Babbs, R. K., Sun, X., Felsted, J., et al. (2013). Decreased caudate response to milkshake is associated with higher body mass index and greater impulsivity. Physiology & Behavior, 121, 103–111.CrossRefGoogle ScholarPubMed

Benedict, C., Brooks, S. J., O’Daly, O. G., et al. (2012). Acute sleep deprivation enhances the brain’s response to hedonic food stimuli: an fMRI study. Journal of Clinical Endocrinology and Metabolism, 97(3), E443–447. doi: 10.1210/jc.2011-2759Google Scholar

Berkman, E. T. & Falk, E. B. (2013). Beyond brain mapping using neural measures to predict real-world outcomes. Current Directions in Psychological Science, 22(1), 45–50.CrossRefGoogle ScholarPubMed

Berridge, K. C. (2012). From prediction error to incentive salience: mesolimbic computation of reward motivation. European Journal of Neuroscience, 35(7), 1124–1143. doi: 10.1111/j.1460-9568.2012.07990.xGoogle Scholar

Blum, K., Cull, J. G., Braverman, E. R. & Comings, D. E. (1996). Reward deficiency syndrome. American Scientist, 84(2), 132–145.Google Scholar

Boswell, R. G., Sun, W., Suzuki, S. & Kober, H. (2018). Training in cognitive strategies reduces eating and improves food choice. Proceedings of the National Academy of Sciences, 115(48), E11238–E11247.CrossRefGoogle ScholarPubMed

Buhle, J. T., Silvers, J. A., Wager, T. D., et al. (2014). Cognitive reappraisal of emotion: a meta-analysis of human neuroimaging studies. Cerebral Cortex, 24(11), 2981–2990.Google Scholar

Burger, K. S. (2017). Frontostriatal and behavioral adaptations to daily sugar-sweetened beverage intake: a randomized controlled trial. The American Journal of Clinical Nutrition, 105(3), 555–563. doi: 10.3945/ajcn.116.140145CrossRefGoogle ScholarPubMed

Chen, G., Saad, Z. S., Britton, J. C., Pine, D. S. & Cox, R. W. (2013). Linear mixed-effects modeling approach to fMRI group analysis. Neuroimage, 73, 176–190.CrossRefGoogle ScholarPubMed

Crockford, D. N., Goodyear, B., Edwards, J., et al. (2005). Cue-induced brain activity in pathological gamblers. Biological Psychiatry, 58(10), 787–795.CrossRefGoogle ScholarPubMed

Dawe, S. & Loxton, N. J. (2004). The role of impulsivity in the development of substance use and eating disorders. Neuroscience and Biobehavioral Reviews, 28(3), 343–351. doi: 10.1016/j.neubiorev.2004.03.007CrossRefGoogle ScholarPubMed

Fowler, J. S., Volkow, N. D., Ding, Y. S., et al. (1998). PET and the study of drug action in the human brain. Pharmaceutical News, 5, 11–16.Google Scholar

Frank, G. K. W., Reynolds, J. R., Shott, M. E., et al. (2012). Anorexia nervosa and obesity are associated with opposite brain reward response. Neuropsychopharmacology, 37(9), 2031–2046.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), 652.Google Scholar

Goldstein, R. Z., Tomasi, D., Alia-Klein, N., et al. (2009). Dopaminergic response to drug words in cocaine addiction. Journal of Neuroscience, 29(18), 6001–6006.Google Scholar

Goudriaan, A. E., et al. (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), 491–503.CrossRefGoogle ScholarPubMed

Haller, S. & Bartsch, A. J. (2009). Pitfalls in fMRI. European Radiology, 19(11), 2689–2706.CrossRefGoogle ScholarPubMed

Koehler, S., Hasselmann, E., Wüstenberg, T., et al. (2015). Higher volume of ventral striatum and right prefrontal cortex in pathological gambling. Brain Structure and Function, 220(1), 469–477. doi: 10.1007/s00429-013-0668-6CrossRefGoogle ScholarPubMed

Koob, G. F. & Volkow, N. D. (2010). Neurocircuitry of addiction. Neuropsychopharmacology, 35(1), 217–238. doi: http://dx.doi.org/10.1038/npp.2009.110CrossRefGoogle ScholarPubMed

Lieberman, M. D. & Cunningham, W. A. (2009). Type I and Type II error concerns in fMRI research: re-balancing the scale. Social Cognitive and Affective Neuroscience, 4(4), 423–428.CrossRefGoogle ScholarPubMed

Liu, J., Liang, J., Qin, W., et al. (2009). Dysfunctional connectivity patterns in chronic heroin users: an fMRI study. Neuroscience Letters, 460(1), 72–77. doi: 10.1016/j.neulet.2009.05.038Google Scholar

Martinez, D., Slifstein, M., Narendran, R., et al. (2009). Dopamine D1 receptors in cocaine dependence measured with PET and the choice to self-administer cocaine. Neuropsychopharmacology, 34(7), 1774.CrossRefGoogle ScholarPubMed

