00213-009-1731-0Google Scholar
Pertwee, R. (2010). S.27.01 Pharmacological actions of cannabinoids. European Neuropsychopharmacology, 20, S205. http://doi.org/10.1016/S0924-977X(10)70232-7Google Scholar
Phillips, A. G. & Fibiger, H. C. (1979). Decreased resistance to extinction after haloperidol: implications for the role of dopamine in reinforcement. Pharmacology Biochemistry and Behavior, 10(5), 751760. http://doi.org/10.1016/0091-3057(79)90328-9Google Scholar
Pidoplichko, V. I., DeBiasi, M., Williams, J. T. & Dani, J. A. (1997). Nicotine activates and desensitizes midbrain dopamine neurons. Nature, 390(6658), 401404. http://doi.org/10.1038/37120Google Scholar
Pierce, R. C. & Kumaresan, V. (2006). The mesolimbic dopamine system: the final common pathway for the reinforcing effect of drugs of abuse? Neuroscience & Biobehavioral Reviews, 30(2), 215238. http://doi.org/10.1016/J.NEUBIOREV.2005.04.016Google Scholar
Porkka-Heiskanen, T., Strecker, R., Thakkar, M., Bjorkum, A. & Greene, R. (1997). Adenosine: a mediator of the sleep-inducing effects of prolonged wakefulness. Science, 276(5316), 12651268. http://doi.org/10.1126/science.276.5316.1265Google Scholar
Robbe, D., Kopf, M., Remaury, A., Bockaert, J. & Manzoni, O. J. (2002). Endogenous cannabinoids mediate long-term synaptic depression in the nucleus accumbens. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 8384–8. http://doi.org/10.1073/pnas.122149199Google Scholar
Robbins, T. W., Ersche, K. D. & Everitt, B. J. (2008). Drug addiction and the memory systems of the brain. Annals of the New York Academy of Sciences, 1141(1), 121. http://doi.org/10.1196/annals.1441.020Google Scholar
Robinson, J. (2002). Decades of Drug Use: The ’80s and ’90s. Retrieved January 10, 2018, from http://news.gallup.com/poll/6352/decades-drug-use-80s-90s.aspxGoogle Scholar
Robinson, S., Sandstrom, S. M., Denenberg, V. H. & Palmiter, R. D. (2005). Distinguishing whether dopamine regulates liking, wanting, and/or learning about rewards. Behavioral Neuroscience, 119(1), 515. http://doi.org/10.1037/0735-7044.119.1.5Google Scholar
Robinson, T. E. & Berridge, K. C. (1993). The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Research Reviews, 18(3), 247291. http://doi.org/10.1016/0165-0173(93)90013-PGoogle Scholar
Robinson, T. E. & Berridge, K. C. (2001). Incentive-sensitization and addiction. Addiction, 96(1), 103114. http://doi.org/10.1046/j.1360-0443.2001.9611038.xGoogle Scholar
Roozendaal, B., McReynolds, J. R. & McGaugh, J. L. (2004). The basolateral amygdala interacts with the medial prefrontal cortex in regulating glucocorticoid effects on working memory impairment. The Journal of Neuroscience, 24(6), 1385 LP–1392. Retrieved from www.jneurosci.org/content/24/6/1385.abstractGoogle Scholar
Rossetti, Z. L., Hmaidan, Y. & Gessa, G. L. (1992). Marked inhibition of mesolimbic dopamine release: a common feature of ethanol, morphine, cocaine and amphetamine abstinence in rats. European Journal of Pharmacology, 221(2–3), 227234. http://doi.org/10.1016/0014-2999(92)90706-AGoogle Scholar
Rudd, R. A., Aleshire, N., Zibbell, J. E. & Matthew Gladden, R. (2016). Increases in drug and opioid overdose deaths – United States, 2000–2014. American Journal of Transplantation, 16(4), 13231327. http://doi.org/10.1111/ajt.13776Google Scholar
Samanez-Larkin, G. R., Hollon, N. G., Carstensen, L. L. & Knutson, B. (2008). Individual differences in insular sensitivity during loss: anticipation predict avoidance learning: research report. Psychological Science, 19(4), 320323. http://doi.org/10.1111/j.1467-9280.2008.02087.xGoogle Scholar
Schmidt, A., Borgwardt, S., Gerber, H., et al. (2014). Acute effects of heroin on negative emotional processing: relation of amygdala activity and stress-related responses. Biological Psychiatry, 76(4), 289296. http://doi.org/10.1016/j.biopsych.2013.10.019Google Scholar
Schoenbaum, G. & Shaham, Y. (2008). The role of orbitofrontal cortex in drug addiction: a review of preclinical studies. Biological Psychiatry, 63(3), 256262. http://doi.org/10.1016/J.BIOPSYCH.2007.06.003Google Scholar
Schoenbaum, G., Roesch, M. R. & Stalnaker, T. A. (2006). Orbitofrontal cortex, decision-making and drug addiction. Trends in Neurosciences, 29(2), 116124. http://doi.org/10.1016/j.tins.2005.12.006Google Scholar
Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80(1), 127. http://doi.org/10.1152/jn.1998.80.1.1Google Scholar
Sell, L. A., Morris, J. S., Bearn, J., et al. (2000). Neural responses associated with cue evoked emotional states and heroin in opiate addicts. Drug and Alcohol Dependence, 60(2), 207216. http://doi.org/10.1016/S0376-8716(99)00158-1Google Scholar
Seymour, B., Daw, N., Dayan, P., Singer, T. & Dolan, R. (2007). Differential encoding of losses and gains in the human striatum. Journal of Neuroscience, 27(18), 48264831. http://doi.org/10.1523/JNEUROSCI.0400-07.2007Google Scholar
Shen, M., Piser, T. M., Seybold, V. S., et al. (1996). Cannabinoid receptor agonists inhibit glutamatergic synaptic transmission in rat hippocampal cultures. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 16(14), 43224334. Retrieved from www.ncbi.nlm.nih.gov/pubmed/8699243Google Scholar
Singer, T., Critchley, H. D. & Preuschoff, K. (2009). A common role of insula in feelings, empathy and uncertainty. Trends in Cognitive Sciences, 13(8), 334340. http://doi.org/10.1016/j.tics.2009.05.001Google Scholar
Solinas, M., Ferré, S., You, Z.-B., et al. (2002). Caffeine induces dopamine and glutamate release in the shell of the nucleus accumbens. The Journal of Neuroscience, 22(15), 6321 LP–6324. Retrieved from www.jneurosci.org/content/22/15/6321.abstractGoogle Scholar
Spanagel, R., Herz, A. & Shippenberg, T. S. (1992). Opposing tonically active endogenous opioid systems modulate the mesolimbic dopaminergic pathway (mirodialysis/nucleus accumbens/dopamine release and metabolism/opiate dependence). Pharmacology, 89, 20462050. Retrieved from www.pnas.org/content/89/6/2046.full.pdfGoogle Scholar
Spiller, M. W., Broz, D., Wejnert, C., Nerlander, L., Paz-Bailey, G., Centers for Disease Control and Prevention (CDC), & National HIV Behavioral Surveillance System Study Group. (2015). HIV infection and HIV-associated behaviors among persons who inject drugs – 20 cities, United States, 2012. Morbidity and Mortality Weekly Report, 64(10), 270275. Retrieved from www.ncbi.nlm.nih.gov/pubmed/25789742Google Scholar
Stacy, A. W. & Wiers, R. W. (2010). Implicit cognition and addiction: a tool for explaining paradoxical behavior. Annual Review of Clinical Psychology, 6(1), 551575. http://doi.org/10.1146/annurev.clinpsy.121208.131444Google Scholar
Stacy, A. W., Pike, J. & Lee, A. Y. (2020). Multiple memory systems, addiction, and health habits: new routes for translational science. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions. Cambridge, UK: Cambridge University Press, pp. 152170.Google Scholar
Tanda, G., Pontieri, F. E. & Di Chiara, G. (1997). Cannabinoid and heroin activation of mesolimbic dopamine transmission by a common mu1 opioid receptor mechanism. Science (New York, N.Y.), 276(5321), 2048–50. http://doi.org/10.1126/SCIENCE.276.5321.2048Google Scholar
The Heroin Hug | Absolute Advocacy. (n.d.). Retrieved January 10, 2020, from www.absoluteadvocacy.org/the-heroin-hug/Google Scholar
Thompson, P. M., Hayashi, K. M., Simon, S. L., et al. (2004). Structural abnormalities in the brains of human subjects who use methamphetamine. The Journal of Neuroscience, 24(26), 6028 LP-6036. Retrieved from www.jneurosci.org/content/24/26/6028.abstractGoogle Scholar
Turcotte, C., Blanchet, M.-R., Laviolette, M. & Flamand, N. (2016). Impact of cannabis, cannabinoids, and endocannabinoids in the lungs. Frontiers in Pharmacology, 7, 317. http://doi.org/10.3389/fphar.2016.00317Google Scholar
Turel, O. & Bechara, A. (2016). A triadic reflective-impulsive-interoceptive awareness model of general and impulsive information system use: behavioral tests of neuro-cognitive theory. Frontiers in Psychology, 7, 601. http://doi.org/10.3389/fpsyg.2016.00601Google Scholar
Vaccarro, A. G. & Potenza, M. N. (2020). Neurobiological foundations of behavioral addictions. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions. Cambridge, UK: Cambridge University Press, pp. 136151.Google Scholar
Villafuerte, S., Heitzeg, M. M., Foley, S., et al. (2012). Impulsiveness and insula activation during reward anticipation are associated with genetic variants in GABRA2 in a family sample enriched for alcoholism. Molecular Psychiatry, 17(5), 511519. http://doi.org/10.1038/mp.2011.33Google Scholar
Volkow, N. D. & Fowler, J. S. (2000). Addiction, a disease of compulsion and drive: involvement of the orbitofrontal cortex. Cerebral Cortex, 10(3), 318325. http://doi.org/10.1093/cercor/10.3.318Google Scholar
Volkow, N. D., Chang, L., Wang, G.-J., et al. (2001). Loss of dopamine transporters in methamphetamine abusers recovers with protracted abstinence. The Journal of Neuroscience, 21(23), 94149418. Retrieved from www.jneurosci.org/content/21/23/9414.abstractGoogle Scholar
Volkow, N. D., Fowler, J. S., Wang, G.-J. & Swanson, J. M. (2004). Dopamine in drug abuse and addiction: results from imaging studies and treatment implications. Molecular Psychiatry, 9(6), 557569. http://doi.org/10.1038/sj.mp.4001507Google Scholar
Volkow, N. D., Wang, G.-J., Fowler, J. S., et al. (1996). Decreases in dopamine receptors but not in dopamine transporters in alcoholics. Alcoholism: Clinical and Experimental Research, 20(9), 15941598. http://doi.org/10.1111/j.1530-0277.1996.tb05936.xGoogle Scholar
Volkow, N. D., Wang, G.-J., Telang, F., et al. (2008). Dopamine increases in striatum do not elicit craving in cocaine abusers unless they are coupled with cocaine cues. NeuroImage, 39(3), 12661273. http://doi.org/10.1016/J.NEUROIMAGE.2007.09.059Google Scholar
Wager, T. D., Davidson, M. L., Hughes, B. L., Lindquist, M. A. & Ochsner, K. N. (2008). Prefrontal-subcortical pathways mediating successful emotion regulation. Neuron, 59(6), 10371050. http://doi.org/10.1016/J.NEURON.2008.09.006Google Scholar
Wang, Y., Zhu, L., Zou, Q., et al. (2018). Frequency dependent hub role of the dorsal and ventral right anterior insula. NeuroImage, 165, 112117. https://doi.org/10.1016/j.neuroimage.2017.10.004Google Scholar
Warlow, S. M., et al. (2020). Sensitization of incentive salience and the transition to addiction. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions. Cambridge, UK: Cambridge University Press, pp. 2337.Google Scholar
Watson, P., de Wit, S., Hommel, B. & Wiers, R. W. (2012). Motivational mechanisms and outcome expectancies underlying the approach bias toward addictive substances. Frontiers in Psychology, 3, 440. http://doi.org/10.3389/fpsyg.2012.00440Google Scholar
Westman, E. C., Behm, F. M. & Rose, J. E. (1995). Airway sensory replacement combined with nicotine replacement for smoking cessation: a randomized, placebo-controlled trial using a citric acid inhaler. Chest, 107(5), 13581364. http://doi.org/10.1378/CHEST.107.5.1358Google Scholar
Westman, E. C., Behm, F. M. & Rose, J. E. (1996). Dissociating the nicotine and airway sensory effects of smoking. Pharmacology Biochemistry and Behavior, 53(2), 309315. http://doi.org/10.1016/0091-3057(95)02027-6CrossRefGoogle ScholarPubMed
What does it feel like to use cocaine? | Drug Policy Alliance. (n.d.). Retrieved January 9, 2020, from www.drugpolicy.org/drug-facts/cocaine/what-cocaine-feels-likeGoogle Scholar
Wise, R. A. (1996). Neurobiology of addiction. Current Opinion in Neurobiology, 6(2), 243251. http://doi.org/10.1016/S0959-4388(96)80079-1Google Scholar
Yuan, Y., Zhu, Z., Shi, J., et al. (2009). Gray matter density negatively correlates with duration of heroin use in young lifetime heroin-dependent individuals. Brain and Cognition, 71(3), 223228. http://doi.org/10.1016/J.BANDC.2009.08.014Google Scholar

References

Aarseth, E., Bean, A. M., Boonen, H., et al. (2017). Scholars’ open debate paper on the World Health Organization ICD-11 Gaming Disorder proposalJournal of Behavioral Addictions, 6(3), 267270.Google Scholar
Andrade, L. F. & Petry, N. M. (2012). Delay and probability discounting in pathological gamblers with and without a history of substance use problems. Psychopharmacology, 219(2), 491499.Google Scholar
American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (5th edition). Arlington, VA: American Psychiatric Publishing.Google Scholar
Balconi, M., Venturella, I. & Finocchiaro, R. (2017). Evidences from rewarding system, FRN and P300 effect in internet-addiction in young people. Brain Sciences, 7(7). doi: 10.3390/brainsci7070081Google Scholar
Balodis, I. M., Kober, H., Worhunsky, P. D., et al. (2012). Diminished frontostriatal activity during processing of monetary rewards and losses in pathological gambling. Biological Psychiatry, 71(8), 749757.Google Scholar
Balodis, I. M., Linnet, J., Arshad, F., et al. (2018). A preliminary study relating neural processing of reward and loss prospect to risky decision-making in individuals with and without gambling disorder. International Gambling Studies, 18(2), 269285.Google Scholar
Balodis, I. M. & Potenza, M. N. (2015). Anticipatory reward processing in addicted populations: a focus on the monetary incentive delay task. Biological Psychiatry, 77, 434444.Google Scholar
Banca, P., Morris, L. S., Mitchell, S., et al. (2016). Novelty, conditioning and attentional bias to sexual rewards. Journal of Psychiatric Research, 72, 91101.Google Scholar
Bechara, A., Damasio, A. R., Damasio, H. & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1–3), 715.Google Scholar
Beck, A., Schlagenhauf, F., Wustenberg, T., et al. (2009). Ventral striatal activation during reward anticipation correlates with impulsivity in alcoholics. Biological Psychiatry, 66(8), 734742.Google Scholar
Berlin, H. A., Braun, A., Simeon, D., et al. (2013). A double-blind, placebo-controlled trial of topiramate for pathological gambling. The World Journal of Biological Psychiatry: The Official Journal of the World Federation of Societies of Biological Psychiatry, 14(2), 121128.Google Scholar
Berridge, K. C. (2012). From prediction error to incentive salience: mesolimbic computation of reward motivation. European Journal of Neuroscience, 35(7), 11241143.Google Scholar
Black, D. W., Arndt, S., Coryell, W. H., et al. (2007). Bupropion in the treatment of pathological gambling: a randomized, double-blind, placebo-controlled, flexible-dose study. Journal of Clinical Psychopharmacology, 27(2), 143150. https://doi.org/10.1097/01.jcp.0000264985.25109.25CrossRefGoogle ScholarPubMed
Blanco, C., Petkova, E., Ibanez, A. & Saiz-Ruiz, J. (2002). A pilot placebo-controlled study of fluvoxamine for pathological gambling. Annals of Clinical Psychiatry: Official Journal of the American Academy of Clinical Psychiatrists, 14(1), 915.Google Scholar
Blum, K., Thanos, P. K., Oscar-Berman, M., et al. (2015). Dopamine in the brain: hypothesizing surfeit or deficit links to reward and addiction. Journal of Reward Deficiency Syndrome, 1(3), 95104.Google Scholar
Boileau, I., Payer, D., Chugani, B., et al. (2013). The D2/3 dopamine receptor in pathological gambling: a positron emission tomography study with [11C]-(+)-propyl-hexahydro-naphtho-oxazin and [11C]raclopride. Addiction, 108(5), 953963.Google Scholar
Bőthe, B., Tóth-Király, I., Orosz, G., et al. (2019). Revisiting the role of impulsivity and compulsivity in problematic sexual behaviors. Journal of Sex Research, 56(2), 166179.Google Scholar
Bostwick, J. M. & Bucci, J. A. (2008). Internet sex addiction treated with naltrexone. Mayo Clinic Proceedings, 83(2), 226230.Google Scholar
Brand, M., Snagowski, J., Laier, C. & Maderwald, S. (2016). Ventral striatum activity when watching preferred pornographic pictures is correlated with symptoms of Internet pornography addiction. NeuroImage, 129, 224232.Google Scholar
Brevers, D., Bechara, A., Cleeremans, A. & Noel, X. (2013). Iowa Gambling Task (IGT): twenty years after – gambling disorder and IGT. Frontiers in Psychology, 4, 665.Google Scholar
Brevers, D., Noel, X., He, Q., Melrose, J. A. & Bechara, A. (2016). Increased ventral-striatal activity during monetary decision making is a marker of problem poker gambling severity. Addiction Biology, 21(3), 688699.Google Scholar
Bullock, S. A. & Potenza, M. N. (2012). Pathological gambling: neuropsychopharmacology and treatment. Current Psychopharmacology, 1(1). doi: 10.2174/2211556011201010067Google Scholar
Capurso, N. A. (2017). Naltrexone for the treatment of comorbid tobacco and pornography addiction. The American Journal on Addictions, 26(2), 115117.Google Scholar
Chen, C.-Y., Huang, M.-F., Yen, J.-Y., et al. (2015). Brain correlates of response inhibition in Internet gaming disorder. Psychiatry and Clinical Neurosciences, 69(4), 201209.Google Scholar
Chen, S. H., Weng, L. J., Su, Y. J., Wu, H. M. & Yang, P. F. (2003). Development of a Chinese Internet addiction scale and its psychometric studyChinese Journal of Psychology, 45, 279294.Google Scholar
Choi, J.-S., Park, S. M., Lee, J., et al. (2013). Resting-state beta and gamma activity in Internet addiction. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 89(3), 328333.Google Scholar
Clark, L., Stokes, P. R., Wu, K., et al. (2012). Striatal dopamine D(2)/D(3) receptor binding in pathological gambling is correlated with mood-related impulsivity. NeuroImage, 63(1), 4046.Google Scholar
Cole, H. & Griffiths, M. D. (2007). Social interactions in massively multiplayer online role-playing gamers. Cyberpsychology & Behavior: The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 10(4), 575583.Google Scholar
Crockford, D. N., Goodyear, B., Edwards, J., Quickfall, J. & el-Guebaly, N. (2005). Cue-induced brain activity in pathological gamblers. Biological Psychiatry, 58(10), 787795.Google Scholar
de Ruiter, M. B., Veltman, D. J., Goudriaan, A. E., et al. (2009). Response perseveration and ventral prefrontal sensitivity to reward and punishment in male problem gamblers and smokers. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 34(4), 10271038.Google Scholar
Derbyshire, K. L. & Grant, J. E. (2015). Compulsive sexual behavior: a review of the literatureJournal of Behavioral Addictions4(2), 3743.Google Scholar
Dieter, J., Hill, H., Sell, M., et al. (2015). Avatar’s neurobiological traces in the self-concept of massively multiplayer online role-playing game (MMORPG) addicts. Behavioral Neuroscience, 129(1), 817.Google Scholar
Ding, W., Sun, J., Sun, Y.-W., et al. (2014). Trait impulsivity and impaired prefrontal impulse inhibition function in adolescents with internet gaming addiction revealed by a Go/No-Go fMRI study. Behavioral and Brain Functions, 10, 20.Google Scholar
Dong, G., Devito, E. E., Du, X. & Cui, Z. (2012a). Impaired inhibitory control in “internet addiction disorder”: a functional magnetic resonance imaging study. Psychiatry Research, 203(2–3), 153158.Google Scholar
Dong, G., DeVito, E., Huang, J. & Du, X. (2012b). Diffusion tensor imaging reveals thalamus and posterior cingulate cortex abnormalities in internet gaming addicts. Journal of Psychiatric Research, 46(9), 12121216.Google Scholar
Dong, G., Hu, Y., Lin, X. & Lu, Q. (2013). What makes Internet addicts continue playing online even when faced by severe negative consequences? Possible explanations from an fMRI study. Biological Psychology, 94(2), 282289.Google Scholar
Dong, G., Huang, J. & Du, X. (2011). Enhanced reward sensitivity and decreased losssensitivity in Internet addicts: an fMRI study during a guessing task. Journal of Psychiatric Research, 45(11), 15251529.Google Scholar
Dong, G., Lin, X. & Potenza, M. N. (2015). Decreased functional connectivity in an executive control network is related to impaired executive function in Internet gaming disorder. Progress Neuro-Psychopharmacol Biol Psychiatry, 57, 7685.Google Scholar
Dong, G., Lin, X., Zhou, H. & Lu, Q. (2014). Cognitive flexibility in internet addicts: fMRI evidence from difficult-to-easy and easy-to-difficult switching situations. Addictive Behaviors, 39(3), 677683.CrossRefGoogle ScholarPubMed
Dong, G. & Potenza, M. N. (2016). Risk-taking and risky decision-making in Internet gaming disorder: implications regarding online gaming in the setting of negative consequences. Journal of Psychiatric Research, 73, 18.Google Scholar
Dullur, P. & Starcevic, V. (2017). Internet gaming disorder does not qualify as a mental disorder. Australian & New Zealand Journal of Psychiatry, 52(2), 110111.Google Scholar
Dunning, J. P., Parvaz, M. A., Hajcak, G., et al. (2011). Motivated attention to cocaine and emotional cues in abstinent and current cocaine users – an ERP study. European Journal of Neuroscience, 33(9), 17161723.Google Scholar
Duven, E. C. P., Muller, K. W., Beutel, M. E. & Wolfling, K. (2015). Altered reward processing in pathological computer gamers – ERP-results from a semi-natural gaming-design. Brain and Behavior, 5(1), 1323.Google Scholar
Dymond, S., Lawrence, N. S., Dunkley, B. T., et al. (2014). Almost winning: induced MEG theta power in insula and orbitofrontal cortex increases during gambling near-misses and is associated with BOLD signal and gambling severity. NeuroImage, 91, 210219.Google Scholar
Eichenbaum, A., Kattner, F., Bradford, D., Gentile, D. A. & Green, C. S. (2015). role-playing and real-time strategy games associated with greater probability of Internet Gaming Disorder. Cyberpsychology, Behavior and Social Networking, 18(8), 480485.Google Scholar
Eisen, S. A., Lin, N., Lyons, M. J., et al. (1998). Familial influences on gambling behavior: an analysis of 3359 twin pairs. Addiction, 93(9), 13751384.Google ScholarPubMed
Everitt, B. J. & Robbins, T. W. (2005). Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nature Neuroscience, 8(11), 14811489.Google Scholar
Everitt, B. J. & Robbins, T. W. (2013). From the ventral to the dorsal striatum: devolving views of their roles in drug addiction. Neuroscience and Biobehavioral Reviews, 37(9 Pt A), 19461954.Google Scholar
Fong, T., Kalechstein, A., Bernhard, B., Rosenthal, R. & Rugle, L. (2008). A double-blind, placebo-controlled trial of olanzapine for the treatment of video poker pathological gamblers. Pharmacology, Biochemistry, and Behavior, 89(3), 298303.CrossRefGoogle ScholarPubMed
Garrison, K. A., Yip, S. W., Balodis, I. M., et al. (2017). Reward-related frontostriatal activity and smoking behavior among adolescents in treatment for smoking cessation. Drug and Alcohol Dependence, 177, 268276.CrossRefGoogle ScholarPubMed
Georgiadis, J. R. & Kringelbach, M. L. (2012). The human sexual response cycle: brain imaging evidence linking sex to other pleasures. Progress in Neurobiology, 98(1), 4981.Google Scholar
Gescheidt, T., Marecek, R., Mikl, M., et al. (2013). Functional anatomy of outcome evaluation during Iowa Gambling Task performance in patients with Parkinson’s disease: an fMRI study. Neurological Sciences: Official Journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 34(12), 21592166.CrossRefGoogle ScholarPubMed
Giddens, J. L., Xian, H., Scherrer, J. F., Eisen, S. A. & Potenza, M. N. (2011). Shared genetic contributions to anxiety disorders and pathological gambling in a male population. Journal of Affective Disorders, 132, 406412.Google Scholar
Gola, M. (2016). Decreased LPP for sexual images in problematic pornography users may be consistent with addiction models. Everything depends on the model. (Commentary on Prause, Steele, Staley, Sabatinelli, & Hajcak, 2015). Biological Psychology, 120, 156158.Google Scholar
Gola, M., Miyakoshi, M. & Sescousse, G. (2015). Sex, impulsivity, and anxiety: interplay between ventral striatum and amygdala reactivity in sexual behaviors. Journal of Neuroscience, 35(46), 1522715229.Google Scholar
Gola, M. & Potenza, M. N. (2016). Paroxetine treatment of problematic pornography use: a case series. Journal of Behavioral Addictions, 5(3), 529532.Google Scholar
Gola, M., Wordecha, M., Marchewka, A. & Sescousse, G. (2016). Visual sexual stimuli – cue or reward? A perspective for interpreting brain imaging findings on human sexual behaviorsFrontiers in Human Neuroscience10, 402.Google Scholar
Gola, M., Wordecha, M., Sescousse, G., et al. (2017). Can pornography be addictive? An fMRI study of men seeking treatment for problematic pornography use. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 42(10), 20212031.Google Scholar
Goldstein, L., Manowitz, P., Nora, R., Swartzburg, M. & Carlton, P. L. (1985). Differential EEG activation and pathological gambling. Biological Psychiatry, 20(11), 12321234.Google Scholar
Goldstein, R. Z. & Volkow, N. D. (2011). Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nature Reviews. Neuroscience, 12(11), 652669.CrossRefGoogle ScholarPubMed
Goudriaan, A. E., De Ruiter, M. B., Van Den Brink, W., Oosterlaan, J. & Veltman, D. J. (2010). Brain activation patterns associated with cue reactivity and craving in abstinent problem gamblers, heavy smokers and healthy controls: an fMRI study. Addiction Biology, 15, 491503.Google Scholar
Grant, J. E., Kim, S. W. & Hartman, B. K. (2008b). A double-blind, placebo-controlled study of the opiate antagonist naltrexone in the treatment of pathological gambling urges. The Journal of Clinical Psychiatry, 69(5), 783789.Google Scholar
Grant, J. E., Kim, S. W., Hollander, E. & Potenza, M. N. (2008a). Predicting response to opiate antagonists and placebo in the treatment of pathological gambling. Psychopharmacology, 200, 521527.CrossRefGoogle ScholarPubMed
Grant, J. E., Kim, S. W. & Odlaug, B. L. (2007). N-acetyl cysteine, a glutamate-modulating agent, in the treatment of pathological gambling: a pilot study. Biological Psychiatry, 62(6), 652657.Google Scholar
Grant, J. E., Kim, S. W., Potenza, M. N., et al. (2003). Paroxetine treatment of pathological gambling: a multi-centre randomized controlled trial. International Clinical Psychopharmacology, 18(4), 243249.CrossRefGoogle ScholarPubMed
Grant, J. E., Odlaug, B. L., Chamberlain, S. R., et al. (2013). A proof of concept study of tolcapone for pathological gambling: relationships with COMT genotype and brain activationEuropean Neuropsychopharmacology23(11), 15871596.Google Scholar
Grant, J. E., Odlaug, B. L., Chamberlain, S. R., et al. (2014). A randomized, placebo-controlled trial of N-acetylcysteine plus imaginal desensitization for nicotine-dependent pathological gamblers. The Journal of Clinical Psychiatry, 75(1), 3945.Google Scholar
Grant, J. E., Odlaug, B. L., Potenza, M. N., Hollander, E. & Kim, S. W. (2010). A multi-center, double-blind, placebo-controlled study of the opioid antagonist nalmefene in the treatment of pathological gambling. British Journal of Psychiatry, 197, 330331.Google Scholar
Grant, J. E. & Potenza, M. N. (2006). Escitalopram treatment of pathological gambling with co-occurring anxiety: an open-label pilot study with double-blind discontinuation. International Clinical Psychopharmacology, 21(4), 203209.Google Scholar
Grant, J. E., Potenza, M. N., Hollander, E., et al. (2006). A multicenter investigation of the opioid antagonist nalmefene in the treatment of pathological gambling. American Journal of Psychiatry, 163, 303312.Google Scholar
Grant, J. E., Potenza, M. N., Kraus, S. W. & Petrakis, I. L. (2017). Naltrexone and disulfiram treatment response in veterans with alcohol dependence and co-occurring gambling problems. Journal of Clinical Psychiatry, 78(9), e1299e1306.Google Scholar
Griffiths, M. D., King, D. L. & Demetrovics, Z. (2014). DSM-5 internet gaming disorder needs a unified approach to assessment. Neuropsychiatry, 4(1), 14.Google Scholar
Han, D. H., Kim, Y. S., Lee, Y. S., Min, K. J. & Renshaw, P. F. (2010). Changes in cue-induced, prefrontal cortex activity with video-game play. Cyberpsychology, Behavior and Social Networking, 13(6), 655661.Google Scholar
Han, D. H., Lee, Y. S., Na, C., et al. (2009). The effect of methylphenidate on Internet video game play in children with attention-deficit/hyperactivity disorder. Comprehensive Psychiatry, 50(3), 251256.Google Scholar
Han, D. H.Lee, Y. S.Yang, K. C.et al. (2007). Dopamine genes and reward dependence in adolescents with excessive internet video game playJournal of Addiction Medicine., 1133138.Google Scholar
Han, D. H. & Renshaw, P. F. (2012). Bupropion in the treatment of problematic online game play in patients with major depressive disorderJournal of Psychopharmacology26(5), 689696.Google Scholar
Hariri, A. R., Brown, S. M., Williamson, D. E., et al. (2006). Preference for immediate over delayed rewards is associated with magnitude of ventral striatal activity. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 26(51), 1321313217.Google Scholar
Hayoun, M. (2014). China: Inside an Internet gaming disorder rehab center. Aljazeera America.Google Scholar
Heidbreder, C. A., Gardner, E. L., Xi, Z.-X., et al. (2005). The role of central dopamine D3 receptors in drug addiction: a review of pharmacological evidence. Brain Research. Brain Research Reviews, 49(1), 77105.Google Scholar
Hewig, J., Kretschmer, N., Trippe, R. H., et al. (2010). Hypersensitivity to reward in problem gamblers. Biological Psychiatry, 67(8), 781783.Google Scholar
Hollander, E., DeCaria, C. M., Finkell, J. N., et al. (2000). A randomized double-blind fluvoxamine/placebo crossover trial in pathologic gambling. Biological Psychiatry, 47(9), 813817.Google Scholar
Hollander, E., DeCaria, C. M., Mari, E., et al. (1998). Short-term single-blind fluvoxamine treatment of pathological gambling. The American Journal of Psychiatry, 155(12), 17811783.Google Scholar
Huffington Post (2013). Porn sites get more visitors each month than Netflix, Amazon and Twitter combined.Google Scholar
Joutsa, J., Johansson, J., Niemela, S., et al. (2012). Mesolimbic dopamine release is linked to symptom severity in pathological gambling. NeuroImage, 60(4), 19921999.Google Scholar
Joutsa, J., Saunavaara, J., Parkkola, R., Niemela, S. & Kaasinen, V. (2011). Extensive abnormality of brain white matter integrity in pathological gambling. Psychiatry Research, 194(3), 340346.Google Scholar
Kessler, R. C., Hwang, I., LaBrie, R., et al. (2008). DSM-IV pathological gambling in the National Comorbidity Survey ReplicationPsychological Medicine38(9), 13511360.Google Scholar
Kafka, M. P. (2010). Hypersexual disorder: a proposed diagnosis for DSM-VArchives of Sexual Behavior39(2), 377400.Google Scholar
Kim, S. H., Baik, S. H., Park, C. S., et al. (2011). Reduced striatal dopamine D2 receptors in people with Internet addiction. Neuroreport22(8), 407411.Google Scholar
Kim, S. W. (1998). Opioid antagonists in the treatment of impulse-control disorders. The Journal of Clinical Psychiatry, 59(4), 159164.CrossRefGoogle ScholarPubMed
Kim, S. W., Grant, J. E., Adson, D. E. & Shin, Y. C. (2001). Double-blind naltrexone and placebo comparison study in the treatment of pathological gambling. Biological Psychiatry, 49(11), 914921.Google Scholar
Kim, S. W., Grant, J. E., Adson, D. E., Shin, Y. C. & Zaninelli, R. (2002). A double-blind placebo-controlled study of the efficacy and safety of paroxetine in the treatment of pathological gambling. The Journal of Clinical Psychiatry, 63(6), 501507.CrossRefGoogle ScholarPubMed
Kim, Y. J., Lee, J.-Y., Oh, S., et al. (2017). Associations between prospective symptom changes and slow-wave activity in patients with Internet gaming disorder: a resting-state EEG study. Medicine, 96(8), e6178.Google Scholar
Kim, Y.-R., Son, J.-W., Lee, S.-I., et al. (2012). Abnormal brain activation of adolescent internet addict in a ball-throwing animation task: possible neural correlates of disembodiment revealed by fMRI. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 39(1), 8895.Google Scholar
King, D. L. & Delfabbro, P. H. (2016). Defining tolerance in Internet Gaming disorder: Isn’t it time?Addiction111(11), 20642065.Google Scholar
King, D. L., Delfabbro, P. H., Potenza, M. N., et al. (2018). Internet gaming disorder should qualify as a mental disorder. Australian & New Zealand Journal of Psychiatry, 52(7), 615617.Google Scholar
King, D. L., Haagsma, M. C., Delfabbro, P. H., Gradisar, M. & Griffiths, M. D. (2013). Toward a consensus definition of pathological video-gaming: a systematic review of psychometric assessment toolsClinical Psychology Review33(3), 331342.Google Scholar
King, D. L., Herd, M. C. & Delfabbro, P. H. (2017). Tolerance in Internet gaming disorder: a need for increasing gaming time or something else? Journal of Behavioral Addictions6(4), 525533.Google Scholar
Ko, C.-H., Hsieh, T.-J., Chen, C.-Y., et al. (2014). Altered brain activation during response inhibition and error processing in subjects with Internet gaming disorder: a functional magnetic imaging study. European Archives of Psychiatry and Clinical Neuroscience, 264(8), 661672.Google Scholar
Ko, C.-H., Liu, G.-C., Hsiao, S., et al. (2009). Brain activities associated with gaming urge of online gaming addiction. Journal of Psychiatric Research, 43(7), 739747.Google Scholar
Ko, C.-H., Liu, G.-C., Yen, J.-Y., et al. (2013a). Brain correlates of craving for online gaming under cue exposure in subjects with Internet gaming addiction and in remitted subjects. Addiction Biology, 18(3), 559569.Google Scholar
Ko, C.-H., Liu, G.-C., Yen, J.-Y., et al. (2013b). The brain activations for both cue-induced gaming urge and smoking craving among subjects comorbid with Internet gaming addiction and nicotine dependence. Journal of Psychiatric Research, 47(4), 486493.Google Scholar
Kober, H., Lacadie, C. M., Wexler, B. E., et al. (2016). Brain activity during cocaine craving and gambling urges: an fMRI study. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 41(2), 628637.Google Scholar
Kor, A., Fogel, Y., Reid, R. & Potenza, M. N. (2013). Should hypersexual disorder be classified as an addiction? Sexual Addiction Compulsivity, 20, 2747.Google Scholar
Kraus, S. W., Krueger, R. B., Briken, P., et al. (2018). Compulsive sexual behaviour disorder in the ICD‐11. World Psychiatry, 17, 109-110.Google Scholar
Kraus, S. W., Meshberg-Cohen, S., Martino, S., Quinones, L. J. & Potenza, M. N. (2015). Treatment of compulsive pornography use with naltrexone: a case report. American Journal of Psychiatry, 172(12), 12601261.Google Scholar
Kühn, S. & Gallinat, J. (2014). Brain structure and functional connectivity associated with pornography consumption: the brain on porn. JAMA Psychiatry, 71(7), 827834.Google Scholar
Kuss, D. J. (2013). Internet gaming addiction: current perspectives. Psychology Research and Behavior Management, 6, 125137.Google Scholar
Lang, M., Leménager, T., Streit, F., et al. (2016). Genome-wide association study of pathological gamblingEuropean Psychiatry36, 3846.Google Scholar
Lawrence, A. J., Luty, J., Bogdan, N. A., Sahakian, B. J. & Clark, L. (2009). Problem gamblers share deficits in impulsive decision‐making with alcohol‐dependent individualsAddiction104(6), 10061015.Google Scholar
Lee, J.-Y., Kim, J.-M., Kim, J. W., et al. (2010). Association between the dose of dopaminergic medication and the behavioral disturbances in Parkinson disease. Parkinsonism & Related Disorders, 16(3), 202207.Google Scholar
Leeman, R. F. & Potenza, M. N. (2012). Similarities and differences between pathological gambling and substance use disorders: a focus on impulsivity and compulsivity. Psychopharmacology, 219(2), 469490.Google Scholar
Leménager, T., Dieter, J., Hill, H., et al. (2014). Neurobiological correlates of physical self-concept and self-identification with avatars in addicted players of Massively Multiplayer Online Role-Playing Games (MMORPGs). Addictive Behaviors, 39(12), 17891797.Google Scholar
Lim, S., Ha, J., Choi, S.-W., Kang, S.-G. & Shin, Y.-C. (2012). Association study on pathological gambling and polymorphisms of dopamine D1, D2, D3, and D4 receptor genes in a Korean population. Journal of Gambling Studies, 28(3), 481491.Google Scholar
Limbrick-Oldfield, E. H., Mick, I., Cocks, R. E., et al. (2017). Neural substrates of cue reactivity and craving in gambling disorder. Translational Psychiatry, 7(1), e992.Google Scholar
Lin, F., Zhou, Y., Du, Y., et al. (2012). Abnormal white matter integrity in adolescents with internet addiction disorder: a tract-based spatial statistics study. PLoS ONE, 7(1), e30253.Google Scholar
Lin, X., Zhou, H., Dong, G. & Du, X. (2015). Impaired risk evaluation in people with Internet gaming disorder: fMRI evidence from a probability discounting task. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 56, 142148.Google Scholar
Lind, P. A., Zhu, G., Montgomery, G. W., et al. (2013). Genome‐wide association study of a quantitative disordered gambling traitAddiction Biology18(3), 511522.Google Scholar
Linnet, J., Moller, A., Peterson, E., Gjedde, A. & Doudet, D. (2011). Dopamine release in ventral striatum during Iowa Gambling Task performance is associated with increased excitement levels in pathological gambling. Addiction, 106(2), 383390.Google Scholar
Liu, G.-C., Yen, J.-Y., Chen, C.-Y., et al. (2014). Brain activation for response inhibition under gaming cue distraction in internet gaming disorder. The Kaohsiung Journal of Medical Sciences, 30(1), 4351.Google Scholar
Liu, L., Xue, G., Potenza, M. N., et al. (2017a). Dissociable neural processes during risky decision-making in individuals with Internet-gaming disorder. NeuroImage. Clinical, 14, 741749.Google Scholar
Liu, L., Yip, S. W., Zhang, J.-T., et al. (2017b). Activation of the ventral and dorsal striatum during cue reactivity in Internet gaming disorder. Addiction Biology, 22(3), 791801.Google Scholar