Morgan, V. L., Dawant, B. M., Li, Y. & Pickens, D. R. (2007). Comparison of fMRI statistical software packages and strategies for analysis of images containing random and stimulus-correlated motion. Computerized Medical Imaging and Graphics, 31(6), 436–446.CrossRefGoogle ScholarPubMed

Poldrack, R. A., Fletcher, P. C., Henson, R. N., et al. (2008). Guidelines for reporting an fMRI study. Neuroimage, 40(2), 409–414.Google Scholar

Potenza, M. N., Steinberg, M. A., Skudlarski, P., et al. (2003). Gambling urges in pathological gambling: a functional magnetic resonance imaging study. Archives of General Psychiatry, 60(8), 828–836. doi: 10.1001/archpsyc.60.8.828CrossRefGoogle ScholarPubMed

Volkow, N., Chang, L., Wang, G. J., et al. (2001). Low level of brain dopamine D2 receptors in methamphetamine abusers: association with metabolism in the orbitofrontal cortex. American Journal of Psychiatry, 158(12), 2015–2021.Google Scholar

Volkow, N. D., Fowler, J. S., Wang, G. J., et al. (2009). Imaging dopamine’s role in drug abuse and addiction. Neuropharmacology, 56, 3–8.Google Scholar

Vul, E. & Pashler, H. (2012). Voodoo and circularity errors. Neuroimage, 62(2), 945–948.CrossRefGoogle ScholarPubMed

Wang, G.-J., Volkow, N. D., Thanos, P. K. & Fowler, J. S. (2004). Similarity between obesity and drug addiction as assessed by neurofunctional imaging: a concept review. Journal of Addictive Diseases, 23(3), 39–53.CrossRefGoogle ScholarPubMed

Wilcox, C. E., Braskie, M. N., Kluth, J. T. & Jagust, W. J. (2010). Overeating behavior and striatal Dopamine with 6-[1 8 F]-Fluoro-L-m-Tyrosine PET. Journal of Obesity, 2010, 909348.Google Scholar

Woo, C. W., Krishnan, A. & Wager, T. D. (2014). Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. Neuroimage, 91, 412–419.CrossRefGoogle ScholarPubMed

Yarkoni, T. (2009). Big correlations in little studies: Inflated fMRI correlations reflect low statistical power – Commentary on Vul et al. (2009). Perspectives on Psychological Science, 4(3), 294–298.CrossRefGoogle ScholarPubMed

Zhao, L. Y., Tian, J., Wang, W., et al. (2012). The role of dorsal anterior cingulate cortex in the regulation of craving by reappraisal in smokers. PLoS ONE, 7(8) e43598.Google Scholar

Abrams, D. B., Monti, P. M., Carey, K. B., Pinto, R. P. & Jacobus, S. I. (1988). Reactivity to smoking cues and relapse: Two studies of discriminant validity. Behaviour Research and Therapy, 26(3), 225–233. doi:10.1016/0005-7967(88)90003-4Google Scholar

Ait-Daoud, N., Seneviratne, C., Smith, J. B., et al. (2012). Preliminary evidence for cue-induced alcohol craving modulated by serotonin transporter gene polymorphism rs1042173. Frontiers in Psychiatry, 3. doi:10.3389/fpsyt.2012.00006CrossRefGoogle ScholarPubMed

American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders (5th edition). Washington, DC: American Psychiatric Association Publishing.Google Scholar

Amlung, M. T., Acker, J., Stojek, M. K., Murphy, J. G. & MacKillop, J. (2011). Is talk “cheap”? An initial investigation of the equivalence of alcohol purchase task performance for hypothetical and actual rewards. Alcoholism: Clinical and Experimental Research, 36(4), 716–724. doi:10.1111/j.1530-0277.2011.01656.xCrossRefGoogle ScholarPubMed

Arnsten, A. F. T. (2009). Stress signalling pathways that impair prefrontal cortex structure and function. Nature Reviews Neuroscience, 10(6), 410–422. https://doi.org/10.1038/nrn2648CrossRefGoogle ScholarPubMed

Avants, S., Margolin, A., Kosten, T. R. & Cooney, N. L. (1995). Differences between responders and nonresponders to cocaine cues in the laboratory. Addictive Behaviors, 20(2), 215–224. doi:10.1016/0306-4603(94)00066-2Google Scholar

Balodis, I. M., Wynne-Edwards, K. E. & Olmstead, M. C. (2010). The other side of the curve: Examining the relationship between pre-stressor physiological responses and stress reactivity. Psychoneuroendocrinology, 35(9), 1363–1373. https://doi.org/10.1016/j.psyneuen.2010.03.011CrossRefGoogle ScholarPubMed

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), 749–757.Google Scholar

Becirevic, A., Reed, D. D., Amlung, M., et al. (2017). An initial study of behavioral addiction symptom severity and demand for indoor tanning. Experimental and Clinical Psychopharmacology, 25(5), 346–352. doi:10.1037/pha0000146CrossRefGoogle ScholarPubMed

Carter, B. L. & Tiffany, S. T. (1999). Meta-analysis of cue-reactivity in addiction research. Addiction, 94(3), 327–340. doi:10.1046/j.1360-0443.1999.9433273.xCrossRefGoogle ScholarPubMed

Chen, X., Gianferante, D., Hanlin, L., et al. (2017). HPA-axis and inflammatory reactivity to acute stress is related with basal HPA-axis activity. Psychoneuroendocrinology, 78, 168–176. https://doi.org/10.1016/j.psyneuen.2017.01.035Google Scholar

Clark, G. I., Rock, A. J., Mckeith, C. F. & Coventry, W. L. (2016). Cue-reactive rationality, visual imagery and volitional control predict cue-reactive urge to gamble in poker-machine gamblers. Journal of Gambling Studies, 33(3), 807–823. doi:10.1007/s10899-016-9650-6Google Scholar

DeLongis, A. Folkman, S. & Lazarus, R. S. (1988). The impact of daily stress on health and mood: Psychological and social resources as mediators. Journal of Personality and Social Psychology, 54(3), 486–495. https://doi.org/10.1037/0022-3514.54.3.486CrossRefGoogle ScholarPubMed

Dickerson, S. S. & Kemeny, M. E. (2004). Acute stressors and cortisol responses: A theoretical integration and synthesis of laboratory research. Psychological Bulletin, 130(3), 355–391. https://doi.org/10.1037/0033-2909.130.3.355CrossRefGoogle ScholarPubMed

Dixon, M. J., Harrigan, K. A., Santesso, D. L., et al. (2013a). The impact of sound in modern multiline video slot machine play. Journal of Gambling Studies, 30(4), 913–929. doi:10.1007/s10899-013-9391-8CrossRefGoogle Scholar

Dixon, M. J., Collins, K., Harrigan, K. A., Graydon, C. & Fugelsang, J. A. (2013b). Using sound to unmask losses disguised as wins in multiline slot machines. Journal of Gambling Studies, 31(1), 183–196. doi:10.1007/s10899-013-9411-8CrossRefGoogle Scholar

Dixon, M. J., Graydon, C., Harrigan, K. A., et al. (2014). The allure of multi-line games in modern slot machines. Addiction, 109(11), 1920–1928. doi:10.1111/add.12675Google Scholar

Dixon, M. J., Larche, C. J., Stange, M., Graydon, C. & Fugelsang, J. A. (2017). Near-misses and stop buttons in slot machine play: An investigation of how they affect players, and may foster erroneous cognitions. Journal of Gambling Studies, 34(1), 161–180. doi:10.1007/s10899-017-9699-xCrossRefGoogle Scholar

Dolinsky, Z. S. & Babor, T. F. (1997). Ethical, scientific and clinical issues in ethanol administration research involving alcoholics as human subjects. Addiction, 92, 1087–1097.CrossRefGoogle ScholarPubMed

Drummond, D. C., et al. (2000). Craving research: Future directions. Addiction, 95(8s2), 247–255. doi:10.1046/j.1360-0443.95.8s2.13.xCrossRefGoogle ScholarPubMed

Enoch, M.-A., et al. (2009). Ethical considerations for administering alcohol or alcohol cues to treatment-seeking alcoholics in a research setting: Can the benefits to society outweigh the risks to the individual? Alcoholism: Clinical and Experimental Research, 33(9), 1508–1512. doi:10.1111/j.1530-0277.2009.00988.xGoogle Scholar

Epstein, L. H., Handley, E. A., Dearing, K. K., et al. (2006). Purchases of food in youth. Influence of price and income. Psychological Science, 17(1), 82–89. doi:10.1111/j.1467-9280.2005.01668.xCrossRefGoogle ScholarPubMed

Epstein, L. H., Salvy, S. J., Carr, K. A., Dearing, K. K. & Bickel, W. K. (2010). Food reinforcement, delay discounting and obesity. Physiology & Behavior, 100(5), 438–445. doi:10.1016/j.physbeh.2010.04.029CrossRefGoogle ScholarPubMed

Epstein, L. H., Finkelstein, E., Raynor, H., et al. (2015). Experimental analysis of the effect of taxes and subsides on calories purchased in an on-line supermarket. Appetite, 95, 245–251. doi:10.1016/j.appet.2015.06.020CrossRefGoogle Scholar

Ferguson, C. J., Coulson, M. & Barnett, J. (2011). A meta-analysis of pathological gaming prevalence and comorbidity with mental health, academic and social problems. Journal of Psychiatric Research, 45(12), 1573–1578. doi:10.1016/j.jpsychires.2011.09.005Google Scholar

Ferrer-García, M., Gutiérrez-Maldonado, J., Pla-Sanjuanelo, J., et al. (2017). A randomised controlled comparison of second-level treatment approaches for treatment-resistant adults with bulimia nervosa and binge eating disorder: Assessing the benefits of virtual reality cue exposure therapy. European Eating Disorders Review, 25(6), 479–490. doi:10.1002/erv.2538CrossRefGoogle ScholarPubMed

Filbey, F. M., Claus, E., Audette, A. R., et al. (2007). Exposure to the taste of alcohol elicits activation of the mesocorticolimbic neurocircuitry. Neuropsychopharmacology, 33(6), 1391–1401. doi:10.1038/sj.npp.1301513Google Scholar