Lobo, D. S. S., Souza, R. P., Tong, R. P., et al. (2010). Association of functional variants in the dopamine D2-like receptors with risk for gambling behaviour in healthy Caucasian subjects. Biological Psychology, 85(1), 3337.Google Scholar
Lobo, D. S. S, Vallada, H. P., Knight, J., et al. (2007). Dopamine genes and pathological gambling in discordant sib-pairsJournal of Gambling Studies23(4), 421433.Google Scholar
Majuri, J., Joutsa, J., Johansson, J., et al. (2017a). Dopamine and opioid neurotransmission in behavioral addictions: A comparative PET study in pathological gambling and binge eating. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 42(5), 11691177.Google Scholar
Majuri, J., Joutsa, J., Johansson, J., et al. (2017b). Serotonin transporter density in binge eating disorder and pathological gambling: a PET study with [(11)C]MADAM. European Neuropsychopharmacology: The Journal of the European College of Neuropsychopharmacology, 27(12), 12811288.Google Scholar
McClure, E. A., Gipson, C. D., Malcolm, R. J., Kalivas, P. W. & Gray, K. M. (2014). Potential role of N-acetylcysteine in the management of substance use disorders. CNS Drugs, 28(2), 95106.Google Scholar
McElroy, S. L., Nelson, E. B., Welge, J. A., Kaehler, L. & Keck, P. E. J. (2008). Olanzapine in the treatment of pathological gambling: a negative randomized placebo-controlled trial. The Journal of Clinical Psychiatry, 69(3), 433440.Google Scholar
Meyer, G., Schwertfeger, J., Exton, M. S., et al. (2004). Neuroendocrine response to casino gambling in problem gamblers. Psychoneuroendocrinology, 29(10), 12721280.Google Scholar
Mick, I., Myers, J., Ramos, A. C., et al. (2016). Blunted endogenous opioid release following an oral amphetamine challenge in pathological gamblers. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 41(7), 17421750.Google Scholar
Miedl, S. F., Peters, J. & Buchel, C. (2012). Altered neural reward representations in pathological gamblers revealed by delay and probability discounting. Archives of General Psychiatry, 69(2), 177186.Google Scholar
Moccia, L., Pettorruso, M., De Crescenzo, F., et al. (2017). Neural correlates of cognitive control in gambling disorder: a systematic review of fMRI studies. Neuroscience and Biobehavioral Reviews, 78, 104116.Google Scholar
Moeller, S. J., Hajcak, G., Parvaz, M. A., et al. (2012). Psychophysiological prediction of choice: relevance to insight and drug addictionBrain 135(11), 34813494. doi:10.1093/brain/aws252Google Scholar
Moorman, D. E. & Aston-Jones, G. (2014). Orbitofrontal cortical neurons encode expectation-driven initiation of reward-seeking. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 34(31), 1023410246.Google Scholar
Myrseth, H., Molde, H., Støylen, I., et al. (2011). A pilot study of CBT versus escitalopram combined with CBT in the treatment of pathological gamblers. International Gambling Studies 11(1), 121141.Google Scholar
Na, E., Choi, I., Lee, T.-H., et al. (2017). The influence of game genre on Internet gaming disorder. Journal of Behavioral Addictions, 1–8.Google Scholar
Nordin, C. & Eklundh, T. (1999). Altered CSF 5-HIAA disposition in pathologic male gamblers. CNS Spectrums, 4(12), 2533.Google Scholar
Nutt, D. J., Lingford-Hughes, A., Erritzoe, D. & Stokes, P. R. (2015). The dopamine theory of addiction: 40 years of highs and lowsNature Reviews Neuroscience16(5), 305.Google Scholar
Pallanti, S., Bernardi, S., Quercioli, L., DeCaria, C. & Hollander, E. (2006). Serotonin dysfunction in pathological gamblers: increased prolactin response to oral m-CPP versus placebo. CNS Spectrums, 11(12), 956964.Google Scholar
Park, J. H., Hong, J. S., Han, D. H., et al. (2017a). Comparison of QEEG findings between adolescents with attention deficit hyperactivity disorder (ADHD) without comorbidity and ADHD comorbid with Internet Gaming Disorder. Journal of Korean Medical Science, 32(3), 514521.Google Scholar
Park, S. M., Lee, J. Y., Kim, Y. J., et al. (2017b). Neural connectivity in Internet gaming disorder and alcohol use disorder: a resting-state EEG coherence study. Scientific Reports, 7(1), 1333.Google Scholar
Park, J. H., Lee, Y. S., Sohn, J. H. & Han, D. H. (2016). Effectiveness of atomoxetine and methylphenidate for problematic online gaming in adolescents with attention deficit hyperactivity disorderHuman Psychopharmacology: Clinical and Experimental31(6), 427432.Google Scholar
Petry, N. M. & O'Brien, C. P. (2013). Internet gaming disorder and the DSM- 5Addiction108(7), 11861187.Google Scholar
Petry, N. M., Stinson, F. S. & Grant, B. F. (2005). Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: results from the National Epidemiologic Survey on Alcohol and Related ConditionsThe Journal of Clinical Psychiatry, 66(5), 564574.Google Scholar
Potenza, M. N. (2013a). Neurobiology of gambling behaviors. Current Opinion in Neurobiology, 23(4), 660667.CrossRefGoogle ScholarPubMed
Potenza, M. N. (2013b) How central is dopamine to pathological gambling or gambling disorder? Frontiers in Behavioral Neuroscience 7,206. (PMC3870289)Google Scholar
Potenza, M. N. (2018) Searching for replicable dopamine-related findings in gambling disorder. Biological Psychiatry, 83, 984986.Google Scholar
Potenza, M. N., Balodis, I. M., Franco, C. A., et al. (2013a). Neurobiological considerations in understanding behavioral treatments for pathological gambling. Psychology of Addictive Behaviors: Journal of the Society of Psychologists in Addictive Behaviors, 27(2), 380392.Google Scholar
Potenza, M. N., Leung, H.-C., Blumberg, H. P., et al. (2003a). An fMRI Stroop task study of ventromedial prefrontal cortical function in pathological gamblers. The American Journal of Psychiatry, 160(11), 19901994.Google Scholar
Potenza, M. N., Steinberg, M. A., Skudlarski, P., et al. (2003b). Gambling urges in pathological gambling: a functional magnetic resonance imaging study. Archives of General Psychiatry, 60(8), 828836.Google Scholar
Potenza, M. N., Walderhaug, E., Henry, S., et al. (2013b). Serotonin 1B receptor imaging in pathological gambling. The World Journal of Biological Psychiatry: The Official Journal of the World Federation of Societies of Biological Psychiatry, 14(2), 139145.Google Scholar
Potenza, M. N., Xian, H., Shah, K. R., Scherrer, J. F. & Eisen, S. A. (2005). Shared genetic contributions to pathological gambling and major depression in men. Archives of General Psychiatry, 62, 10151021.Google Scholar
Power, Y., Goodyear, B. & Crockford, D. (2012). Neural correlates of pathological gamblers preference for immediate rewards during the Iowa Gambling Task: an fMRI study. Journal of Gambling Studies, 28(4), 623636.Google Scholar
Prause, N., Steele, V. R., Staley, C., Sabatinelli, D. & Hajcak, G. (2015). Modulation of late positive potentials by sexual images in problem users and controls inconsistent with “porn addiction.” Biological Psychology, 109, 192199.Google Scholar
Qi, X., Du, X., Yang, Y., et al. (2015). Decreased modulation by the risk level on the brain activation during decision making in adolescents with internet gaming disorder. Frontiers in Behavioral Neuroscience, 9, 296.Google Scholar
Rahman, A. S., Xu, J. & Potenza, M. N. (2014). Hippocampal and amygdalar volumetric differences in pathological gambling: a preliminary study of the associations with the behavioral inhibition system. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 39(3), 738745.Google Scholar
Reid, R. C., Carpenter, B. N., Hook, J. N., et al. (2012). Report of findings in a DSM‐5 field trial for hypersexual disorderThe Journal of Sexual Medicine9(11), 28682877.Google Scholar
Reissner, K. J. & Kalivas, P. W. (2010). Using glutamate homeostasis as a target for treating addictive disordersBehavioural Pharmacology, 21(5–6), 514522.Google Scholar
Reuter, J., Raedler, T., Rose, M., et al. (2005). Pathological gambling is linked to reduced activation of the mesolimbic reward system. Nature Neuroscience, 8(2), 147148.Google Scholar
Robinson, M. J., Fischer, A. M., Ahuja, A., Lesser, E. N. & Maniates, H. (2016). Roles of “wanting” and “liking” in motivating behavior: Gambling, food, and drug addictions. Current Topics in Behavioral Neuroscience, 27, 105136.Google Scholar
Rosenberg, O., Dinur, L. K. & Dannon, P. N. (2013). Four-year follow-up study of pharmacological treatment in pathological gamblers. Clinical Neuropharmacology, 36(2), 4245.Google Scholar
Roy, A., Adinoff, B., Roehrich, L., et al. (1988). Pathological gambling: a psychobiological study. Archives of General Psychiatry, 45(4), 369373.Google Scholar
Roy, A., De Jong, J. & Linnoila, M. (1989). Extraversion in pathological gamblers: correlates with indexes of noradrenergic function. Archives of General Psychiatry, 46(8), 679681.Google Scholar
Saiz-Ruiz, J., Blanco, C., Ibanez, A., et al. (2005). Sertraline treatment of pathological gambling: a pilot study. The Journal of Clinical Psychiatry, 66(1), 2833.Google Scholar
Schimmenti, A., Guglielmucci, F., Barbasio, C. & Granieri, A. (2012). Attachment disorganization and dissociation in virtual worlds: a study on problematic Internet use among players of online role playing games. Clinical Neuropsychiatry, 9(5), 187195.Google Scholar
Schwartz, D. G. (2013). Roll the Bones: The History of Gambling. Las Vegas, NV: Winchester Books.Google Scholar
Seok, J. W. & Sohn, J. H. (2015). Neural substrates of sexual desire in individuals with problematic hypersexual behaviorFrontiers in Behavioral Neuroscience9, 321.Google Scholar
Slutske, W. S., Eisen, S., True, W. R., et al. (2000). Common genetic vulnerability for pathological gambling and alcohol dependence in men. Archives of General Psychiatry, 57(7), 666673.Google Scholar
Slutske, W. S., Ellingson, J. M., Richmond-Rakerd, L. S., Zhu, G. & Martin, N. G. (2013). Shared genetic vulnerability for disordered gambling and alcohol use disorder in men and women: evidence from a national community-based Australian Twin Study. Twin Research and Human Genetics: The Official Journal of the International Society for Twin Studies, 16(2), 525534.Google Scholar
Son, K.-L., Choi, J.-S., Lee, J., et al. (2015). Neurophysiological features of Internet gaming disorder and alcohol use disorder: a resting-state EEG study. Translational Psychiatry, 5, e628.Google Scholar
Song, J., Park, J. H., Han, D. H., et al. (2016). Comparative study of the effects of bupropion and escitalopram on Internet gaming disorder. Psychiatry and Clinical Neurosciences, 70(11), 527535.Google Scholar
Steeves, T. D. L., Miyasaki, J., Zurowski, M., et al. (2009). Increased striatal dopamine release in Parkinsonian patients with pathological gambling: a [11C] raclopride PET studyBrain132(5), 13761385.Google Scholar
Sun, Y., Ying, H., Seetohul, R. M., et al. (2012). Brain fMRI study of crave induced by cue pictures in online game addicts (male adolescents). Behavioural Brain Research, 233(2), 563576.Google Scholar
Tanabe, J., Thompson, L., Claus, E., et al. (2007). Prefrontal cortex activity is reduced in gambling and nongambling substance users during decision-making. Human Brain Mapping, 28(12), 12761286.Google Scholar
Thalemann, R., Wölfling, K. & Grüsser, S. M. (2007). Specific cue reactivity on computer game-related cues in excessive gamersBehavioral Neuroscience121(3), 614.Google Scholar
Thomas, A., Bonanni, L., Gambi, F., Di Iorio, A. & Onofrj, M. (2010). Pathological gambling in Parkinson disease is reduced by amantadine. Annals of Neurology, 68(3), 400404.Google Scholar
Tian, M., Chen, Q., Zhang, Y., et al. (2014). PET imaging reveals brain functional changes in internet gaming disorder. European Journal of Nuclear Medicine and Molecular Imaging41(7), 13881397.Google Scholar
van Eimeren, T., Pellecchia, G., Cilia, R., et al. (2010). Drug-induced deactivation of inhibitory networks predicts pathological gambling in PD. Neurology, 75(19), 17111716.Google Scholar
van Holst, R. J., de Ruiter, M. B., van den Brink, W., Veltman, D. J. & Goudriaan, A. E. (2012). A voxel-based morphometry study comparing problem gamblers, alcohol abusers, and healthy controls. Drug and Alcohol Dependence, 124(1–2), 142148.Google Scholar
Voon, V., Mole, T. B., Banca, P., et al. (2014). Neural correlates of sexual cue reactivity in individuals with and without compulsive sexual behaviours. PLoS ONE, 9(7), e102419.Google Scholar
Voon, V., Napier, T. C., Frank, M. J., et al. (2017). Impulse control disorders and levodopa-induced dyskinesias in Parkinson’s disease: an update. The Lancet. Neurology, 16(3), 238250.Google Scholar
Voon, V., Sohr, M., Lang, A. E., et al. (2011). Impulse control disorders in Parkinson disease: a multicenter case-control study. Annals of Neurology, 69(6), 986996.Google Scholar
Wang, Y., Wu, L., Wang, L., et al. (2017). Impaired decision-making and impulse control in Internet gaming addicts: evidence from the comparison with recreational Internet game users. Addiction Biology, 22(6), 16101621.Google Scholar
Weintraub, D. & Claassen, D. O. (2017). Impulse control and related disorders in Parkinson’s disease. International Review of Neurobiology, 133, 679717.Google Scholar
Weintraub, D., Koester, J., Potenza, M. N., et al. for the DOMINION Study Group (2010). Impulse control disorders in Parkinson's disease: a cross-sectional study of 3,090 patients. Archives of Neurology, 67, 589595.Google Scholar
Wexler, B. E., Gottschalk, C. H., Fulbright, R. K., et al. (2001). Functional magnetic resonance imaging of cocaine craving. American Journal of Psychiatry158(1), 8695.Google Scholar
Wölfling, K., Morsen, C. P., Duven, E., et al. (2011). To gamble or not to gamble: at risk for craving and relapse--learned motivated attention in pathological gambling. Biological Psychology, 87(2), 275281.Google Scholar
Worhunsky, P. D., Malison, R. T., Rogers, R. D. & Potenza, M. N. (2014). Altered neural correlates of reward and loss processing during simulated slot-machine fMRI in pathological gambling and cocaine dependence. Drug and Alcohol Dependence, 145, 7786.Google Scholar
Xian, H., Giddens, J. L., Scherrer, J. F., Eisen, S. A. & Potenza, M. N. (2014). Environmental factors selectively impact co-occurrence of problem/pathological gambling with specific drug-use disorders in male twins. Addiction, 109(4), 635644.Google Scholar
Yang, B.-Z., Balodis, I. M., Lacadie, C. M., Xu, J. & Potenza, M. N. (2016). A preliminary study of DBH (encoding dopamine beta-hydroxylase) genetic variation and neural correlates of emotional and motivational processing in individuals with and without pathological gambling. Journal of Behavioral Addictions, 5(2), 282292.Google Scholar
Yao, Y.-W., Liu, L., Ma, S.-S., et al. (2017). Functional and structural neural alterations in Internet gaming disorder: a systematic review and meta-analysis. Neuroscience and Biobehavioral Reviews, 83, 313324.Google Scholar
Yau, Y. H. C., Crowley, M. J., Mayes, L. C. & Potenza, M. N. (2012). Are Internet use and video-game-playing addictive behaviors? Biological, clinical and public health implications for youths and adults. Minerva Psychiatrica, 53(3), 153170.Google Scholar
Yip, S. W., Lacadie, C., Xu, J., et al. (2013). Reduced genual corpus callosal white matter integrity in pathological gambling and its relationship to alcohol abuse or dependence. The World Journal of Biological Psychiatry: The Official Journal of the World Federation of Societies of Biological Psychiatry, 14(2), 129138.Google Scholar
Yip, S. W., Morie, K. P., Xu, J., et al. (2017). Shared microstructural features of behavioral and substance addictions revealed in areas of crossing fibers. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 2(2), 188195.Google Scholar
Yip, S. W. & Potenza, M. N. (2014). Treatment of gambling disorders. Current Treatment Options in Psychiatry, 1(2), 189203.Google Scholar
Yip, S. W., Worhsunky, P. D., Xu, J., et al. (2018). Gray-matter relationships to diagnostic and transdiagnostic features of drug and behavioral addictions. Addiction Biology, 23(1), 394402.Google Scholar
Youh, J., Hong, J. S., Han, D. H., et al. (2017). Comparison of electroencephalography (EEG) coherence between major depressive disorder (MDD) without comorbidity and MDD comorbid with Internet Gaming Disorder. Journal of Korean Medical Science, 32(7), 11601165.Google Scholar
Young, K. (2009). Internet addiction: diagnosis and treatment considerationsJournal of Contemporary Psychotherapy39(4), 241246.Google Scholar
Yuan, K., Cheng, P., Dong, T., et al. (2013). Cortical thickness abnormalities in late adolescence with online gaming addiction. PLoS ONE, 8(1), e53055.Google Scholar
Yuan, K., Qin, W., Wang, G., Zeng, F., Zhao, L., Yang, X., … Tian, J. (2011). Microstructure abnormalities in adolescents with internet addiction disorder. PLoS ONE, 6(6), e20708.Google Scholar
Yuan, K., Qin, W., Yu, D., et al. (2016). Core brain networks interactions and cognitive control in internet gaming disorder individuals in late adolescence/early adulthood. Brain Structure & Function, 221(3), 14271442.Google Scholar
Zack, M. & Poulos, C. X. (2007). A D2 antagonist enhances the rewarding and priming effects of a gambling episode in pathological gamblers. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 32(8), 16781686.Google Scholar
Zhang, J.-T., Yao, Y.-W., Potenza, M. N., et al. (2016). Effects of craving behavioral intervention on neural substrates of cue-induced craving in Internet gaming disorder. NeuroImage. Clinical, 12, 591599.Google Scholar

References

Albarracín, D., Johnson, B. T., Fishbein, M. & Muellerleile, P. A. (2001). Theories of reasoned action and planned behavior as models of condom use: A meta-analysis. Psychological Bulletin, 127(1), 142161.Google Scholar
Ames, S. L., Grenard, J. L., He, Q., et al. (2014b). Functional imaging of an alcohol-Implicit Association Test (IAT). Addiction Biology, 19(3), 467481. doi:10.1111/adb.12071Google Scholar
Ames, S. L., Grenard, J. L., Stacy, A. W., et al. (2013). Functional imaging of implicit marijuana associations during performance on an Implicit Association Test (IAT). Behavioural Brain Research, 256, 494502. doi:10.1016/j.bbr.2013.09.013Google Scholar
Ames, S. L., Wong, S. W., Bechara, A., et al. (2014a). Neural correlates of a Go/No Go task with alcohol stimuli in light and heavy young drinkers. Behavioural Brain Research, 274, 382389Google Scholar
Ames, S. L., Xie, B., Aragon, J. & Stacy, A. W. (2020). Moderating effects of control functions on implicit cognitive processes in health behavior: A meta-analysis. Submitted for publication.Google Scholar
Ames, S. L., Xie, B., Shono, Y. & Stacy, A. W. (2017). Adolescents at risk for drug abuse: A 3-year dual-process analysis. Addiction, 112(5), 852863. doi:10.1111/add.13742Google Scholar
Baddeley, A. D. (2001). Is working memory still working? American Psychologist, 11, 851864. doi:10.1037/0003-066X.56.11.851Google Scholar