Filbey, F. M., Ray, L., Smolen, A., et al. (2008). Differential neural response to alcohol priming and alcohol taste cues is associated with DRD4 VNTR and OPRM1 genotypes. Alcoholism: Clinical and Experimental Research, 32(7), 1113–1123. doi:10.1111/j.1530-0277.2008.00692.xCrossRefGoogle ScholarPubMed

Filbey, F. M., Schacht, J. P., Myers, U. S., Chavez, R. S. & Hutchison, K. E. (2009). Individual and additive effects of the CNR1 and FAAH genes on brain response to marijuana cues. Neuropsychopharmacology, 35(4), 967–975. doi:10.1038/npp.2009.200Google Scholar

Fosnocht, A. Q. & Briand, L. A. (2016). Substance use modulates stress reactivity. Behavioral and Physiological Outcomes, 166, 32–42. https://doi.org/10.1126/science.1249098.SleepGoogle Scholar

Franken, I. H. a., et al. (1999). Cue reactivity and effects of cue exposure in abstinent posttreatment drug users. Journal of Substance Abuse Treatment, 16(1), 81–85. doi:10.1016/s0740-5472(98)00004-xGoogle Scholar

Goeders, N. E. (2003). The impact of stress on addiction. European Neuropsychopharmacology, 13(6), 435–441. https://doi.org/10.1016/j.euroneuro.2003.08.004CrossRefGoogle ScholarPubMed

Goldman, R. L., Borckardt, J. J., Frohman, H. A., et al. (2011). Prefrontal cortex transcranial direct current stimulation (tDCS) temporarily reduces food cravings and increases the self-reported ability to resist food in adults with frequent food craving. Appetite, 56(3), 741–746. doi:10.1016/j.appet.2011.02.013CrossRefGoogle ScholarPubMed

Goodman, W. K., Janson, J. & Wolf, J. M. (2017). Meta-analytical assessment of the effects of protocol variations on cortisol responses to the Trier Social Stress Test. Psychoneuroendocrinology, 80, 26–35. https://doi.org/10.1016/j.psyneuen.2017.02.030Google Scholar

Gruenewald, T. L., Kemeny, M. E., Aziz, N. & Fahey, J. L. (2004). Acute threat to the social self: Shame, social self-esteem, and cortisol activity. Psychosomatic Medicine, 66(6), 915–924. https://doi.org/10.1097/01.psy.0000143639.61693.efGoogle Scholar

Hartston, H. (2012). The case for compulsive shopping as an addiction. Journal of Psychoactive Drugs, 44(1), 64–67. doi:10.1080/02791072.2012.660110Google 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, 12–22.Google Scholar

Kirschbaum, C., Pirke, K.-M. & Hellhammer, D. H. (1993). The “Trier Social Stress Test” – A tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology, 28, 76–81.CrossRefGoogle Scholar

Kirschbaum, C. & Hellhammer, D. H. (1994). Review: Salivary cortisol in psychoneuroendocrine research: Recent developments and applications. Psychoneuroendocrinology, 19(4), 313–333.CrossRefGoogle ScholarPubMed

Kirschbaum, C., Kudielka, B. M., Gaab, J., Schommer, N. C. & Hellhammer, D. H. (1999). Impact of gender, menstrual cycle phase, and oral contraceptives on the activity of the hypothalamus-pituitary-adrenal axis. Psychosomatic Medicine, 61(2), 154–162. https://doi.org/10.1097/00006842-199903000-00006CrossRefGoogle ScholarPubMed

Koob, G. F. (1999). Corticotopin-releasing factor, norephinephrine, and stress. Stress: The International Journal on the Biology of Stress, 1800, 47–59. https://doi.org/10.1016/j.bbagen.2009.07.018Google Scholar

Koob, G. & Kreek, M. J. (2007). Stress, dysregulation of drug reward pathways, and the transition to drug dependence. American Journal of Psychiatry, 164(8), 1149–1159. https://doi.org/10.1176/appi.ajp.2007.05030503CrossRefGoogle ScholarPubMed

Kudielka, B. M., Buske-Kirschbaum, A., Hellhammer, D. H. & Kirschbaum, C. (2004). HPA axis responses to laboratory psychosocial stress in healthy elderly adults, younger adults, and children: Impact of age and gender. Psychoneuroendocrinology, 29(1), 83–98. https://doi.org/10.1016/S0306-4530(02)00146-4Google Scholar

Kuhn, S. & Gallinat, J. (2011). Common biology of craving across legal and illegal drugs – A quantitative meta-analysis of cue-reactivity brain response. European Journal of Neuroscience, 33(7), 1318–1326. doi:10.1111/j.1460-9568.2010.07590.xGoogle Scholar

Kushner, M. G., Abrams, K., Donahue, C., et al. (2007). Urge to gamble in problem gamblers exposed to a casino environment. Journal of Gambling Studies, 23(2), 121–132. doi:10.1007/s10899-006-9050-4Google Scholar

Laudat, M. H., Cerdas, S., Fournier, C., et al. (1988). Salivary cortisol measurement: A practical approach to assess pituitary-adrenal function. Journal of Clinical Endocrinology and Metabolism, 66(2), 343–348. https://doi.org/10.1210/jcem-66-2-343Google Scholar

Loo, J. A., Yan, W., Ramachandran, P. & Wong, D. T. (2010). Comparative human salivary and plasma proteomes. Journal of Dental Research, 89(10), 1016–1023. https://doi.org/10.1177/0022034510380414Google Scholar

MacKillop, J. & Lisman, S. A. (2008). Effects of a context shift and multiple context extinction on reactivity to alcohol cues. Experimental and Clinical Psychopharmacology, 16(4), 322–331. doi:10.1037/a0012686CrossRefGoogle ScholarPubMed

MacKillop, J., et al. (2010).Behavioral economic analysis of cue-elicited craving for alcohol. Addiction, 105(9), 1599–1607. doi:10.1111/j.1360-0443.2010.03004.xCrossRefGoogle ScholarPubMed

MacKillop, J., et al. (2012). Behavioral economic analysis of withdrawal- and cue-elicited craving for tobacco: An initial investigation. Nicotine & Tobacco Research, 14(12), 1426–1434., doi:10.1093/ntr/nts006Google Scholar

Maxwell, A. L., Loxton, N. J. & Hennegan, J. M. (2017). Exposure to food cues moderates the indirect effect of reward sensitivity and external eating via implicit eating expectancies. Appetite, 111, 135–141. doi:10.1016/j.appet.2016.12.037CrossRefGoogle ScholarPubMed

Márquez, S. & De la Vega, R. (2015). [Exercise addiction: An emergent behavioral disorder]. Nutricion Hospitalaria, 31(6). doi:10.3305/nh.2015.31.6.8934Google ScholarPubMed

McRae, A. L., Saladin, M. E., Brady, K. T., et al. (2006). Stress reactivity:  Biological and subjective responses to the cold pressor and Trier Social stressors. Human Psychopharmacology, 21(August), 377–385. https://doi.org/10.1002/hup.778CrossRefGoogle Scholar

Metrik, J., et al. (2016). Cue-elicited increases in incentive salience for marijuana: Craving, demand, and attentional bias. Drug and Alcohol Dependence, 167, 82–88. doi:10.1016/j.drugalcdep.2016.07.027Google Scholar

Meyer, G., Hauffa, B. P., Schedlowski, M., et al. S. (2000). Casino gambling increases heart rate and salivary cortisol in regular gamblers. Biological Psychiatry, 48(9), 948–953. https://doi.org/10.1016/S0006-3223(00)00888-XGoogle Scholar

Meyer, G., Schwertfeger, J., Exton, M. S., et al. (2004). Neuroendocrine response to casino gambling in problem gamblers. Psychoneuroendocrinology, 29(10), 1272–1280. https://doi.org/10.1016/j.psyneuen.2004.03.005CrossRefGoogle ScholarPubMed

Miller, G. A., Levin, D. N., Kozak, M. J., et al. (1987). Individual differences in imagery and the psychophysiology of emotion. Cognition and Emotion, 1(4), 367–390. https://doi.org/10.1080/02699938708408058Google Scholar

Miller, K. A. & Mays, D. (2020). Tanning as an addiction: The state of the research and implications for intervention. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions. Cambridge, UK: Cambridge University Press, pp. 362–372.Google Scholar

Mónok, K., Berczik, K., Urbán, R., et al. (2012). Psychometric properties and concurrent validity of two exercise addiction measures: A population wide study. Psychology of Sport and Exercise, 13(6), 739–746. doi:10.1016/j.psychsport.2012.06.003Google Scholar

Mogensen, M. & Jemec, G. B. (2010). The potential carcinogenic risk of tanning beds: clinical guidelines and patient safety advice. Cancer Management and Research, 2, 277–282.CrossRefGoogle ScholarPubMed

National Institute on Drug Abuse [NIDA] (2017). Trends & Statistics. NIDA, 24 April 2017. www.drugabuse.gov/related-topics/trends-statisticsGoogle Scholar

NIAAA. (2005). National Advisory Council on Alcohol Abuse and Alcoholism – Recommended Council Guidelines on Ethyl Alcohol Administration in Human Experimentation. Retrieved from www.niaaa.nih.gov/Resources/ResearchResources/Pages/job22.aspxGoogle Scholar

Niaura, R., Abrams, D., Demuth, B., Pinto, R. & Monti, P. (1989). Responses to smoking-related stimuli and early relapse to smoking. Addictive Behaviors, 14(4), 419–428. doi:10.1016/0306-4603(89)90029-4CrossRefGoogle ScholarPubMed

Niaura, R., Abrams, D. B., Shadel, W. G., et al. (1999). Cue exposure treatment for smoking relapse prevention: A controlled clinical trial. Addiction, 94(5), 685–695. doi:10.1046/j.1360-0443.1999.9456856.xGoogle Scholar

Niaura, R. (2002). Does “unlearning” ever really occur: Comment on Conklin & Tiffany. Addiction, 97(3), 357. doi:10.1046/j.1360-0443.2002.0055a.xGoogle Scholar