Back, S. E., Gros, D. F., McCauley, J. L., et al. (2014). Laboratory-induced cue reactivity among individuals with prescription opioid dependence. Addictive Behaviors, 39(8), 12171223. doi:10.1016/j.addbeh.2014.04.007Google Scholar
Bargh, J. A., Chen, M. & Burrows, L. (1996). Automaticity of social behavior: Direct effects of trait construct and stereotype activation on action. Journal of Personality and Social Psychology, 71(2), 230244.Google Scholar
Bargh, J. A. & Morsella, E. (2008). The unconscious mind. Perspectives on Psychological Science, 3(1), 7379.Google Scholar
Bazzaz, M. M., Fadardi, J. S. & Parkinson, J. (2017). Efficacy of the attention control training program on reducing attentional bias in obese and overweight dieters. Appetite, 108, 111. doi:10.1016/j.appet.2016.08.114Google Scholar
Bechara, A. (2005). Decision making, impulse control and loss of willpower to resist drugs: A neurocognitive perspective. Nature Neuroscience, 8(11), 14581463. doi:10.1038/nn1584Google Scholar
Berridge, K. C. & Robinson, T. E. (2016). Liking, wanting, and the incentive-sensitization theory of addiction. American Psychologist, 71(8), 670679. doi:10.1037/amp0000059Google Scholar
Binder, J. R. & Desai, R. H. (2011). The neurobiology of semantic memory. Trends in Cognitive Sciences, 15(11), 527536. doi:10.1016/j.tics.2011.10.001Google Scholar
Binder, J. R., Desai, R. H., Graves, W. W. & Conant, L. L. (2009). Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cerebral Cortex, 19(12), 27672796. doi:10.1093/cercor/bhp055Google Scholar
Blanton, H., Burrows, C. N. & Jaccard, J. (2016). To accurately estimate implicit influences on health behavior, accurately estimate explicit influences. Health Psychology, 35(8), 856860. doi:10.1037/hea0000348; 10.1037/hea0000348.supp (Supplemental).Google Scholar
Bousfield, W. A., Whitmarsh, G. A. & Danick, J. J. (1958). Partial response identities in verbal generalization. Psychological Reports, 4(3), 703713.Google Scholar
Brewer, D. D., Catalano, R. F., Haggerty, K., Gainey, R. R. & Fleming, C. B. (1998). A meta-analysis of predictors of continued drug use during and after treatment for opiate addiction. Addiction, 93(1), 7392. doi:10.1046/j.1360-0443.1998.931738.xGoogle Scholar
Buckner, R. L., Koutstaal, W., Schacter, D. L. & Rosen, B. R. (2000). Functional MRI evidence for a role of frontal and inferior temporal cortex in amodal components of priming. Brain: A Journal of Neurology, 123(3), 620640.Google Scholar
Burton, P. C. & Martin, R. C. (2006). Semantic retrieval versus selection in verb generation: An fMRI investigation. Paper presented at the 47th Annual Meeting of the Psychonomic Society, Houston, Texas.Google Scholar
Cabeza, R. & Lennartson, E. R. (2005). False memory across languages: Implicit associative response vs fuzzy trace views. Memory, 13(1), 15.Google Scholar
Cabeza, R. & Nyberg, L. (2000). Imaging cognition II: An empirical review of 275 PET and fMRI studies. Journal of Cognitive Neuroscience, 12(1), 147.Google Scholar
Cacioppo, J. T. & Berntson, G. G. (1999). The affect system: Architecture and operating characteristics. Current Directions in Psychological Science, 8, 133137.Google Scholar
Cacioppo, J. T. & Gardner, W. L. (1999). Emotion. Annual Review of Psychology, 50, 191214.Google Scholar
Chassin, L., Presson, C. C., Sherman, S. J., Seo, D.-C. & Macy, J. T. (2010). Implicit and explicit attitudes predict smoking cessation: Moderating effects of experienced failure to control smoking and plans to quit. Psychology of Addictive Behaviors, 24(4), 670679. doi:10.1037/a0021722Google Scholar
Chein, J. M. & Schneider, W. (2005). Neuroimaging studies of practice-related change: fMRI and meta-analytic evidence of a domain-general control network for learning. Cognitive Brain Research, 25(3), 607623.Google Scholar
Coallier, É, Michelet, T. & Kalaska, J. F. (2015). Dorsal premotor cortex: Neural correlates of reach target decisions based on a color-location matching rule and conflicting sensory evidence. Journal of Neurophysiology, 113(10), 35433573. doi:10.1152/jn.00166.2014Google Scholar
Cousijn, J., Wiers, R. W., Ridderinkhof, K. R., et al. (2014). Effect of baseline cannabis use and working-memory network function on changes in cannabis use in heavy cannabis users: A prospective fMRI study. Human Brain Mapping, 35(5), 24702482. doi:10.1002/hbm.22342Google Scholar
Cowan, N. (1988). Evolving conceptions of memory storage, selective attention, and their mutual constraints within the human information-processing system. Psychological Bulletin, 104(2), 163191. doi:10.1037/0033-2909.104.2.163Google Scholar
Cox, W. M., Fadardi, J. S. & Pothos, E. M. (2006). The addiction-Stroop test: Theoretical considerations and procedural recommendations. Psychological Bulletin, 132(3), 443476. doi:10.1037/0033-2909.132.3.443Google Scholar
Cramer, P. (1970a). Associative strength as a determinant of mediated priming. Journal of Verbal Learning and Verbal Behavior, 9(6), 658664. doi:http://dx.doi.org/10.1016/S0022-5371(70)80029-9Google Scholar
Cramer, P. (1970b). Semantic generalization: Demonstration of an associative gradient. Journal of Experimental Psychology, 84(1), 164172. doi:10.1037/h0028935Google Scholar
Crescentini, C., Shallice, T. & Macaluso, E. (2010). Item retrieval and competition in noun and verb generation: An fMRI study. Journal of Cognitive Neuroscience, 22(6), 11401157. doi:10.1162/jocn.2009.21255.Google Scholar
Cristofori, I. & Grafman, J. (2017). Neural underpinnings of the human belief system. In Angel, H., Oviedo, L., Paloutzian, R. F., et al. (Eds.), Processes of Believing: The Acquisition, Maintenance, and Change in Creditions. Cham, Switzerland: Springer International Publishing, pp. 111123. doi:10.1007/978-3-319-50924-2_8Google Scholar
de Jong, P. J., Wiers, R. W., van de Braak, M. & Huijding, J. (2007). Using the Extrinsic Affective Simon Test as a measure of implicit attitudes towards alcohol: Relationship with drinking behavior and alcohol problems. Addictive Behaviors, 32(4), 881887. doi:10.1016/j.addbeh.2006.06.017Google Scholar
Deese, J. (1959). On the prediction of occurrence of particular verbal intrusions in immediate recall. Journal of Experimental Psychology, 58(1), 1722.Google Scholar
Deese, J. (1962). On the structure of associative meaning. Psychological Review, 69(3), 161175. doi:10.1037/h0045842Google Scholar
Derzon, J. H. & Lipsey, M. W. (1999). Predicting tobacco use to age 18: A synthesis of longitudinal research. Addiction, 94(7), 9951006. doi:10.1046/j.1360-0443.1999.9479955.xGoogle Scholar
Eldridge, L. L., Masterman, D. & Knowlton, B. J. (2002). Intact implicit habit learning in Alzheimer’s disease. Behavioral Neuroscience, 116(4), 722726.Google Scholar
Eriksson, J., Vogel, E.K., Lansner, A., Bergström, F. & Nyberg, L. (2015). Neurocognitive architecture of working memory. Neuron, 88(1), 3346. doi:10.1016/j.neuron.2015.09.020Google Scholar
Everitt, B. J. & Robbins, T. W. (2016). Drug addiction: Updating actions to habits to compulsions ten years on. Annual Review of Psychology, 67, 2350. doi:10.1146/annurev-psych-122414-033457Google Scholar
Fillmore, M. T., Vogel-Sprott, M. & Gavrilescu, D. (1999). Alcohol effects on intentional behavior: Dissociating controlled and automatic influences. Experimental and Clinical Psychopharmacology, 7(4), 372378.Google Scholar
Fishbein, D. H., Ridenour, T. A., Stahl, M. & Sussman, S. (2016). The full translational spectrum of prevention science: Facilitating the transfer of knowledge to practices and policies that prevent behavioral health problems. Translational Behavioral Medicine, 6(1), 516. doi:10.1007/s13142-015-0376-2Google Scholar
Foerde, K., Knowlton, B. J. & Poldrack, R. A. (2006). Modulation of competing memory systems by distraction. PNAS Proceedings of the National Academy of Sciences of the United States of America, 103(31), 1177811783. doi:10.1073/pnas.0602659103Google Scholar
Frankland, L., Bradley, B. P. & Mogg, K. (2016). Time course of attentional bias to drug cues in opioid dependence. Psychology of Addictive Behaviors, 30(5), 601606. doi:10.1037/adb0000169; 10.1037/adb0000169.supp (Supplemental)Google Scholar
Fuster, J. M. (2001). The prefrontal cortex – An update: Time is of the essence. Neuron, 30, 319333.Google Scholar
Gabrieli, J. D. E., Stebbins, G. T., Singh, J., Willingham, D. B. & Goetz, C.G. (1997). Intact mirror-tracing and impaired rotary-pursuit skill learning in patients with Huntington’s disease: Evidence for dissociable memory systems in skill learning. Neuropsychology, 11(2), 272281. doi:10.1037/0894-4105.11.2.272Google Scholar
Gardner, B., de Bruijn, G. & Lally, P. (2011). A systematic review and meta-analysis of applications of the Self-Report Habit Index to nutrition and physical activity behaviours. Annals of Behavioral Medicine, 42(2), 174187. doi:10.1007/s12160-011-9282-0Google Scholar
Gardner, B., de Bruijn, G. & Lally, P. (2012). Habit, identity, and repetitive action: A prospective study of binge-drinking in UK students. British Journal of Health Psychology, 17(3), 565581. doi:10.1111/j.2044-8287.2011.02056.xGoogle Scholar
Garland, E. L., Franken, I. H., Sheetz, J. J. & Howard, M. O. (2012). Alcohol attentional bias is associated with autonomic indices of stress-primed alcohol cue-reactivity in alcohol-dependent patients. Experimental and Clinical Psychopharmacology, 20(3), 225235. doi:10.1037/a0027199Google Scholar
Glenberg, A. M., Witt, J. K. & Metcalfe, J. (2013). From the revolution to embodiment: 25 years of cognitive psychology. Perspectives on Psychological Science, 8(5), 573585. doi:10.1177/1745691613498098Google Scholar
Glock, S., Klapproth, F. & Müller, B. C. N. (2015). Promoting responsible drinking? A mass media campaign affects implicit but not explicit alcohol-related cognitions and attitudes. British Journal of Health Psychology, 20(3), 482497. doi:10.1111/bjhp.12130Google Scholar
Graf, P. & Schacter, D. L. (1985). Implicit and explicit memory for new associations in normal and amnesic subjects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11(3), 501518.Google Scholar
Greenwald, A. G. & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102(1), 427.Google Scholar
Greenwald, A. G., McGhee, D. E. & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74(6), 14641480.Google Scholar
Greenwald, A. G., Nosek, B. A. & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85(2), 197216.Google Scholar
Grenard, J. L., Ames, S. L., Wiers, R. W., et al. (2008). Working memory capacity moderates the predictive effects of drug-related associations on substance use. Psychology of Addictive Behaviors , 22(3), 426432. doi:10.1037/0893-164X.22.3.426Google Scholar
Hagger, M. S., Chan, D. K. C., Protogerou, C. & Chatzisarantis, N. L. D. (2016). Using meta-analytic path analysis to test theoretical predictions in health behavior: An illustration based on meta-analyses of the theory of planned behavior. Preventive Medicine, 89, 154161. doi:10.1016/j.ypmed.2016.05.020Google Scholar
Hanlon, C. A., Dowdle, L. T., Naselaris, T., Canterberry, M. & Cortese, B. M. (2014). Visual cortex activation to drug cues: A meta-analysis of functional neuroimaging papers in addiction and substance abuse literature. Drug and Alcohol Dependence, 143, 206212. doi:10.1016/j.drugalcdep.2014.07.028Google Scholar
Hélie, S., Ell, S. W. & Ashby, F. G. (2015). Learning robust cortico-cortical associations with the basal ganglia: An integrative review. Cortex: A Journal Devoted to the Study of the Nervous System and Behavior, 64, 123135. doi:10.1016/j.cortex.2014.10.011Google Scholar
Henke, K. (2010). A model for memory systems based on processing modes rather than consciousness. Nature Reviews Neuroscience, 11(7), 523532. doi:10.1038/nrn2850Google Scholar
Hintzman, D. L. (1986). “Schema abstraction” in a multiple-trace memory model. Psychological Review, 93(4), 411428.Google Scholar
Hommel, B. & Wiers, R. W. (2017). Towards a unitary approach to human action control. Trends in Cognitive Sciences, 21(12), 940949. doi:10.1016/j.tics.2017.09.009Google Scholar
Hoshi, E. & Tanji, J. (2007). Distinctions between dorsal and ventral premotor areas: Anatomical connectivity and functional properties. Current Opinion in Neurobiology, 17(2), 234242. doi:10.1016/j.conb.2007.02.003Google Scholar
Hovington, C. L. & Brouwer, B. (2010). Guided motor imagery in healthy adults and stroke: Does strategy matter? Neurorehabilitation and Neural Repair, 24(9), 851857. doi:10.1177/1545968310374190Google Scholar
Hutchison, K. A., Balota, D. A., Cortese, M. J. & Watson, J. M. (2008). Predicting semantic priming at the item level. The Quarterly Journal of Experimental Psychology, 61(7), 10361066. doi:10.1080/17470210701438111Google Scholar
Ishida, H., Inoue, K., Takada, M. & Hoshi, E. (2016). Origins of multisynaptic projections from the basal ganglia to the forelimb region of the ventral premotor cortex in macaque monkeys. European Journal of Neuroscience, 43(2), 258269. doi:10.1111/ejn.13127Google Scholar
Kahneman, D. (2003). A perspective on judgment and choice: Mapping bounded rationality. American Psychologist, 58(9), 697720.Google Scholar
Kahneman, D. (2011). Thinking, Fast and Slow (1st edition). New York, NY: Farrar, Straus and Giroux.Google Scholar
Kandel, E. R. & Kandel, D. B. (2014). A molecular basis for nicotine as a gateway drugThe New England Journal of Medicine371(10), 932943. doi:10.1056/NEJMsa1405092Google Scholar
Kelly, A. B., Masterman, P. W. & Marlatt, G. A. (2005). Alcohol-related associative strength and drinking behaviours: Concurrent and prospective relationships. Drug and Alcohol Review, 24(6), 489498.Google Scholar
Kharitonova, M., Winter, W. & Sheridan, M. A. (2015). As working memory grows: A developmental account of neural bases of working memory capacity in 5- to 8-year old children and adults. Journal of Cognitive Neuroscience, 27(9), 17751788. doi:10.1162/jocn_a_00824Google Scholar
Khurana, A., Romer, D., Betancourt, L. M., et al. (2012). Working memory ability predicts trajectories of early alcohol use in adolescents: The mediational role of impulsivity. Addiction, 108, 506515.Google Scholar
Khurana, A., Romer, D., Betancourt, L. M., et al. (2015). Stronger working memory reduces sexual risk taking in adolescents, even after controlling for parental influences. Child Development, 86(4), 11251141. doi:10.1111/cdev.12383Google Scholar
Khurana, A., Romer, D., Betancourt, L. M. & Hurt, H. (2017). Working memory ability and early drug use progression as predictors of adolescent substance use disorders. Addiction, 112(7), 12201228. doi:10.1111/add.13792Google Scholar
Knowlton, B. J., Mangels, J. A. & Squire, L. R. (1996). A neostriatal habit learning system in humans. Science, 273(5280), 1399–402.Google Scholar
Koob, G. F. & Le Moal, M. (2008). Addiction and the brain antireward system. Annual Review of Psychology, 59, 2953. doi:10.1146/annurev.psych.59.103006.093548Google Scholar
Koob, G. F. & Volkow, N. D. (2016). Neurobiology of addiction: A neurocircuitry analysis. The Lancet Psychiatry, 3(8), 760773. doi:10.1016/S2215-0366(16)00104-8Google Scholar
Kosslyn, S. M., Ganis, G. & Thompson, W. L. (2001). Neural foundations of imagery. Nature Reviews Neuroscience, 2(9), 635642. doi:10.1038/35090055Google Scholar
Krank, M., Wall, A.-M., Stewart, S. H., Wiers, R. W. & Goldman, M. S. (2005). Context effects on alcohol cognitions. Alcoholism: Clinical & Experimental Research, 29(2), 196206. doi:10.1097/01.ALC.0000153545.36787.C8Google Scholar
Landis, D., Triandis, H. C. & Adamopoulos, J. (1978). Habit and behavioral intentions as predictors of social behavior. The Journal of Social Psychology, 106(2), 227237. doi:10.1080/00224545.1978.9924174Google Scholar
Levy, D. A., Stark, C. E. L. & Squire, L. R. (2004). Intact conceptual priming in the absence of declarative memory. Psychological Science, 15(10), 680686. doi:10.1111/j.0956-7976.2004.00740.xGoogle Scholar
Lindgren, K. P., Neighbors, C., Gasser, M. L., Ramirez, J. J. & Cvencek, D. (2017). A review of implicit and explicit substance self-concept as a predictor of alcohol and tobacco use and misuse. The American Journal of Drug and Alcohol Abuse, 43(3), 237246. doi:10.1080/00952990.2016.1229324Google Scholar
Lindgren, K. P., Neighbors, C., Teachman, B. A., et al. (2016). Implicit alcohol associations, especially drinking identity, predict drinking over time. Health Psychology, 35(8), 908918. doi:10.1037/hea0000396; 10.1037/hea0000396.supp (Supplemental).Google Scholar
Luttrell, A., Stillman, P. E., Hasinski, A. E. & Cunningham, W. A. (2016). Neural dissociations in attitude strength: Distinct regions of cingulate cortex track ambivalence and certainty. Journal of Experimental Psychology: General, 145(4), 419433. doi:10.1037/xge0000141; 10.1037/xge0000141.supp (Supplemental).Google Scholar
Mahler, S. V. & Berridge, K. C. (2012). What and when to “want”? Amygdala-based focusing of incentive salience upon sugar and sex. Psychopharmacology, 221(3), 407426. doi:10.1007/s00213-011-2588-6Google Scholar
Marins, T. F., Rodrigues, E. C., Engel, A., et al. (2015). Enhancing motor network activity using real-time functional MRI neurofeedback of left premotor cortex. Frontiers in Behavioral Neuroscience, 9. doi:10.3389/fnbeh.2015.00341Google Scholar
McEachan, R. R. C., Conner, M., Taylor, N. J. & Lawton, R. J. (2011). Prospective prediction of health-related behaviours with the Theory of Planned Behaviour: A meta-analysis. Health Psychology Review, 5(2), 97144. doi:10.1080/17437199.2010.521684Google Scholar
Miller, K. J., Schalk, G., Fetz, E. E., et al. (2010). Cortical activity during motor execution, motor imagery, and imagery-based online feedback. Proceedings of the National Academy of Sciences of the United States of America, 107(9), 44304435. doi:10.1073/pnas.0913697107Google Scholar
Mirabella, G., Pani, P. & Ferraina, S. (2011). Neural correlates of cognitive control of reaching movements in the dorsal premotor cortex of rhesus monkeys. Journal of Neurophysiology, 106(3), 14541466. doi:10.1152/jn.00995.2010Google Scholar
Moore, P. J., Turner, R., Park, C. L. & Adler, N. E. (1996). The impact of behavior and addiction on psychological models of cigarette and alcohol use during pregnancy. Addictive Behaviors, 21(5), 645658. doi:10.1016/0306-4603(95)00100-XGoogle Scholar
Moors, A., Spruyt, A. & De Houwer, J. (2010). In search of a measure that qualifies as implicit: Recommendations based on a decompositional view of automaticity. In Gawronski, B. & Payne, B. K. (Eds.), Handbook of Implicit Social Cognition: Measurement, Theory, and Applications. New York, NY: The Guilford Press, pp. 1937.Google Scholar
Neal, D. T., Wood, W. & Drolet, A. (2013). How do people adhere to goals when willpower is low? The profits (and pitfalls) of strong habits. Journal of Personality and Social Psychology, 104(6), 959975. doi:10.1037/a0032626Google Scholar