Norberg, M. M., et al. (2016). Craving cannabis: A meta-analysis of self-report and psychophysiological cue-reactivity studies. Addiction, 111(11), 1923–1934. doi:10.1111/add.13472Google Scholar

Park, C., Park, S. M., Gwak, A. R., et al. (2015). The effect of repeated exposure to virtual gambling cues on the urge to gamble. Addictive Behaviors, 41, 61–64. doi:10.1016/j.addbeh.2014.09.027CrossRefGoogle ScholarPubMed

Pavlov, I. P. & Anrep, G. V. (2003). Conditioned Reflexes. Mineola, NY: Dover Publications.Google Scholar

Pedram, P., Wadden, D., Amini, P., et al. (2013). Food addiction: Its prevalence and significant association with obesity in the general population. PLoS ONE, 8(9). doi:10.1371/journal.pone.0074832Google Scholar

Phan, J. M., Schneider, E., Peres, J., et al. (2017). Social evaluative threat with verbal performance feedback alters neuroendocrine response to stress. Hormones and Behavior, 96(September), 104–115. https://doi.org/10.1016/j.yhbeh.2017.09.007Google Scholar

Powell, J. (2006). Conditioned responses to drug-related stimuli: Is context crucial. Addiction, 90(8), 1089–1095. doi:10.1046/j.1360-0443.1995.90810897.xGoogle Scholar

Pruessner, J. C., Wolf, O. T., Hellhammer, D. H., et al. (1997). Free cortisol levels after awakening: A reliable biological marker for the assessment of adrenocortical activity. Life Sciences, 61(26), 2539–2549.Google Scholar

Pulido, C., Brown, S. A., Cummins, K., Paulus, M. P. & Tapert, S. F. (2010). Alcohol cue reactivity task development. Addictive Behaviors, 35(2), 84–90. doi:10.1016/j.addbeh.2009.09.006Google Scholar

Radley, J. J., Arias, C. M. & Sawchenko, P. E. (2006). Regional differentiation of the medial prefrontal cortex in regulating adaptive responses to acute emotional stress. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 26(50), 12967–12976. https://doi.org/10.1523/JNEUROSCI.4297-06.2006Google Scholar

Reed, D. D. (2015). Ultra-violet indoor tanning addiction: A reinforcer pathology interpretation. Addictive Behaviors, 41, 247–251. doi:10.1016/j.addbeh.2014.10.026Google Scholar

Reed, D. D, Naudé, G. P., Gelino, B. W. & Amlung, M. (2020). Behavioral economic considerations of novel addictions and nonaddictive behavior: Research and analytic methods. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions, Cambridge, UK: Cambridge University Press, pp. 73–86.Google Scholar

Rose, S. & Dhandayudham, A. (2014). Towards an understanding of Internet-based problem shopping behaviour: The concept of online shopping addiction and its proposed predictors. Journal of Behavioral Addictions, 3(2), 83–89. doi:10.1556/jba.3.2014.003Google Scholar

Saladin, M. E., Gray, K. M., Carpenter, M. J., et al. (2012). Gender differences in craving and cue reactivity to smoking and negative affect/stress cues. The American Journal on Addictions, 21(3), 210–220. doi:10.1111/j.1521-0391.2012.00232.xGoogle Scholar

Sayette, M. A., Shiffman, S., Tiffany, S. T., et al. (2000). The measurement of drug craving. Addiction, 95(8s2), 189–210. doi:10.1046/j.1360-0443.95.8s2.8.xGoogle Scholar

Schwabe, L., Haddad, L. & Schachinger, H. (2008). HPA axis activation by a socially evaluated cold-pressor test. Psychoneuroendocrinology, 33(6), 890–895. https://doi.org/10.1016/j.psyneuen.2008.03.001Google Scholar

Sephton, S. E., Lush, E., Dedert, E. A., et al. (2013). Diurnal cortisol rhythm as a predictor of lung cancer survival. Brain, Behavior, and Immunity, 30(Supplement), S163–S170. https://doi.org/10.1016/j.bbi.2012.07.019Google Scholar

Shaffer, H. J., Hall, M. N. & Bilt, J. V. (1999). Estimating the prevalence of disordered gambling behavior in the United States and Canada: A research synthesis. American Journal of Public Health, 89(9), 1369–1376. doi:10.2105/ajph.89.9.1369Google Scholar

Singh, A., Petrides, J. S., Gold, P. W., Chrousos, G. P. & Deuster, P. A. (1999). Differential hypothalamic-pituitary-adrenal axis reactivity to psychological and physical stress. The Journal of Clinical Endocrinology & Metabolism, 84(6), 1944–1948.Google Scholar

Sinha, R. (2008). Chronic stress, drug use, and vulnerability to addiction. Annals of the New York Academy of Sciences, 1141, 105–130. https://doi.org/10.1196/annals.1441.030.ChronicGoogle Scholar