Nelson, D. L., McKinney, V. M., Gee, N. R. & Janczura, G. A. (1998). Interpreting the influence of implicitly activated memories on recall and recognition. Psychological Review, 105(2), 299324. doi:10.1037/0033-295X.105.2.299Google Scholar
Noël, X., Brevers, D. & Bechara, A. (2013). A neurocognitive approach to understanding the neurobiology of addiction. Current Opinion in Neurobiology, 23(4), 632638. doi:10.1016/j.conb.2013.01.018Google Scholar
Norman, P. (2011). The theory of planned behavior and binge drinking among undergraduate students: Assessing the impact of habit strength. Addictive Behaviors, 36(5), 502507. doi:10.1016/j.addbeh.2011.01.025Google Scholar
Nosek, B. A., Greenwald, A. G. & Banaji, M. R. (2005). Understanding and using the Implicit Association Test: II. Method variables and construct validity. Personality and Social Psychology Bulletin, 31(2), 166180.Google Scholar
Nosek, B. A., Greenwald, A. G. & Banaji, M. R. (2007). The Implicit Association Test at age 7: A methodological and conceptual review. In Bargh, J. A. (Ed.), Automatic Processes in Social Thinking and Behavior. New York, NY: Psychology Press Ltd, pp. 265292.Google Scholar
Ntoumanis, N. D., Ng, J. Y. Y., Barkoukis, V. & Backhouse, S. (2014). Personal and psychosocial predictors of doping use in physical activity settings: A meta-analysis. Sports Medicine, 44(11), 16031624. doi:10.1007/s40279-014-0240-4Google Scholar
Oguri, T., Sawamoto, N., Tabu, H., et al. (2013). Overlapping connections within the motor cortico-basal ganglia circuit: fMRI-tractography analysis. Neuroimage, 78, 353362. doi:10.1016/j.neuroimage.2013.04.026Google Scholar
Oliver, J. A., Jentink, K. G., Drobes, D. J. & Evans, D. E. (2016). Smokers exhibit biased neural processing of smoking and affective images. Health Psychology, 35(8), 866869. doi:10.1037/hea0000350; 10.1037/hea0000350.supp (Supplemental).Google Scholar
Orbell, S. & Verplanken, B. (2010). The automatic component of habit in health behavior: Habit as cue-contingent automaticity. Health Psychology, 29(4), 374383. doi:10.1037/a0019596Google Scholar
Ouellette, J. & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124(1), 5474. doi:10.1037//0033-2909.124.1.54Google Scholar
Packard, M. G. & Goodman, J. (2013). Factors that influence the relative use of multiple memory systems. Hippocampus, 23(11), 10441052. doi:10.1002/hipo.22178Google Scholar
Packard, M. G. & Knowlton, B. J. (2002). Learning and memory functions of the basal ganglia. Annual Review of Neuroscience, 25(1), 563593. doi:10.1146/annurev.neuro.25.112701.142937Google Scholar
Palfai, T. P. & Ostafin, B. D. (2003). Alcohol-related motivational tendencies in hazardous drinkers: Assessing implicit response tendencies using the modified-IAT. Behaviour Research and Therapy, 41(10), 11491162.Google Scholar
Patterson, K., Nestor, P. J. & Rogers, T. T. (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. Nature Reviews Neuroscience, 8(12), 976987. doi:10.1038/nrn2277Google Scholar
Payne, B. K. & Gawronski, B. (2010). A history of implicit social cognition: Where is it coming from? Where is it now? Where is it going? In Gawronski, B. & Payne, B. K. (Eds.), Handbook of Implicit Social Cognition: Measurement, Theory, and Applications . New York, NY: The Guilford Press, pp. 115.Google Scholar
Payne, B. K., Lee, K. M., Giletta, M. & Prinstein, M. J. (2016). Implicit attitudes predict drinking onset in adolescents: Shaping by social norms. Health Psychology, 35(8), 829836. doi:10.1037/hea0000353; 10.1037/hea0000353.supp (Supplemental).Google Scholar
Peeters, M., Monshouwer, K., Janssen, T., Wiers, R. W. & Vollebergh, W. A. M. (2014). Working memory and alcohol use in at-risk adolescents: A 2-year follow-up. Alcoholism: Clinical & Experimental Research, 38(4), 11761183. doi:10.1111/acer.12339Google Scholar
Pilgramm, S., de Haas, B., Helm, F., et al. (2016). Motor imagery of hand actions: Decoding the content of motor imagery from brain activity in frontal and parietal motor areas. Human Brain Mapping, 37(1), 8193. doi:10.1002/hbm.23015Google Scholar
Raichle, M. E., Fiez, J. A., Videen, T. O., et al. (1994). Practice-related changes in human brain functional anatomy during nonmotor learning. Cerebral Cortex, 4(1), 826. doi:10.1093/cercor/4.1.8Google Scholar
Ray, S., Bates, M. E. & Ely, B. M. (2004). Alcohol's dissociation of implicit and explicit memory processes: Implications of a parallel distributed processing model of semantic priming. Experimental and Clinical Psychopharmacology, 12(2), 118125.Google Scholar
Reder, L. M., Park, H. & Kieffaber, P. D. (2009). Memory systems do not divide on consciousness: Reinterpreting memory in terms of activation and binding. Psychological Bulletin, 135(1), 2349. doi:10.1037/a0013974Google Scholar
Reich, R. R., Goldman, M. S. & Noll, J. A. (2004). Using the false memory paradigm to test two key elements of alcohol expectancy theory. Experimental and Clinical Psychopharmacology, 12(2), 102110.Google Scholar
Robin, N., Dominique, L., Toussaint, L., et al. (2007). Effect of motor imagery training on service return accuracy in tennis: The role of imagery ability. International Journal of Sport and Exercise Psychology, 5(2), 175186.Google Scholar
Robinson, M. J. F., 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, 119130. doi:10.1016/j.bbr.2014.03.004Google Scholar
Robinson, K. J. & Roediger, H. L. (1997). Associative processes in false recall and false recognition. Psychological Science, 8(3), 231237.Google Scholar
Roediger, H. L., Watson, J. M., McDermott, K. B. & Gallo, D. A. (2001). Factors that determine false recall: A multiple regression analysis. Psychonomic Bulletin & Review, 8(3), 385407.Google Scholar
Rolls, E. T. (2000). Memory systems in the brain. Annual Review of Psychology, 51, 599630. doi:10.1146/annurev.psych.51.1.599Google Scholar
Rotter, J. B. (1954). Social Learning and Clinical Psychology. Englewood Cliffs, NJ: Prentice-Hall, Inc. doi:10.1037/10788-000Google Scholar
Salemink, E. & Wiers, R. W. (2014). Alcohol-related memory associations in positive and negative affect situations: Drinking motives, working memory capacity, and prospective drinking. Psychology of Addictive Behaviors, 28(1), 105113. doi:10.1037/a0032806Google Scholar
Schacter, D. L. (1985). Priming of old and new knowledge in amnesic patients and normal subjects. Annals of the New York Academy of Sciences, 444, 4153.Google Scholar
Schneider, W. & Chein, J. M. (2003). Controlled & automatic processing: Behavior, theory, and biological mechanisms. Cognitive Science, 27(3), 525559. doi:10.1016/S0364-0213(03)00011-9Google Scholar
Schneider, W. & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review, 84(1), 166. doi:10.1037/0033-295X.84.1.1Google Scholar
Scoville, W. B. & Milner, B. (1957). Loss of recent memory after bilateral hippocampal lesions. Journal of Neurology, Neurosurgery & Psychiatry, 20(1), 1121. doi:10.1136/jnnp.20.1.11Google Scholar
Seger, C. A., Desmond, J. E., Glover, G. H. & Gabrieli, J. D. (2000). Functional magnetic resonance imaging evidence for right-hemisphere involvement in processing unusual semantic relationships. Neuropsychology, 14(3), 361369.Google Scholar
Seger, C. A., Rabin, L. A., Desmond, J. E. & Gabrieli, J. D. (1999). Verb generation priming involves conceptual implicit memory. Brain and Cognition, 41(2), 150177. doi:10.1006/brcg.1999.1116Google Scholar
Seger, C. A., Rabin, L. A., Zarella, M. M. & Gabrieli, J. D. E. (1997). Preserved verb generation priming in global amnesia. Neuropsychologia, 35(8), 10691074. doi:10.1097/OLQ.0b013e318214bb70Google Scholar
Sheldon, S., Romero, K. & Moscovitch, M. (2013). Medial temporal lobe amnesia impairs performance on a free association task. Hippocampus, 23(5), 405412. doi:10.1002/hipo.22099Google Scholar
Sherman, S. J., Chassin, L., Presson, C., Seo, D.-C. & Macy, J. T. (2009). The intergenerational transmission of implicit and explicit attitudes toward smoking: Predicting adolescent smoking initiation. Journal of Experimental Social Psychology, 45(2), 313319. doi:10.1016/j.jesp.2008.09.012Google Scholar
Shiffrin, R. M. & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological Review, 84(2), 127190. doi:10.1037/0033-295X.84.2.127Google Scholar
Shimamura, A. P. & Squire, L. R. (1984). Paired-associate learning and priming effects in amnesia: A neuropsychological study. Journal of Experimental Psychology: General, 113(4), 556570. doi:10.1037/0096-3445.113.4.556Google Scholar
Shono, Y., Ames, S. L. & Stacy, A. W. (2016). Evaluation of internal validity using modern test theory: Application to word association. Psychological Assessment, 28(2), 194204. doi:10.1037/pas0000175Google Scholar
Shono, Y., Edwards, M. C., Ames, S. L. & Stacy, A. W. (2018). Trajectories of cannabis-related associative memory among vulnerable adolescents: Psychometric and longitudinal evaluations. Developmental Psychology, 54(6), 11481158. doi:10.1037/dev0000510Google Scholar
Spruyt, A., Lemaigre, V., Salhi, B., et al. (2015). Implicit attitudes towards smoking predict long-term relapse in abstinent smokers. Psychopharmacology, 232(14), 25512561. doi:10.1007/s00213-015-3893-2Google Scholar
Squire, L. R. (2009). Memory and brain systems: 1969–2009. The Journal of Neuroscience, 29(41), 1271112716. doi:10.1523/JNEUROSCI.3575-09.2009Google Scholar
Squire, L. R. & Knowlton, B. J. (1995). Memory, hippocampus, & brain systems. In Gazzaniga, M. (Ed.), The Cognitive Neurosciences. Cambridge, MA: MIT Press, pp. 825837.Google Scholar
Squire, L. R., Knowlton, B. & Musen, G. (1993). The structure and organization of memory. Annual Review of Psychology, 44, 453495.Google Scholar
Stacy, A. W., Ames, S. L. & Knowlton, B. J. (2004). Neurologically plausible distinctions in cognition relevant to drug use etiology and prevention. Substance Use & Misuse, 39(10–12), 15711623.Google Scholar
Stacy, A. W., Bentler, P. M. & Flay, B. R. (1994). Attitudes and health behavior in diverse populations: Drunk driving, alcohol use, binge eating, marijuana use, and cigarette use. Health Psychology, 13(1), 7385. doi:10.1037/0278-6133.13.1.73Google Scholar
Stacy, A. W., Nydegger, L. A. & Shono, Y. (2019). Translation of basic research in cognitive science to HIV risk: A randomized controlled trial. Journal of Behavioral Medicine, 42, 440451.Google Scholar
Stacy, A. W., Stein, J. A. & Longshore, D. (1999). Habit, intention, and drug use as interactive predictors of condom use among drug abusers. AIDS and Behavior, 3(3), 231241.Google Scholar
Sussman, S., Ames, S. L., Dent, C. W. & Stacy, A. W. (2001). Self-reported high-risk locations of drug use among drug offenders. American Journal of Drug and Alcohol Abuse, 27(2), 281299.Google Scholar
Szalay, L. B., Strohl, J. B. & Doherty, K. T. (1999). Psychoenvironmental Forces in Substance Abuse Prevention. Dordrecht, the Netherlands: Kluwer Academic Publishers.Google Scholar
Thompson-Schill, S. L., D'Esposito, M. & Kan, I. P. (1999). Effects of repetition and competition on activity of left prefrontal cortex during word generation. Neuron, 23(3), 513522.Google Scholar
Thush, C. & Wiers, R. W. (2007). Explicit and implicit alcohol-related cognitions and the prediction of future drinking in adolescents. Addictive Behaviors, 32(7), 13671383. doi:S0306-4603(06)00299-1 [pii] 10.1016/j.addbeh.2006.09.011Google Scholar
Thush, C., Wiers, R. W., Ames, S. L., et al. (2007). Apples and oranges? Comparing indirect measures of alcohol-related cognition predicting alcohol use in at-risk adolescents. Psychology of Addictive Behaviors, 21(4), 587591.Google Scholar
Thush, C., Wiers, R. W., Ames, S. L., et al. (2008). Interactions between implicit and explicit cognition and working memory capacity in the prediction of alcohol use in at-risk adolescents. Drug and Alcohol Dependence, 94(1–3), 116124. doi:S0376-8716(07)00432-2 [pii] 10.1016/j.drugalcdep.2007.10.019Google Scholar
Tversky, A. & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207232. doi:10.1016/0010-0285(73)90033-9Google Scholar
Underwood, B. J. (1965). False recognition produced by implicit verbal responses. Journal of Experimental Psychology, 70(1), 122129.Google Scholar
Underwood, B. J., Reichardt, C. S. & Malmi, R. A. (1975). Sources of facilitation in learning conceptually structured paired-associate lists. Journal of Experimental Psychology: Human Learning and Memory, 1(2), 160166. doi:10.1037/0278-7393.1.2.160Google Scholar
Vaidya, C. J., Gabrieli, J. D.E., Keane, M. M. & Monti, L. A. (1995). Perceptual and conceptual memory processes in global amnesia. Neuropsychology, 9(4), 580591.Google Scholar
Vallet, G. T., Hudon, C., Bier, N., et al. (2017). A semantic and episodic memory test (SEMEP) developed within the embodied cognition framework: Application to normal aging, Alzheimer’s disease and semantic dementia. Frontiers in Psychology, 8. doi:10.3389/fpsyg.2017.01493Google Scholar
Van Der Vorst, H., Krank, M. D., Engels, R. C. M. E., et al. (2013). The mediating role of alcohol-related memory associations on the relation between perceived parental drinking and the onset of adolescents’ alcohol use. Addiction, 108(3), 526533. doi:10.1111/add.12042Google Scholar
Verplanken, B. & Orbell, S. (2003). Reflections on past behavior: A self-report index of habit strength. Journal of Applied Social Psychology, 33(6), 13131330. doi:10.1007/s12160-011-9305-xGoogle Scholar
Volkow, N. D., Koob, G. F. & McLellan, A. T. (2016). Neurobiologic advances from the brain disease model of addiction. The New England Journal of Medicine, 374(4), 363-371. doi:10.1056/NEJMra1511480Google Scholar
Wang, J., Johnson, L. A., Jensen, A. L., et al. (2017). Network-wide oscillations in the parkinsonian state: Alterations in neuronal activities occur in the premotor cortex in parkinsonian nonhuman primates. Journal of Neurophysiology, 117(6), 22422249. doi:10.1152/jn.00011.2017Google Scholar
Wei, G. & Luo, J. (2010). Sport expert's motor imagery: Functional imaging of professional motor skills and simple motor skills. Brain Research, 1341, 5262. doi:10.1016/j.brainres.2009.08.014Google Scholar
Weingardt, K. R., Stacy, A. W. & Leigh, B. C. (1996). Automatic activation of alcohol concepts in response to positive outcomes of alcohol use. Alcoholism: Clinical and Experimental Research, 20(1), 2530.Google Scholar
White, N. M. & McDonald, R. J. (2002). Multiple parallel memory systems in the brain of the rat. Neurobiology of Learning and Memory, 77(2), 125184. doi:10.1006/nlme.2001.4008Google Scholar
White, N. M., Packard, M. G. & McDonald, R. J. (2013). Dissociation of memory systems: The story unfolds. Behavioral Neuroscience, 127(6), 813834. doi:10.1037/a0034859Google Scholar
Wiers, R. W., Rinck, M., Dictus, M. & van den Wildenberg, E. (2009). Relatively strong automatic appetitive action-tendencies in male carriers of the OPRM1 G-allele. Genes, Brain & Behavior, 8(1), 101106. doi:10.1111/j.1601-183X.2008.00454.xGoogle Scholar
Wiers, R. W. & Stacy, A. W. (2006). Implicit cognition and addiction. Current Directions in Psychological Science, 15(6), 292296.Google Scholar
Wiers, R. W., van de Luitgaarden, J., van den Wildenberg, E. & Smulders, F. T. Y. (2005). Challenging implicit and explicit alcohol-related cognitions in young heavy drinkers. Addiction, 100(6), 806819. doi:10.1111/j.1360-0443.2005.01064.xGoogle Scholar
Wing, E. A., Iyengar, V., Hess, T. M., et al. (2018). Neural mechanisms underlying subsequent memory for personal beliefs: An fMRI study. Cognitive, Affective & Behavioral Neuroscience, 18(2), 216231. doi:10.3758/s13415-018-0563-yGoogle Scholar
Wood, W. & Neal, D. T. (2007). A new look at habits and the habit-goal interface. Psychological Review, 114(4), 843863. doi:2007-13558-001 [pii] 10.1037/0033-295X.114.4.843Google Scholar
Wriessnegger, S. C., Steyrl, D., Koschutnig, K. & Müller-Putz, G. R. (2016). Cooperation in mind: Motor imagery of joint and single actions is represented in different brain areas. Brain and Cognition, 109, 1925. doi:10.1016/j.bandc.2016.08.008Google Scholar
Yalachkov, Y., Kaiser, J. & Naumer, M.J. (2015). The role of sensory and motor brain regions in drug-cue reactivity. In Wilson, S. J. (Ed.), The Wiley Handbook on the Cognitive Neuroscience of Addiction. Wiley-Blackwell, pp. 175194.Google Scholar
Yan, W., Li, Y., Xiao, L., et al. (2014). Working memory and affective decision- making in addiction: A neurocognitive comparison between heroin addicts, pathological gamblers and healthy controls. Drug and Alcohol Dependence, 134, 194200. doi:10.1016/j.drugalcdep.2013.09.027Google Scholar
Yin, H. H. & Knowlton, B. J. (2006a). Addiction and learning in the brain. In Wiers, R. W. & Stacy, A. W. (Eds.), Handbook of Implicit Cognition and Addiction. Thousand Oaks, CA: SAGE Publications, pp. 167184.Google Scholar
Yin, H. H. & Knowlton, B. J. (2006b). The role of the basal ganglia in habit formation. Nature Reviews Neuroscience, 7(6), 464476. doi:10.1038/nrn1919CrossRefGoogle ScholarPubMed
Young, K. A., Franklin, T. R., Roberts, D. C. S., et al. (2014). Nipping cue reactivity in the bud: Baclofen prevents limbic activation elicited by subliminal drug cues. The Journal of Neuroscience, 34(14), 50385043. doi:10.1523/JNEUROSCI.4977-13.2014Google Scholar
Zabicki, A., de Haas, B., Zentgraf, K., et al. (2017). Imagined and executed actions in the human motor system: Testing neural similarity between execution and imagery of actions with a multivariate approach. Cerebral Cortex, 27(9), 45234536. doi:10.1093/cercor/bhw257Google Scholar
Zack, M., Poulos, C. X. & Woodford, T. M. (2006). Diazepam dose-dependently increases or decreases implicit priming of alcohol associations in problem drinkers. Alcohol and Alcoholism, 41(6), 604610. doi:10.1093/alcalc/agl076CrossRefGoogle ScholarPubMed
Ziaee, S. S., Fadardi, J. S., Cox, W. M. & Yazdi, S. A. A. (2016). Effects of attention control training on drug abusers’ attentional bias and treatment outcome. Journal of Consulting and Clinical Psychology, 84(10), 861873. doi:10.1037/a0040290Google Scholar

References

Alegria, M., Mulvaney-Day, N., Torres, M., et al. (2007). Prevalence of psychiatric disorders across Latino subgroups in the United States. American Journal of Public Health, 97, 6875.Google Scholar
Alesina, A., Glaeser, E. & Sacerdote, B. (2005). Work and leisure in the US and Europe: Why so different? NBER Maroeconomic Annual, 20, 164.Google Scholar
Altonji, J. & Oldham, J. (2003). Vacation laws annual work hours. Economic Perspectives: Federal Reserve Bank of Chicago, Fall, 19–29.Google Scholar
American Cancer Society (2017). Cancer Facts & Figures 2017.Google Scholar
American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders (5th edition). Arlington, VA: American Psychiatric Publishing.Google Scholar