Sinha, R., Talih, M., Malison, R., et al. (2003). Hypothalamic-pituitary-adrenal axis and sympatho-adreno-medullary responses during stress-induced and drug cue-induced cocaine craving states. Psychopharmacology, 170(1), 62–72. https://doi.org/10.1007/s00213-003-1525-8Google Scholar

Sterling, R. C., Dean, J., Weinstein, S. P., Murphy, J. & Gottheil, E. (2004). Gender differences in cue exposure reactivity and 9-month outcome. Journal of Substance Abuse Treatment, 27(1), 39–44. doi:10.1016/j.jsat.2004.03.008Google Scholar

Sussman, S. Y. (2017). Substance and Behavioral Addictions: Concepts, Causes, and Cures. Cambridge, United Kingdom: Cambridge University Press.Google Scholar

Symes, B. A. & Nicki, R. M. (1997). a preliminary consideration of cue-exposure, response-prevention treatment for pathological gambling behaviour: Two case studies. Journal of Gambling Studies, 13(2), 145–157. https://doi.org/10.1023/A:1024951301959Google Scholar

Szegedi, A., Lörch, B., Scheurich, A., et al. (2000). Cue exposure in alcohol dependent patients: preliminary evidence for different types of cue reactivity. Journal of Neural Transmission, 107(6), 721–730. doi:10.1007/s007020070073CrossRefGoogle ScholarPubMed

Templeton, J. A., Dixon, M. J., Harrigan, K. A. & Fugelsang, J. A. (2014). Upping the reinforcement rate by playing the maximum lines in multi-line slot machine play. Journal of Gambling Studies, 31(3), 949–964. doi:10.1007/s10899-014-9446-5Google Scholar

Testa, M., et al. (2006). Understanding alcohol expectancy effects: Revisiting the placebo condition. Alcoholism: Clinical and Experimental Research, 30(2), 339–348. doi:10.1111/j.1530-0277.2006.00039.xGoogle Scholar

Vadlin, S., Åslund, C. & Nilsson, K. W. (2015). Development and content validity of a screening instrument for gaming addiction in adolescents: The Gaming Addiction Identification Test (GAIT). Scandinavian Journal of Psychology, 56(4), 458–466. doi:10.1111/sjop.12196Google Scholar

Van Hedger, K., Bershad, A. K. & de Wit, H. (2017). Pharmacological challenge studies with acute psychosocial stress. Psychoneuroendocrinology, 85(August), 123–133. https://doi.org/10.1016/j.psyneuen.2017.08.020CrossRefGoogle ScholarPubMed

Watt, M. J., Weber, M. A., Davies, S. R. & Forster, G. L. (2017). Impact of juvenile chronic stress on adult cortico-accumbal function: Implications for cognition and addiction. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 79(June), 136–154. https://doi.org/10.1016/j.pnpbp.2017.06.015CrossRefGoogle ScholarPubMed

Weinstein, A. & Weinstein, Y. (2014). Exercise addiction – Diagnosis, bio-psychological mechanisms and treatment issues. Current Pharmaceutical Design, 20(25), 4062–4069. doi:10.2174/13816128113199990614Google Scholar

Wohl, M. J. A., Matheson, K., Young, M. M. & Anisman, H. (2008). Cortisol rise following awakening among problem gamblers: Dissociation from comorbid symptoms of depression and impulsivity. Journal of Gambling Studies, 24(1), 79–90. https://doi.org/10.1007/s10899-007-9080-6Google Scholar

World Health Organization. (2015, December). Health in 2015: From MDGs to SDGs. Retrieved March 02, 2018, from www.who.int/gho/publications/mdgs-sdgs/en/Google Scholar

Ainslie, G. (1975). Specious reward: A behavioral theory of impulsiveness and impulse control. Psychological Bulletin, 82(4), 463–496. doi: 10.1037/h0076860Google Scholar

Amlung, M. T., Acker, J., Stojek, M. K., Murphy, J. G. & MacKillop, J. (2012). Is talk “cheap”? An initial investigation of the equivalence of alcohol purchase task performance for hypothetical and actual rewards. Alcoholism: Clinical and Experimental Research, 36(4), 716–724. https://doi.org/10.1111/j.1530-0277.2011.01656.xCrossRefGoogle ScholarPubMed

Aston, E. R., Farris, S. G., MacKillop, J. & Metrik, J. (2017). Latent factor structure of a behavioral economic marijuana demand curve. Psychopharmacology, 234(16), 2421–2429. https://doi.org/10.1007/s00213-017-4633-6Google Scholar

Aston, E. R., MacKillop, J., Cassidy, R. & Metrik, J. (2015). Initial validation of a marijuana purchase task. Drug and Alcohol Dependence, 146, e212. https://doi.org/10.1016/j.drugalcdep.2014.09.044Google Scholar

Aston, E. R., Metrik, J. & MacKillop, J. (2015). Further validation of a marijuana purchase task. Drug and Alcohol Dependence, 152, 32–38. https://doi.org/10.1016/j.drugalcdep.2015.04.025Google Scholar

Atchley, P. & Warden, A. C. (2012). The need of young adults to text now: Using delay discounting to assess informational choice. Journal of Applied Research in Memory and Cognition, 1(4), 229–234. doi: 10.1016/j.jarmac.2012.09.001Google Scholar