Aziz, S., Adkins, C. T., Walker, A. G. & Wuensch, K. L. (2010). Wokaholism and work-life imbalance: Does cultural origin influence the relationship? International Journal of Psychology, 45(1), 7279.CrossRefGoogle ScholarPubMed
Babbar, J. (2007). Correspondence: Compulsive buying – A culture-bound disorder? International Journal of Social Psychiatry, 53(2), 189190.Google Scholar
Bancroft, J. & Vukadinovic, Z. (2004). Sexual addiction, sexual compulsivity, sexual impulsivity, or what? Toward a theoretical model. The Journal of Sex Research, 41(3), 225234.Google Scholar
Barnes, G. M., Welte, J. W. & Tidwell, M. O. (2017). Gambling involvement among Native Americans, Blacks, and whites in the United States. The American Journal on Addictions, 26, 713721.Google Scholar
Baruch, Y. (2011). The positive wellbeing aspects of workaholism in cross cultural perspective: The chocoholism metaphor. Career Development International, 16(6), 572591.Google Scholar
Bax, T. (2016). “Internet gaming disorder” in China: Biomedical sickness or sociological badness? Games and Culture, 11(3), 233255.CrossRefGoogle Scholar
Blachnio, A., Przepiórka, A., Senol-Durak, E., Durak, M. & Sherstyuk, L. (2016). The role of self-esteem in Internet addiction: A comparison between Turkish, Polish and Ukrainian samples. European Journal of Psychiatry, 30(2), 149155.Google Scholar
Blachnio, A., Przepiórka, A., Senol-Durak, E., Durak, M. & Sherstyuk, L. (2017). The role of personality traits in Facebook and Internet addictions: A study on Polish, Turkish and Ukrainian samples. Computers in Human Behavior, 68, 269275.CrossRefGoogle Scholar
Black, D. W. (2007). A review of compulsive buying disorder. World Psychiatry, 6, 14-18.Google Scholar
Block, J. J. (2008). Issues for DSM-V: Internet addiction. American Journal of Psychiatry, 165(3), 306307.Google Scholar
Brenner, G. A., Lipeb, M. & Servet, J. (1996). Gambling in Cameroon and Senegal: A response to crisis? In McMillen, J. (Ed.), Gambling Cultures: Studies in History and Interpretation. New York: Routledge, Taylor & Francis Group.Google Scholar
Brody, D. (1989). Time and work during early American industrialism. Labor History, 30(1), 546.Google Scholar
Burke, R. J. (1999). Workaholism in organizations: Gender differences. Sex Roles, 41(5/6), 335345.Google Scholar
Burke, R. J., Koyuncu, M. & Fiksenbaum, L. (2008). Workaholism, work and extra-work satisfactions and psychological well-being among professors in Turkey. Cross-cultural Management: An International Journal, 15(4), 353366.Google Scholar
Caetano, R., Clark, C. L. & Tam, T. (1998). Alcohol consumption among racial/ethnic minorities: Theory and research. Alcohol Health and Research World, 22(4), 233241.Google Scholar
Center for Behavioral Health Statistics and Quality (2017). Results from the 2016 National Survey on Drug Use and Health: Detailed Tables. Rockville, MD: Substance Abuse and Mental Health Services Administration.Google Scholar
Chakraborty, K., Basu, D. & Kumar, V. (2010). Internet addiction: Consensus, controversies and the way ahead. East Asian Archives of Psychiatry, 20, 123132.Google Scholar
Coleman, E. (2003). Compulsive sexual behavior: What to call it, how to treat it? SIECUS Report, 31(5), 1216.Google Scholar
Cryle, P. (2009a). Interrogating the work of Thomas W. Laqueur. Sexualities, 12(4), 411417.Google Scholar
Cryle, P. (2009b). Les Choses et les Mots: Missing words and blurry things in the history of sexuality. Sexualities, 12(4), 437450.CrossRefGoogle Scholar
Dittmar, H. (2004). Understanding and diagnosing compulsive buying. In Coombs, R. H. (Ed.), Handbook of Addictive Disorders. Wiley, pp. 411450.Google Scholar
Drescher, J. (2015). Queer diagnoses revisited: The past and future of homosexuality and gender diagnoses in DSM and ICD. International Review of Psychiatry, 27(5), 386395.Google Scholar
Eckersley, R. M. (2005). “Cultural fraud”: The role of culture in drug abuse. Drug and Alcohol Review, 24, 157163.Google Scholar
Faber, R. J. & O’Guinn, T. C. (1989). Classifying compulsive consumers: Advances in the development of a diagnostic tool. Advances in Consumer Research, 16, 738744.Google Scholar
Faber, R .J., O’Guinn, T. C. & Krych, R. (1987). Compulsive consumption. Advances in Consumer Research, 14, 132135.Google Scholar
Finkenauer, R., Pomerleau, C. S., Snedecor, S. M. & Pomerleau, O. F. (2009). Race differences in factors relating to smoking initiation. Addictive Behaviors, 34, 10561059.Google Scholar
Flint, A.J., Gearhardt, A. N., Corbin, W. R., et al. (2014). Food-addiction scale measurement in 2 cohorts of middle-aged and older women. American Journal of Clinical Nutrition, 99, 578586.Google Scholar
Gardiner, P. S. (2004). The African Americanization of menthol cigarette use in the United States. Nicotine and Tobacco Research, 6(S1), S55–65.Google Scholar
Golden, L. (2009). A brief history of long work time and the contemporary sources of overwork. Journal of Business Ethics, 84, 217227.Google Scholar
Goodman, A. (2001). What’s in a name? Terminology for designating a syndrome of driven sexual behavior. Sexual Addiction & Compulsivity, 8, 191213.Google Scholar
Goodwin, H., Haycraft, E. & Meyer, C. (2011). Sociocultural correlates of compulsive exercise: Is the environment important in fostering a compulsivity towards exercise among adolescents? Body Image, 8, 390395.Google Scholar
Griffiths, M. (1996). Gambling on the Internet: A brief note. Journal of Gambling Studies, 12(4), 471473.Google Scholar
Guidi, J., Pender, M., Hollon, S. D., et al. (2009). The prevalence of compulsive eating and exercise among college students: An exploratory study. Psychiatry Research, 165, 154162.Google Scholar
Ha, J. H., Yoo, H. J., Cho, I. H., et al. (2006). Psychiatric comorbidity assessed in Korean children and adolescents who screen positive for Internet addiction. Journal of Clinical Psychiatry, 67, 821826.Google Scholar
Halkitis, P. N., Parsons, J. T. & Stirratt, M. J. (2001). A double epidemic: Crystal methamphetamine drug use in relation to HIV transmission among gay men. Journal of Homosexuality, 41(2), 1735.Google Scholar
Homer, (1919). The Odyssey with an English Translation by Murray, A. T., in two volumes. Cambridge, MA:, Harvard University Press; London: William Heinemann, Ltd.Google Scholar
Homan, K. (2010). Athletic-ideal and thin-ideal internalization as prospective predictors of body dissatisfaction, dieting, and compulsive exercise. Body Image, 7, 240245.Google Scholar
Hunnicutt, B. (1988). Work Without End: Abandoning Shorter Hours for the Right to Work. Philadelphia: Temple University Press.Google Scholar
Irvine, J. M. (1995). Reinventing perversion: Sex addiction and cultural anxieties. Journal of the History of Sexuality, 5(3), 429450.Google Scholar
Jamal, A., King, B. A., Neff, L. J., et al. (2016). Current cigarette smoking among adults – United States, 2005–2015. Morbidity and Mortality Weekly Report, 65(44), 12051211.Google Scholar
Kang, J. H., Matusik, J. G. & Barclay, L. A. (2017). Affective and normative motives to work overtime in Asian organizations: Four cultural orientations from Confucian ethics. Journal of Business Ethics, 140, 115130.Google Scholar
Kelly, N. R., Cotter, E. W., Tanofsky-Kraff, M. & Mazzeo, S. E. (2015). Racial variations in binge eating, body image concerns, and compulsive exercise among men. Psychology of Men & Masculinity, 16(3), 326336.Google Scholar
King, D. L., Delfabbro, P. H., Doh, Y. Y., et al. (2017). Policy and prevention approaches for disordered and hazardous gaming and Internet use: An international perspective. Prevention Science, 19(2), 233249.Google Scholar
Koran, L. M., Faber, R. J., Aboujaoude, E., Large, M. D. & Serpe, R. T. (2006). Estimated prevalence of compulsive buying behavior in the United States. American Journal of Psychiatry, 163, 18061812.Google Scholar
Kwak, H., Zinkhan, G. M. & Roushanzamir, E. P. (2004). Compulsive comorbidity and its psychological antecedents: A cross-cultural comparison between the US and South Korea. The Journal of Consumer Marketing, 21(6), 418434.Google Scholar
Laqueur, T. W. (2009). Sexuality and the transformation of culture: The Longue Durée. Sexualities, 12(4), 418436.CrossRefGoogle Scholar
Latner, J. D., Puhl, R. M., Murakami, J. M. & O’Brien, K. S. (2014). Food addiction as a causal model of obesity. Effects on stigma, blame, and perceived psychopathology. Appetite, 77C, 7782.Google Scholar
Li, N. & Kirkup, G. (2007). Gender and cultural differences in Internet use: A study of China and the UK. Computers & Education, 48, 301317.Google Scholar
Lv, W., Wu, Q., Liu, X., et al. (2016). Cue reactivity in nicotine and alcohol addiction: A cross-cultural view. Frontiers in Psychology, 7, e1e7.Google Scholar
Luczak, S. E.Khoddam, R., Yu, S.et al. (2017). Prevalence and co-occurrence of addictions in U.S. ethnic/racial groups. Implications for genetic research. The American Journal on Addictions, 26, 424436.Google Scholar
Maraz, A., Griffiths, M. D. & Demetrovics, Z. (2016). The prevalence of compulsive buying: A meta-analysis. Addiction, 111, 408419.Google Scholar
Martinez, M. J., Huang, S., Estrada, Y., Sutton, M. Y. & Prado, G. (2016). The relationship between acculturation, ecodevelopment, and substance use among Hispanic adolescents. Journal of Early Adolescence, 37(7), 948974.Google Scholar
Maxwell, A. E., Crespi, C. M., Alano, R. E., Sudan, M. & Bastani, R. (2012). Health risk behaviors among five Asian American subgroups in California: Identifying intervention priorities. Journal of Immigrant and Minority Health, 14, 890894.Google Scholar
McCrory, E. J. & Mayes, L. (2015). Understanding addiction as a developmental disorder: An argument for a developmentally informed multilevel approach. Current Addiction Reports, 2, 326330.Google Scholar
Medeiros, G. C., Leppink, E. W., Redden, S. A., et al. (2016). A cross-cultural study of gambling disorder: A comparison between women from Brazil and the United States. Revista Brasileira de Psiquiatria, 38, 5357.Google Scholar
Medeiros, G. C., Leppink, E. W., Yaemi, A., et al. (2015a). Electronic gaming machines and gambling disorder: A cross-cultural comparison between treatment-seeking subjects from Brazil and the United States. Psychiatry Research, 230(2), 430435.Google Scholar
Medeiros, G. C., Leppink, E., Yaemi, A., et al. (2015b). Gambling disorder in older adults: A cross-cultural perspective. Comprehensive Psychiatry, 58, 116121.Google Scholar
Meyer, C., Taranis, L., Goodwin, H. & Haycraft, E. (2011). European Eating Disorders Review, 19, 174189.Google Scholar
Ministry of Health (2009). A Focus on Problem Gambling: Results of the 2006/07 New Zealand Health Survey. Wellington: Ministry of Health.Google Scholar
Naydanova, E. & Beal, B. D. (2016). Harmonious and obsessive Internet passion, competence, and self-worth: A study of high school students in the United States and Russia. Computers in Human Behavior, 64, 8893.Google Scholar
Oates, W. (1971). Confessions of a Workaholic: The Facts about Work Addiction. New York: World.Google Scholar
Parhami, I., Siani, A., Campos, M.D., et al. & UCLA Gambling Studies Program (2012). Gambling in the Iranian-American community and an assessment of motives: A case study. International Journal of Mental Health and Addiction, 10, 710721.Google Scholar
Piquet-Pessôa, M., Ferreira, G. M., Melca, I. A. & Fontenelle, L. F. (2014). DSM-5 and the decision not to include sex, shopping or stealing as addictions. Current Addiction Reports, 1, 172176.Google Scholar
Quinones, C. & Kakabadse, N. K. (2015). Self-concept clarity, social support, and compulsive Internet use: A study of the US and the UAE. Computers in Human Behavior, 44, 347356.Google Scholar
Rainey, J. C., Furman, C. R. & Gearhardt, A. N. (2018). Food addiction among sexual minorities. Appetite, 120, 1622.Google Scholar
Raylu, N. & Oei, T. P. (2004). Role of culture in gambling and problem gambling. Clinical Psychology Review, 23, 10871114.Google Scholar
Reay, B., Attwood, N. & Gooder, C. (2013). Inventing sex: The short history of sex addiction. Sexuality and Culture, 17, 119.Google Scholar
Reid, R. C. & Carpenter, B. N. (2009). Exploring relationships of psychopathology in hypersexual patients using the MMPI-2. Journal of Sex & Marital Therapy, 35, 294310.Google Scholar
Reynaud, M., Karila, L., Blecha, L. & Benyamina, A. (2010). Is love passion an addictive disorder? The American Journal of Drug and Alcohol Abuse, 36, 261267.Google Scholar
Ricciardelli, L. A., McCabe, M. P., Williams, R. J. & Thompson, J. K. (2007). The role of ethnicity and culture in body image and disordered eating among males. Clinical Psychology Review, 27, 582606.Google Scholar
Romeo, M., Yepes-Baldó, M., Berger, R. & Da Costa, F. F. (2014). Workaholism in Brazil: Measurement and individual differences. Addiccionese, 26(4), 312320.CrossRefGoogle ScholarPubMed
Russo, P., Nastrucci, C., Alzetta, G. & Szalai, C. (2011). Tobacco habit: Historical, cultural, neurobiological, and genetic features of people’s relationship with an addictive drug. Perspectives in Biology and Medicine, 54(4), 557577.Google Scholar
Sanlier, N., Varli, S. N., Macit, M. S., Mortas, H. & Tatar, T. (2017). Evaluation of disordered eating tendencies in young adults. Eating and Weight Disorders, 22, 623631.Google Scholar
Schulte, E. M., Tuttle, H. M. & Gearhardt, A. N. (2016). Belief in food addiction and obesity-related policy support. PLoS ONE, 11(1), e0147557.Google Scholar
Seok, S. & DaCosta, B. (2012). The world’s most intense online gaming culture: Addiction and high-engagement prevalence rates among South Korean adolescents and young adults. Computers in Human Behavior, 22(6), 21433151.Google Scholar
Shrem, M. T. & Halkitis, P. N. (2008). Methamphetaine abuse in the United States: Contextual, psychological and sociological considerations. Journal of Health Psychology, 13(5), 669679.Google Scholar
Skegg, K., Nada-Raja, S., Dickson, N. & Paul, C. (2010). Perceived “out of control” sexual behavior in a cohort of young adults from the Dunedin Multidisciplinary Health and Development Study. Archives of Sex Behavior, 39, 968978.Google Scholar
Spiegel, A. (2002, January 18). 81 words. This American Life. Radio episode retrieved from www.thisamericanlife.org/204/81-wordsGoogle Scholar
Stankov, L. (2010). Unforgiving Confucian culture: A breeding ground for high academic achievement, test anxiety and self-doubt? Learning and Individual Differences, 20, 555563.Google Scholar
Stenstrom, E. & Saad, G. (2011). Testosterone, financial risk-taking, and pathological gambling. Journal of Neuroscience, Psychology, and Economics, 4(4), 254266.Google Scholar
Stice, E. & Agras, W. S. (1998). Predicting onset and cessation of bulimic behaviors during adolescence: A longitudinal grouping analysis. Behavior Analysis, 29, 257276.Google Scholar
Sussman, S. & Sussman, A. N. (2011). Considering the definition of addiction. International Journal of Environmental Research and Public Health, 8, 40254038.Google Scholar
Tao, R., Huang, X., Wang, J., et al. (2010). Proposed diagnostic criteria for internet addiction. Addiction, 105, 556564.Google Scholar
Thompson, J. K., van den Berg, P., Roehrig, M., Guarda, A. S. & Heinberg, L. J. (2004). The sociocultural attitudes towards appearance scale-3 (SATAQ-3): Development and validation. International Journal of Eating Disorders, 35, 293304.Google Scholar
Ueberfeldt, A. (2006). Working time over the 20th century. Bank of Canada Working Paper, 2006–2018.Google Scholar
United States, Department of Interior, Census Office (1883). Report on the Statistics of Wages in Manufacturing Industries, by Weeks, Joseph, 1880 Census, Vol. 20. Washington: GPO.Google Scholar
US Mortality Volumes 1930 to 1959 and US Mortality Data 1960 to 2014, National Center for Health Statistics, Centers for Disease Control and Prevention. (OR Cancer Facts and Figures 2017, American Cancer Society)Google Scholar
Wall, K. M., Stephenson, R. & Sullivan, T. S. (2013). Frequency of sexual activity of most recent male partner among young, Internet-using men who have sex with men in the United States. Journal of Homosexuality, 60(10), 15201538.Google Scholar
Welch, A. (2017). The problem with Harvey Weinstein’s sex addiction claim. CBS News, November 6, 2017. Retrieved January 17, 2018 from www.cbsnews.com/news/sex-addiction-claims-harvey-weinstein-kevin-spacey/.Google Scholar
Whaples, R. (2001). Hours of work in US history. EH.Net Encyclopedia, April 14, 2001. Retrieved June 6, 2018 from http://eh.net/encyclopedia/hours-of-work-in-u-s-history/.Google Scholar
Wilkerson, M. (2015). America’s five most socially acceptable addictions. Substance.com, January 14, 2015. Retrieved February 1, 2018 from www.substance.com/americas-five-most-socially-acceptable-addictions/18305/.Google Scholar
Yildiz, M., Bingöl, E., Şahan, H., Bayköse, N. & Şenel, E. (2017). A cross-cultural approach to sport psychology: Is exercise addiction a determinant of life quality? Sport Journal, 1, 110.Google Scholar
Young, K. S. (1996). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 1(3), 237244.Google Scholar
Young, K. S. (2017). The evolution of Internet addiction. Addictive Behaviors, 64, 229230.Google Scholar
Ziauddeen, H. & Fletcher, P. C. (2013). Is food addiction a valid and useful concept? Obesity Reviews, 14, 1928.Google Scholar

References

Adler, N. E., Boyce, W. T., Chesney, M. A., Folkman, S. & Syme, S. L. (1993). Socioeconomic inequalities in health: No easy solution. Journal of the American Medical Association, 269(24), 31403145. https://doi.org/10.1001/jama.193.03500240084031Google Scholar
American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders (5th edition). Washington, DC: American Psychiatric Association.Google Scholar
American Public Health Association [APHA] (2016). Opportunities for health collaboration: Leveraging community development investments to improve health in low-income neighborhoods. Retrieved October 17, 2017, from www.apha.org/policies-and-advocacy/public-health-policy-statements/policy-database/2017/01/17/opportunities-for-health-collaborationGoogle Scholar
Andreassen, C. S., Griffiths, M. D., Sinha, R., Hetland, J. & Pallesen, S. (2016). The relationships between workaholism and symptoms of psychiatric disorders: A large-scale cross-sectional study. PLoS ONE, 11(5), e0152978. https://doi.org/10.1371/journal.pone.0152978Google Scholar
Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Bauman, L. J., Silver, E. J. & Stein, R. E. K. (2006). Cumulative social disadvantage and child health. Pediatrics, 117(4), 13211328. https://doi.org/10.1542/ped.2005-1647Google Scholar
Beech, B. M., Fitzgibbon, M. L., Resnicow, K. & Whitt-Glover, M. C. (2011). The impact of socioeconomic factors and the built environment on childhood and adolescent obesity. Childhood Obesity, 7(1), 1924. https://doi.org/10.1089/chi.2011.0106Google Scholar
Bernstein, K. T., Galea, S., Ahern, J., Tracy, M. & Vlahov, D. (2007). The built environment and alcohol consumption in urban neighborhoods. Drug and Alcohol Dependence, 91(2–3), 244252. https://doi.org/10.1016/j.drugalcdep.2007.06.006Google Scholar
Blacksher, E. & Lovasi, G. S. (2012). Place-focused physical activity research, human agency, and social justice in public health: Taking agency seriously in studies of the built environment. Health & Place, 18(2), 172179. https://doi.org/10.1016/j.healthplace.2011.08.019Google Scholar
Braveman, P. A. & Egerter, S. (2013). Overcoming obstacles to health in 2013 and beyond. Robert Wood Johnson Foundation Commission to Build a Healthier America. Retrieved from https://www.rwjf.org/en/library/research/2013/06/overcoming-obstacles-to-health-in-2013-and-beyond.htmlGoogle Scholar
Braveman, P. A., Egerter, S. & Williams, D. R. (2011). The social determinants of health: Coming of age. Annual Review of Public Health, 32(1), 381398. https://doi.org/10.1146/annurev-publhealth-031210-101218Google Scholar
Bronfenbrenner, U. (1977). Toward an experimental ecology of human development. American Psychologist, 32(7), 513531. https://doi.org/10.1037/0003-066X.32.7.513Google Scholar
Brooks, B., McBee, M., Pack, R. & Alamian, A. (2017). The effects of rurality on substance use disorder diagnosis: A multiple-groups latent class analysis. Addictive Behaviors, 68, 2429. https://doi.org/10.1016/j.addbeh.2017.01.019Google Scholar
Brown, G. W. & Harris, T. O. (1978). Social Origins of Depression: A Study of Psychiatric Disorder in Women. New York: Free Press.Google Scholar
Burns, P. A. & Snow, R. C. (2012). The built environment & the impact of neighborhood characteristics on youth sexual risk behavior in Cape Town, South Africa. Health & Place, 18(5), 10881100. https://doi.org/10.1016/j.healthplace.2012.04.013Google Scholar
Burrows, T., Skinner, J., McKenna, R. & Rollo, M. (2017). Food addiction, binge eating disorder, and obesity: Is there a relationship? Behavioral Sciences, 7(3), bs7030054. https://doi.org/10.3390/bs7030054Google Scholar
Carroll-Scott, A., Gilstad-Hayden, K., Rosenthal, L., et al. (2013). Disentangling neighborhood contextual associations with child body mass index, diet, and physical activity: The role of built, socioeconomic, and social environments. Social Science & Medicine, 95, 106114. https://doi.org/10.1016/j.socscimed.2013.04.003Google Scholar
Casazza, K., Brown, A., Astrup, A., et al. (2015). Weighing the evidence of common beliefs in obesity research. Critical Reviews in Food Science and Nutrition, 55(14), 20142053. https://doi.org/10.1080/10408398.2014.922044Google Scholar
Centers for Disease Control and Prevention [CDC] (2007). Cigarette smoking among adults – United States, 2006. Morbidity and Mortality Weekly Report, 56(44), 11571161. Retrieved from http://www.jstor.org/stable/23318296Google Scholar
Centers for Disease Control and Prevention [CDC] (2009). Recommended Community Strategies and Measurements to Prevent Obesity in the United States: Implementation and Measurement Guide (Vol. MMWR 2009). Atlanta, GA: US Dept of Health & Human Services.Google Scholar
Cerdá, M., Ransome, Y., Keyes, K. M., et al. (2013). Revisiting the role of the urban environment in substance use: The case of analgesic overdose fatalities. American Journal of Public Health, 103(12), 22522260. https://doi.org/10.2105/AJPH.2012.301347Google Scholar
Chaix, B., Billaudeau, N., Thomas, F., et al. (2011). Neighborhood effects on health: Correcting bias from neighborhood effects on participation. Epidemiology, 22(1), 1826. https://doi.org/10.1097/EDE.0b013e3181fd2961Google Scholar
Chaloupka, F. J. & Wechsler, H. (1996). Binge drinking in college: The impact of price, availability, and alcohol control policies. Contemporary Economic Policy, 14(4), 112124. https://doi.org/10.1111/j.1465-7287.1996.tb00638.xGoogle Scholar
Cohen, D. A., Inagami, S. & Finch, B. (2008). The built environment and collective efficacy. Health & Place, 14(2), 198208. https://doi.org/10.1016/j.healthplace.2007.06.001Google Scholar
Coleman, J. S. (2000). Social capital in the creation of human capital. In Lesser, E. L. (Ed.), Knowledge and Social Capital: Foundations and Applications. Boston: Butterworth-Heinemann, pp. 1741.Google Scholar
Commission on Social Determinants of Health (2008). Closing the Gap in a Generation: Health Equity Through Action on the Social Determinants of Health. Final Report of the Commission on Social Determinants of Health. Retrieved from World Health Organization: www.who.int/social_determinants/thecommission/finalreport/en/Google Scholar
Cook, B., Hausenblas, H. & Freimuth, M. (2014). Exercise addiction and compulsive exercising: Relationship to eating disorders, substance use disorders, and addictive disorders. In Brewerton, T. & Baker, D. A. (Eds.), Eating Disorders, Addictions and Substance Use Disorders. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-45378-6_7Google Scholar
Corrêa, E. N., Schmitz, B. de A. S. & Vasconcelos, F. de A. G. de. (2015). Aspects of the built environment associated with obesity in children and adolescents: A narrative review. Revista de Nutrição, 28(3), 327340. https://doi.org/10.1590/1415-52732015000300009Google Scholar
Cubbin, C., Hadden, W. C. & Winkleby, M. A. (2001). Neighborhood context and cardiovascular disease risk factors: The contribution of material deprivation. Ethnicity & Disease, 11(4), 687700.Google Scholar
Cummings, K. M., Fong, G. T. & Borland, R. (2009). Environmental influences on tobacco use: Evidence from societal and community influences on tobacco use and dependence. Annual Review of Clinical Psychology, 5, 433458. https://doi.org/10.1146/annurev.clinpsy.032408.153607Google Scholar
Curtis, S., Cave, B. & Coutts, A. (2002). Is urban regeneration good for health? Perceptions and theories of the health impacts of urban change. Environment & Planning C: Government & Policy, 20(4), 517534. https://doi.org/10.1068/c02rGoogle Scholar
Dalbey, M. (2008). Implementing smart growth strategies in rural America: Development patterns that support public health goals. Journal of Public Health Management and Practice, 14(3), 238243. https://doi.org/10.1097/01.PHH.0000316482.65135.e8Google Scholar
Davis, C. (2013). Compulsive overeating as an addictive behavior: Overlap between food addiction and binge eating disorder. Current Obesity Reports, 2(2), 171178. https://doi.org/10.1007/s13679–013-0049-8Google Scholar
Davis, R., Rivera, D. & Parks, L. F. (2015). Moving from Understanding to Action on Health Equity: Social Determinants of Health Frameworks and THRIVE. Retrieved from www.preventioninstitute.org/publications/moving-understanding-action-health-equity-social-determinants-health-frameworks-andGoogle Scholar
Deering, K. N., Rusch, M., Amram, O., et al. (2014). Piloting a “spatial isolation” index: The built environment and sexual and drug use risks to sex workers. International Journal of Drug Policy, 25(3), 533542. https://doi.org/10.1016/j.drugpo.2013.12.002Google Scholar
Diez-Roux, A. V. (1998). Bringing context back into epidemiology: Variables and fallacies in multilevel analysis. American Journal of Public Health, 88(2), 216222. https://doi.org/10.2105/AJPH.88.2.216Google Scholar
Diez-Roux, A. V. (2004). Estimating neighborhood health effects: The challenges of causal inference in a complex world. Social Science & Medicine, 58(10), 19531960. https://doi.org/10.1016/S0277–9536(03)00414-3Google Scholar
Draus, P., Roddy, J. & Asabigi, K. (2015). Streets, strolls and spots: Sex work, drug use and social space in Detroit. International Journal of Drug Policy, 26(5), 453460. https://doi.org/10.1016/j.drugpo.2015.01.004Google Scholar
Evans, G. W. & Kantrowitz, E. (2002). Socioeconomic status and health: The potential role of environmental risk exposure. Annual Review of Public Health, 23(1), 303331. https://doi.org/10.1146/annurev.publhealth.23.112001.112349Google Scholar
Everitt, B. J. & Robbins, T. W. (2016). Drug addiction: Updating actions to habits to compulsions ten years on. Annual Review of Psychology, 67, 2350. https://doi.org/10.1146/annurev-psych-122414-033457Google Scholar
Ewald, D. R., Strack, R. W. & Orsini, M. M. (2019). Rethinking addiction. Global Pediatric Health, 6, 116. https://doi.org/10.1177/2333794X18821943Google Scholar
Fattore, L. (2014). Sex differences in addictive disorders. Frontiers in Neuroendocrinology, 35(3), 272284. https://doi.org/10.1016/j.yfrne.2014.04.003Google Scholar
Federal Reserve System & Brookings Institution (2008). The Enduring Challenge of Concentrated Poverty in America: Case Studies from Communities Across the U.S., In Erickson, D., Reid, C., Nelson, L., O’Shaughnessy, A. & Berube, A. (Eds.). Washington, DC: Federal Reserve System. Retrieved from www.federalreserve.gov/publications.htmGoogle Scholar
Ferlander, S. (2016). The importance of different rorms of social capital for health. Acta Sociologica, 50(2), 115128. https://doi.org/10.1177/0001699307077654Google Scholar
Freudenberg, N. (2007). From lifestyle to social determinants: New directions for community health promotion research and practice. Preventing Chronic Disease, 4(3). Retrieved from www.cdc.gov/pcd/issues/2007/jul/06_0194.htmGoogle Scholar
Freudenberg, N., Franzosa, E., Chisholm, J. & Libman, K. (2015). New approaches for moving upstream: How state and local health departments can transform practice to reduce health inequalities. Health Education & Behavior, 42(1), 46S56S. https://doi.org/10.1177/1090198114568304Google Scholar
Galea, S. & Vlahov, D. (2002). Social determinants and the health of drug users: Socioeconomic status, homelessness, and incarceration. Public Health Reports, 117(Supplement 1), S135S145.Google Scholar
Galea, S., Rudenstine, S. & Vlahov, D. (2005). Drug use, misuse, and the urban environment. Drug and Alcohol Review, 24(2), 127136. https://doi.org/10.1080/09595230500102509Google Scholar
Galvez, M. P., Pearl, M. & Yen, I. H. (2010). Childhood obesity and the built environment. Current Opinion in Pediatrics, 22(2), 202207. https://doi.org/10.1097/MOP.0b013e328336eb6fGoogle Scholar
Gearhardt, A. N., Bragg, M. A., Pearl, R. L., et al. (2012). Obesity and public policy. Annual Review of Clinical Psychology, 8(1), 405430. https://doi.org/10.1146/annurev-clinpsy-032511-143129Google Scholar
Gearhardt, A. N., Corbin, W. R. & Brownell, K. D. (2009). Food addiction: An examination of the diagnostic criteria for dependence. Journal of Addiction Medicine, 3(1), 17. https://doi.org/10.1097/ADM.0b013e318193c993Google Scholar
Gearhardt, A. N., Grilo, C. M., DiLeone, R. J., Brownell, K. D. & Potenza, M. N. (2011). Can food be addictive? Public health and policy implications. Addiction, 106(7), 12081212. https://doi.org/10.1111/j.1360-0443.2010.03301.xGoogle Scholar
Getis, A. (2010). Spatial autocorrelation. In Fischer, M. M. & Getis, A. (Eds.), Handbook of Applied Spatial Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 255278. https://doi.org/10.1007/978-3-642-03647-7_14Google Scholar
Giles-Corti, B. & Donovan, R. J. (2003). Relative influences of individual, social environmental, and physical environmental correlates of walking. American Journal of Public Health, 93(9), 15831589. https://doi.org/10.2105/AJPH.93.9.1583Google Scholar
Glanz, K. & Kegler, M. C. (2008). Environments: Theory, Research and Measures of the Built Environment. Division of Cancer Control and Population Sciences, National Cancer Institute. Retrieved from https://cancercontrol.cancer.gov/brp/research/constructs/environments.htmlGoogle Scholar
Goldberg, D. S. (2012). Social justice, health inequalities and methodological individualism in US health promotion. Public Health Ethics, 5(2), 104115. https://doi.org/10.1093/phe/phs013Google Scholar
Gostin, L. O. & Martinez, R. M. (2004). The future of the public’s health: Vision, values, and strategies. Health Affairs, 23(4), 96107. https://doi.org/10.1377/hlthaff.23.4.96Google Scholar
Gottlieb, L., Sandel, M. & Adler, N. E. (2013). Collecting and applying data on social determinants of health in health care settings. JAMA Internal Medicine, 173(11), 10171020. https://doi.org/10.1001/jamainternmed.2013.560Google Scholar
Greenland, S. & Robins, J. M. (1986). Identifiability, exchangeability, and epidemiological confounding. International Journal of Epidemiology, 15(3), 413419.Google Scholar
Gruen, R. L., Pearson, S. D. & Brennan, T. A. (2004). Physician-citizens – Public roles and professional obligations. Journal of the American Medical Association, 291(1), 9498. https://doi.org/10.1001/jama.291.1.94Google Scholar
Gruenewald, P. J., Ponicki, W. R. & Holder, H. D. (1993). The relationship of outlet densities to alcohol consumption: A time series cross-sectional analysis. Alcoholism: Clinical and Experimental Research, 17(1), 3847. https://doi.org/10.1111/j.1530-0277.1993.tb00723.xGoogle Scholar
Gundersen, C., Mahatmya, D., Garasky, S. & Lohman, B. (2011). Linking psychosocial stressors and childhood obesity. Obesity Reviews, 12(5), e54e63. https://doi.org/10.1111/j.1467-789X.2010.00813.xGoogle Scholar
Handy, S. L., Boarnet, M. G., Ewing, R. & Killingsworth, R. E. (2002). How the built environment affects physical activity: Views from urban planning. American Journal of Preventive Medicine, 23(2), 6473. https://doi.org/10.1016/S0749–3797(02)00475-0Google Scholar
Hankey, S., Marshall, J. D. & Brauer, M. (2012). Health impacts of the built environment: Within-urban variability in physical inactivity, air pollution, and ischemic heart disease mortality. Environmental Health Perspectives, 120(2), 247253. https://doi.org/10.1289/ehp.1103806Google Scholar
Hansen, P. G., Skov, L. R. & Skov, K. L. (2016). Making healthy choices easier: Regulation versus nudging. Annual Review of Public Health, 37(1), 237251. https://doi.org/10.1146/annurev-publhealth-032315-021537Google Scholar
Healthy People 2020 (2017). Social determinants of health. Retrieved October 23, 2017, from www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-healthGoogle Scholar
Hebebrand, J., Albayrak, Ö., Aden, R., et al. (2014). “Eating addiction,” rather than “food addiction,” better captures addictive-like eating behavior. Neuroscience and Biobehavioral Reviews, 47, 295306. https://doi.org/10.1016/j.neubiorev.2014.08.016Google Scholar
Hembree, C., Galea, S., Ahern, J., et al. (2005). The urban built environment and overdose mortality in New York City neighborhoods. Health & Place, 11(2), 147156. https://doi.org/10.1016/j.healthplace.2004.02.005Google Scholar
Higgs, P., Leontowitsch, M., Stevenson, F. & Rees Jones, I. (2009). Not just old and sick - the “will to health” in later life. Ageing and Society, 29(5), 687707. https://doi.org/10.1017/S0144686X08008271Google Scholar
Hillier, A. (2008). Childhood overweight and the built environment: Making technology part of the solution rather than part of the problem. Annals of the American Academy of Political and Social Science, 615(1), 5682. https://doi.org/10.1177/0002716207308399Google Scholar
Hughes, K. (2007). Migrating identities: The relational constitution of drug use and addiction. Sociology of Health & Illness, 29(5), 673691. https://doi.org/10.1111/j.1467-9566.2007.01018.xGoogle Scholar
Hutch, D. J., Bouye, K. E., Skillen, E., et al. (2011). Potential strategies to eliminate built environment disparities for disadvantaged and vulnerable communities. American Journal of Public Health, 101(4), 587595. https://doi.org/10.2105/AJPH.2009.173872Google Scholar
Hyshka, E. (2013). Applying a social determinants of health perspective to early adolescent cannabis use – An overview. Drugs: Education, Prevention and Policy, 20(2), 110119. https://doi.org/10.3109/09687637.2012.752434Google Scholar
Institute of Medicine. (2000). Promoting Health: Intervention Strategies from Social and Behavioral Research. Washington, DC: National Academies Press. https://doi.org/10.17226/9939Google Scholar
Irwin, J., LaGory, M., Ritchey, F. & Fitzpatrick, K. (2008). Social assets and mental distress among the homeless: Exploring the roles of social support and other forms of social capital on depression. Social Science & Medicine, 67(12), 19351943. https://doi.org/10.1016/j.socscimed.2008.09.008Google Scholar
Jilcott Pitts, S. B., Wu, Q., Sharpe, P. A., et al. (2016). Preferred healthy food nudges, food store environments, and customer dietary practices in 2 low-income southern communities. Journal of Nutrition Education and Behavior, 48(10), 735742. https://doi.org/10.1016/j.jneb.2016.08.001Google Scholar
Just, D. R. & Gabrielyan, G. (2016). Why behavioral economics matters to global food policy. Global Food Security, 11, 2633. https://doi.org/10.1016/j.gfs.2016.05.006Google Scholar
Kawachi, I. (1999). Social capital and community effects on population and individual health. Annals of the New York Academy of Sciences, 896(1), 120130. https://doi.org/10.1111/j.1749-6632.1999.tb08110.xGoogle Scholar
Kessler, R. C. & Cleary, P. D. (1980). Social class and psychological distress. American Sociological Review, 45(3), 463478. https://doi.org/10.2307/2095178Google Scholar
Keyes, K. M., Cerdá, M., Brady, J. E., Havens, J. R. & Galea, S. (2014). Understanding the rural-urban differences in nonmedical prescription opioid use and abuse in the United States. American Journal of Public Health, 104(2), 5259. https://doi.org/10.2105/AJPH.2013.301709Google Scholar
Kim, D., Subramanian, S. V & Kawachi, I. (2006). Bonding versus bridging social capital and their associations with self rated health: A multilevel analysis of 40 US communities. Journal of Epidemiology and Community Health, 60(2), 116122. https://doi.org/10.1136/jech.2005.038281Google Scholar
Konkolÿ Thege, B., Hodgins, D. C. & Wild, T. C. (2016). Co-occurring substance-related and behavioral addiction problems: A person-centered, lay epidemiology approach. Journal of Behavioral Addictions, 5(4), 614622. https://doi.org/10.1556/2006.5.2016.079Google Scholar
Kypri, K., Bell, M. L., Hay, G. C. & Baxter, J. (2008). Alcohol outlet density and university student drinking: A national study. Addiction, 103(7), 11311138. https://doi.org/10.1111/j.1360-0443.2008.02239.xGoogle Scholar
Larson, N. I., Story, M. T. & Nelson, M. C. (2009). Neighborhood environments: Disparities in access to healthy foods in the U.S. American Journal of Preventive Medicine, 36(1), 7481. https://doi.org/10.1016/j.amepre.2008.09.025Google Scholar
LaScala, E. A., Johnson, F. W. & Gruenewald, P. J. (2001). Neighborhood characteristics of alcohol-related pedestrian injury collisions: A geostatistical analysis. Prevention Science, 2(2), 123134. https://doi.org/10.1023/A:1011547831475Google Scholar
Latkin, C. A. & Curry, A. D. (2003). Stressful neighborhoods and depression: A prospective study of the impact of neighborhood disorder. Journal of Health and Social Behavior, 44(1), 3444. https://doi.org/10.2307/1519814Google Scholar
Lee, H.-S., Lemanski, J. L. & Jun, J. W. (2008). Role of gambling media exposure in influencing trajectories among college students. Journal of Gambling Studies, 24(1), 2537. https://doi.org/10.1007/s10899–007-9078-0Google Scholar
Lerner, R. M. & Kauffman, M. B. (1985). The concept of development in contextualism. Developmental Review, 5(4), 309333. https://doi.org/10.1016/0273-2297(85)90016-4Google Scholar
Leventhal, T. & Brooks-Gunn, J. (2003). Moving to opportunity: An experimental study of neighborhood effects on mental health. American Journal of Public Health, 93(9), 15761582. https://doi.org/10.2105/AJPH.93.9.1576Google Scholar
Leyden, K. M. (2003). Social capital and the built environment: The importance of walkable neighborhoods. American Journal of Public Health, 93(9), 15461551. https://doi.org/10.2105/AJPH.93.9.1546Google Scholar
Lipperman-Kreda, S., Mair, C., Grube, J. W., et al. (2014). Density and proximity of tobacco outlets to homes and schools: Relations with youth cigarette smoking. Prevention Science, 15(5), 738744. https://doi.org/10.1007/s11121–013-0442-2Google Scholar
Livingston, M., Chikritzhs, T. & Room, R. (2007). Changing the density of alcohol outlets to reduce alcohol-related problems. Drug and Alcohol Review, 26(5), 557566. https://doi.org/10.1080/09595230701499191Google Scholar
McLeroy, K. R., Bibeau, D., Steckler, A. & Glanz, K. (1988). An ecological perspective on health promotion programs. Health Education & Behavior, 15(4), 351377. https://doi.org/10.1177/109019818801500401Google Scholar
Milam, A. J., Furr-Holden, C. D. M., Cooley-Strickland, M. C., Bradshaw, C. P. & Leaf, P. J. (2014). Risk for exposure to alcohol, tobacco, and other drugs on the route to and from school: The role of alcohol outlets. Prevention Science, 15(1), 1221. https://doi.org/10.1007/s11121–012-0350-xGoogle Scholar
Milam, A. J., Johnson, S. L., Furr-Holden, C. D. M. & Bradshaw, C. P. (2016). Alcohol outlets and substance abuse amongh high schoolers. Journal of Community Psychology, 44(7), 819. https://doi.org/10.1002/jcop.21802Google Scholar
Mitchell, C. U. & LaGory, M. (2002). Social capital and mental distress in an impoverished community. City & Community, 1(2), 199222. https://doi.org/10.1111/1540-6040.00017Google Scholar
Monnat, S. M. & Rigg, K. K. (2016). Examining rural/urban differences in prescription opioid misuse among US adolescents. Journal of Rural Health, 32(2), 204218. https://doi.org/10.1111/jrh.12141Google Scholar
Monteiro, C. A., Levy, R. B., Claro, R. M., de Castro, I. R. R. & Cannon, G. (2011). Increasing consumption of ultra-processed foods and likely impact on human health: Evidence from Brazil. Public Health Nutrition, 14(1), 513. https://doi.org/10.1017/S1368980010003241Google Scholar
Moudon, A. V., Lee, C., Cheadle, A. D., et al. (2006). Operational definitions of walkable neighborhood: Theoretical and empirical insights. Journal of Physical Activity and Health, 3(Supplement 1), S99S117. https://doi.org/10.1123/jpah.3.s1.s99Google Scholar
Mulatu, M. S. & Schooler, C. (2002). Causal connections between socio-economic status and health: Reciprocal effects and mediating mechanisms. Journal of Health and Social Behavior, 43(1), 2241. https://doi.org/10.2307/3090243Google Scholar
National Prevention Council. (2012). National Prevention Council Action Plan: Implementing the National Prevention Strategy. Washington, DC: National Prevention Council. Retrieved from http://purl.fdlp.gov/GPO/gpo50605Google Scholar
Northridge, M. E., Sclar, E. D. & Biswas, P. (2003). Sorting out the connections between the built environment and health: A conceptual framework for navigating pathways and planning healthy cities. Journal of Urban Health, 80(4), 556568. https://doi.org/10.1093/jurban/jtg064Google Scholar
Nowak, D. E. & Aloe, A. M. (2014). The prevalence of pathological gambling among college students: a meta-analytic synthesis, 2005–2013. Journal of Gambling Studies, 30(4), 819843. https://doi.org/10.1007/s10899–013-9399-0Google Scholar
Nowell, B. L., Berkowitz, S. L., Deacon, Z. & Foster-Fishman, P. (2006). Revealing the cues within community places: Stories of identity, history, and possibility. American Journal of Community Psychology, 37(1–2), 2946. https://doi.org/10.1007/s10464–005-9006-3Google Scholar
Obesity Research Task Force (2011). Strategic Plan for NIH Obesity Research. Rockville, MD: National Institutes of Health (NIH Publication No. 11-5493). Retrieved from https://obesityresearch.nih.gov/about/StrategicPlanforNIH_Obesity_Research_Full-Report_2011.pdfGoogle Scholar
Olsen, C. M. (2011). Natural rewards, neuroplasticity, and non-drug addictions. Neuropharmacology, 61(7), 11091122. https://doi.org/10.1016/j.neuropharm.2011.03.010Google Scholar
Perdue, W. C., Gostin, L. O. & Stone, L. A. (2003). Public health and the built enviromnent: Historical, empirical, and theoretical foundations for an expanded role. Journal of Law, Medicine & Ethics, 31(4), 557566. https://doi.org/10.1111/j.1748-720X.2003.tb00123.xGoogle Scholar
Perkins, D. D., Wandersman, A., Rich, R. C. & Taylor, R. B. (1993). The physical environment of street crime: Defensible space, territoriality and incivilities. Journal of Environmental Psychology, 13(1), 2949. https://doi.org/10.1016/S0272–4944(05)80213-0Google Scholar
Pollack, C. E., Cubbin, C., Ahn, D. & Winkleby, M. (2005). Neighbourhood deprivation and alcohol consumption: Does the availability of alcohol play a role? International Journal of Epidemiology, 34(4), 772780. https://doi.org/10.1093/ije/dyi026Google Scholar
Popkin, B. M. (2006). Global nutrition dynamics: The world is shifting rapidly toward a diet linked with noncommunicable diseases. American Journal of Clinical Nutrition, 84(2), 289298. https://doi.org/10.1093/ajcn/84.1.289Google Scholar
Public Health Institute [PHI] (2015). Making the case for linking community development and health: A resource for those working to improve low-income communities and the lives of the people living in them. Public Health Institute. Retrieved from www.phi.org/resources/?resource=making-the-case-for-linking-community-development-and-healthGoogle Scholar
Putnam, R. D. (2000). Bowling Alone: The Collapse and Revival of American Community. New York: Simon & Schuster.Google Scholar
Rahman, T., Cushing, R. A. & Jackson, R. J. (2011). Contributions of built environment to childhood obesity. Mount Sinai Journal of Medicine, 78(1), 4957. https://doi.org/10.1002/msj.20235Google Scholar
Renalds, A., Smith, T. H. & Hale, P. J. (2010). A systematic review of built environment and health. Family and Community Health, 33(1), 6878. https://doi.org/10.1097/FCH.0b013e3181c4e2e5Google Scholar
Rhodes, T. (2002). The risk environment: A framework for understanding and reducing drug-related harm. International Journal of Drug Policy, 13(2), 8594. https://doi.org/10.1016/S0955–3959(02)00007-5Google Scholar
Roberto, C. A. & Kawachi, I. (2014). Use of psychology and behavioral economics to promote healthy eating. American Journal of Preventive Medicine, 47(6), 832837. https://doi.org/10.1016/j.amepre.2014.08.002Google Scholar
Sallis, J. F., Owen, N. & Fisher, E. B. (2008). Ecological models of health behavior. In Glanz, K., Rimer, B. K. & Viswanath, K. (Eds.), Health Behavior and Health Education: Theory, Research, and Practice (4th edition). San Francisco, CA: Jossey-Bass, pp. 465485.Google Scholar
Sameroff, A. (2010). A unified theory of development: A dialectic integration of nature and nurture. Child Development, 81(1), 622. https://doi.org/10.1111/j.1467-8624.2009.01378.xGoogle Scholar
Sampson, R. J., Raudenbush, S. W. & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277(5328), 918924. Retrieved from www.jstor.org/stable/2892902Google Scholar
Satcher, D., Okafor, M. & Dill, L. J. (2012). Impact of the built environment on mental and sexual health: Policy implications and recommendations. ISRN Public Health, 2012(9), 17. https://doi.org/10.5402/2012/806792Google Scholar
Scarr, S. & McCartney, K. (1983). How people make their own environments: A theory of genotype → environment effects. Child Development, 54(2), 424435. https://doi.org/10.2307/1129703Google Scholar
Schonlau, M., Scribner, R., Farley, T. A., et al. (2008). Alcohol outlet density and alcohol consumption in Los Angeles county and southern Louisiana. Geospatial Health, 3(1), 91101. https://doi.org/10.4081/gh.2008.235Google Scholar
Schreiber, L. R. N., Odlaug, B. L. & Grant, J. E. (2013). The overlap between binge eating disorder and substance use disorders: Diagnosis and neurobiology. Journal of Behavioral Addictions, 2(4), 191198. https://doi.org/10.1556/JBA.2.2013.015Google Scholar
Schulenberg, J. E., Johnston, L. D., O’Malley, P. M., et al. (2017). Monitoring the Future National Survey Results on Drug Use, 1975–2016: Volume II, College Students and Adults Ages 19–55. Ann Arbor: Institute for Social Research, The University of Michigan. Retrieved from http://monitoringthefuture.org/pubs.html#monographsGoogle Scholar
Schulz, A. & Northridge, M. E. (2016). Social determinants of health: Implications for environmental health promotion. Health Education & Behavior, 31(4), 455471. https://doi.org/10.1177/1090198104265598Google Scholar
Scribner, R. A., Cohen, D. A. & Fisher, W. (2000). Evidence of a structural effect for alcohol outlet density: A multilevel analysis. Alcoholism: Clinical and Experimental Research, 24(2), 188195. https://doi.org/10.1111/j.1530-0277.2000.tb04590.xGoogle Scholar
Sévigny, S., Ladouceur, R., Jacques, C. & Cantinotti, M. (2008). Links between casino proximity and gambling participation, expenditure, and pathology. Psychology of Addictive Behaviors, 22(2), 295301. https://doi.org/10.1037/0893-164X.22.2.295Google Scholar
Sharkey, P. (2013). Stuck in Place: Urban Neighborhoods and the End of Progress Toward Racial Equality. Chicago: The University of Chicago Press.Google Scholar
Sher, K. J., Bartholow, B. D. & Nanda, S. (2001). Short- and long-term effects of fraternity and sorority membership on heavy drinking: A social norms perspective. Psychology of Addictive Behaviors, 15(1), 4251. https://doi.org/10.1037/0893-164X.15.1.42Google Scholar
Sherba, R. T. & Gersper, B. E. (2017). Community college and university student gambling beliefs, motives, and behaviors. Community College Journal of Research and Practice, 41(12), 823841. https://doi.org/10.1080/10668926.2016.1233142Google Scholar
Song, L. (2011). Social capital and psychological distress. Journal of Health and Social Behavior, 52(4), 478492. https://doi.org/10.1177/0022146511411921Google Scholar
Stockwell, T. & Gruenewald, P. J. (2004). Controls on the physical availability of alcohol. In Heather, N. & Stockwell, T. (Eds.), The Essential Handbook of Treatment and Prevention of Alcohol Problems. Hoboken, NJ: Wiley, pp. 213233.Google Scholar
Stokols, D. & Shumaker, S. A. (1981). People in places: A transactional view of settings. In Harvey, J. H. (Ed.), Cognition, Social Behaviour and the Environment. Hillsdale, NJ: Lawrence Erlbaum Assoc., pp. 441488.Google Scholar
Suglia, S. F., Shelton, R. C., Hsiao, A., et al. (2016). Why the neighborhood social environment is critical in obesity prevention. Journal of Urban Health, 93(1), 206212. https://doi.org/10.1007/s11524–015-0017-6Google Scholar
Sussman, C. J., Harper, J. M., Harper, J. M., Stahl, J. L. & Weigle, P. (2018). Internet and video game addictions: Diagnosis, epidemiology, and neurobiology. Child and Adolescent Psychiatric Clinics of North America, 27(2), 307326. https://doi.org/10.1016/j.chc.2017.11.015Google Scholar
Tong, H. H. & Chim, D. (2013). The relationship between casino proximity and problem gambling. Asian Journal of Gambling Issues and Public Health, 3(1), 117. https://doi.org/10.1186/2195-3007-3-2Google Scholar
Toomey, T. L., Lenk, K. M. & Wagenaar, A. C. (2007). Environmental policies to reduce college drinking: An update of research findings. Journal of Studies on Alcohol and Drugs, 68(2), 208219. https://doi.org/10.15288/jsad.2007.68.208Google Scholar
Treno, A. J., Gruenewald, P. J., Grube, J. W., Saltz, R. F. & Paschal, M. J. (2015). Environmental approaches to prevention: A community-based perspective. In Ries, R. K., Fiellin, D. A., Miller, S. C. & Saitz, R. (Eds.), The ASAM Principles of Addiction Medicine (5th edition). Philadelphia, PA: Wolters Kluwer Health.Google Scholar
Treno, A. J., Johnson, F. W., Remer, L. G. & Gruenewald, P. J. (2007). The impact of outlet densities on alcohol-related crashes: A spatial panel approach. Accident Analysis and Prevention, 39(5), 894901. https://doi.org/10.1016/j.aap.2006.12.011Google Scholar
Tucker, J. S., Pollard, M. S., de la Haye, K., Kennedy, D. P. & Green, H. D. (2013). Neighborhood characteristics and the initiation of marijuana use and binge drinking. Drug and Alcohol Dependence, 128(1–2), 8389. https://doi.org/10.1016/j.drugalcdep.2012.08.006Google Scholar
Ulijaszek, S. J. & McLennan, A. K. (2016). Framing obesity in UK policy from the Blair years, 1997–2015: The persistence of individualistic approaches despite overwhelming evidence of societal and economic factors, and the need for collective responsibility. Obesity Reviews, 17(5), 397411. https://doi.org/10.1111/obr.12386Google Scholar
Vaeth, P. A. C., Wang-Schweig, M. & Caetano, R. (2017). Drinking, alcohol use disorder, and treatment access and utilization among U.S. racial/ethnic groups. Alcoholism: Clinical and Experimental Research, 41(1), 619. https://doi.org/10.1111/acer.13285Google Scholar
Victor, C., Scambler, S., Bond, J. & Bowling, A. (2000). Being alone in later life: Loneliness, social isolation and living alone. Reviews in Clinical Gerontology, 10(4), 407417. https://doi.org/10.1017/S0959259800104101Google Scholar
von Bertalanffy, L. (1968). General System Theory: Foundations, Development, Applications. New York: George Braziller.Google Scholar
Warner, T. D. (2016). Up in smoke: Neighborhood contexts of marijuana use from adolescence through young adulthood. Journal of Youth Adolescence, 45(1), 3553. https://doi.org/10.1007/s10964–015-0370-5Google Scholar
Warren, J. C., Smalley, K. B. & Barefoot, K. N. (2015). Perceived ease of access to alcohol, tobacco and other substances in rural and urban US students. Rural and Remote Health, 15(4), 110. Retrieved from www.rrh.org.au/journal/article/3397Google Scholar
Wechsler, H., Lee, J. E., Nelson, T. F. & Kuo, M. (2002). Underage college students’ drinking behavior, access to alcohol, and the influence of deterrence policies. Findings from the Harvard School of Public Health College Alcohol Study. Journal of American College Health, 50(5), 223236. https://doi.org/10.1080/07448480209595714Google Scholar
Weitzman, E. R., Folkman, A., Folkman, K. L. & Wechsler, H. (2003). The relationship of alcohol outlet density to heavy and frequent drinking and drinking-related problems among college students at eight universities. Health & Place, 9(1), 16. https://doi.org/10.1016/S1353–8292(02)00014-XGoogle Scholar
Welte, J. W., Barnes, G. M., Tidwell, M.-C. O., Hoffman, J. H. & Wieczorek, W. F. (2016a). The relationship between distance from gambling venues and gambling participation and problem gambling among U.S. adults. Journal of Gambling Studies, 32(4), 10551063. https://doi.org/10.1007/s10899–015-9583-5Google Scholar
Welte, J. W., Tidwell, M.-C. O., Barnes, G. M., Hoffman, J. H. & Wieczorek, W. F. (2016b). The relationship between the number of types of legal gambling and the rates of gambling behaviors and problems across U.S. states. Journal of Gambling Studies, 32(2), 379390. https://doi.org/10.1007/s10899–015-9551-0Google Scholar
Wilcox, P., Quisenberry, N. & Jones, S. (2016). The built environment and community crime risk interpretation. Journal of Research in Crime and Delinquency, 40(3), 322345. https://doi.org/10.1177/0022427803253801Google Scholar
Wilson, N., Syme, S. L., Boyce, W. T., Battistich, V. A. & Selvin, S. (2016). Adolescent alcohol, tobacco, and marijuana use: The influence of neighborhood disorder and hope. American Journal of Health Promotion, 20(1), 1119. https://doi.org/10.4278/0890-1171-20.1.11Google Scholar
Winters, M., Brauer, M., Setton, E. M. & Teschke, K. (2010). Built environment influences on healthy transportation choices: Bicycling versus driving. Journal of Urban Health, 87(6), 969993. https://doi.org/10.1007/s11524–010-9509-6Google Scholar
Wood, L., Shannon, T., Bulsara, M., et al. (2008). The anatomy of the safe and social suburb: An exploratory study of the built environment, social capital and residents perceptions of safety. Health & Place, 14(1), 1531. https://doi.org/10.1016/j.healthplace.2007.04.004Google Scholar
World Health Organization & Food and Agriculture Organization of the United Nations (2003). Diet, nutrition and the prevention of chronic diseases. Report of a joint WHO/FAO expert consultation. Geneva, Switzerland: World Health Organization (Technical Report Series 916). Retrieved from www.who.int/nutrition/publications/obesity/WHO_TRS_916/en/Google Scholar
Yip, W., Subramanian, S. V., Mitchell, A. D. & Lee, D. T. S. (2007). Does social capital enhance health and well-being? Evidence from rural China. Social Science & Medicine, 64(1), 3549. https://doi.org/10.1016/j.socscimed.2006.08.027Google Scholar
Zellner, D. A., Loaiza, S., Gonzalez, Z., et al. (2006). Food selection changes under stress. Physiology & Behavior, 87(4), 789793. https://doi.org/10.1016/j.physbeh.2006.01.014Google Scholar
Ziersch, A. M., Baum, F. E. & Putland, C. (2005). Neighbourhood life and social capital: The implications for health. Social Science & Medicine, 60(1), 7186. https://doi.org/10.1016/j.socscimed.2004.04.027Google Scholar

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  • Levels of Analysis and Etiology
  • Edited by Steve Sussman, University of Southern California
  • Book: The Cambridge Handbook of Substance and Behavioral Addictions
  • Online publication: 13 July 2020
  • Chapter DOI: https://doi.org/10.1017/9781108632591.014
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  • Levels of Analysis and Etiology
  • Edited by Steve Sussman, University of Southern California
  • Book: The Cambridge Handbook of Substance and Behavioral Addictions
  • Online publication: 13 July 2020
  • Chapter DOI: https://doi.org/10.1017/9781108632591.014
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  • Levels of Analysis and Etiology
  • Edited by Steve Sussman, University of Southern California
  • Book: The Cambridge Handbook of Substance and Behavioral Addictions
  • Online publication: 13 July 2020
  • Chapter DOI: https://doi.org/10.1017/9781108632591.014
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