Becirevic, A., Reed, D. D. & Amlung, M. (2017). An initial investigation of the effects of tanning-related cues on demand and craving for indoor tanning. Psychological Record, 67(2), 149–160. https://doi.org/10.1007/s40732-017-0246-zGoogle Scholar

Becirevic, A., Reed, D. D., Amlung, M., et al. (2017). An initial study of behavioral addiction symptom severity and demand for indoor tanning. Experimental and Clinical Psychopharmacology, 25(5), 346–352. https://doi.org/10.1037/pha0000146Google Scholar

Bickel, W. K. & Madden, G. J. (1999). A comparison of measures of relative reinforcing efficacy and behavioral economics: Cigarettes and money in smokers. Behavioural Pharmacology, 10(6–7), 627–637. https://doi.org/10.1097/00008877-199908001-00023Google Scholar

Bickel, W. K., Degrandpre, R. J. & Higgins, S. T. (1993). Behavioral economics: A novel experimental approach to the study of drug dependence. Drug and Alcohol Dependence, 33(2), 173–192. https://doi.org/10.1016/0376-8716(93)90059-YGoogle Scholar

Bickel, W. K., DeGrandpre, R. J., Higgins, S. T. & Hughes, J. R. (1990). Behavioral economics of drug self-administration. I. Functional equivalence of response requirement and drug dose. Life Sciences, 47(17), 1501–1510.Google Scholar

Bickel, W. K., DeGrandpre, R. J., Hughes, J. R. & Higgins, S. T. (1991). Behavioral economics of drug self‐administration. II. A unit‐price analysis of cigarette smoking. Journal of the Experimental Analysis of Behavior, 55(2), 145–154.CrossRefGoogle Scholar

Bickel, W. K., Jarmolowicz, D. P., Mueller, E. T. & Gatchalian, K. M. (2011). The behavioral economics and neuroeconomics of reinforcer pathologies: Implications for etiology and treatment of addiction. Current Psychiatry Reports, 13(5), 406–415. https://doi.org/10.1007/s11920-011-0215-1CrossRefGoogle ScholarPubMed

Bickel, W. K., Johnson, M. W., Koffarnus, M. N., MacKillop, J. & Murphy, J. G. (2014). The behavioral economics of substance use disorders: reinforcement pathologies and their repair. Annual Review of Clinical Psychology, 10, 641–677. https://doi.org/10.1146/annurev-clinpsy-032813-153724Google Scholar

Bickel, W. K., Odum, A. L. & Madden, G. J. (1999). Impulsivity and cigarette smoking: Delay discounting in current, never, and ex-smokers. Psychopharmacology, 146(4), 447–454. doi: 10.1007/PL00005490Google Scholar

Borges, A. M., Kuang, J., Milhorn, H. & Yi, R. (2016). An alternative approach to calculating area-under-the-curve (AUC) in delay discounting research. Journal of the Experimental Analysis of Behavior, 106(2), 145–155. doi: 10.1002/jeab.219Google Scholar

Bradshaw, C. M. & Killeen, P. R. (2012). A theory of behaviour on progressive ratio schedules, with applications in behavioural pharmacology. Psychopharmacology (Berl), 222(4), 549–564. https://doi.org/10.1007/s00213-012-2771-4Google Scholar

Branch, M. N. (2006). How research in behavioral pharmacology informs behavioral science. Journal of the Experimental Analysis of Behavior, 85(3), 407–423. https://doi.org/10.1901/jeab.2006.130-04Google Scholar

Broadbent, J. & Dakki, M. A. (2015). How much is too much to pay for internet access? A behavioral economic analysis of internet use. Cyberpsychology, Behavior, and Social Networking, 18(8), 457–461. https://doi.org/10.1089/cyber.2014.0367Google Scholar

Bruner, N. R. & Johnson, M. W. (2014). Demand curves for hypothetical cocaine in cocaine-dependent individuals. Psychopharmacology, 231(5), 889–897. https://doi.org/10.1007/s00213-013-3312-5Google Scholar

Catania, A. C. & Reynolds, G. S. (1968). A quantitative analysis of the responding maintained by interval schedules of reinforcement. Journal of the Experimental Analysis of Behavior, 11(3S2), 327–383.Google Scholar

Cheever, N. A., Rosen, L. D., Carrier, L. M. & Chavez, A. (2014). Out of sight is not out of mind: The impact of restricting wireless mobile device use on anxiety levels among low, moderate and high users. Computers in Human Behavior, 37, 290–297. doi: 10.1016/j.chb.2014.05.002CrossRefGoogle Scholar

Chung, S. & Herrnstein, R. J. (1967). Choice and delay of reinforcement. Journal of the Experimental Analysis of Behavior, 10(1), 67–74. doi: 10.1901/jeab.1967.10-67Google Scholar

Collins, R. L., Parks, G. A. & Marlatt, G. A. (1985). Social determinants of alcohol consumption: The effects of social interaction and model status on the self-administration of alcohol. Journal of Consulting and Clinical Psychology, 53(2), 189–200. https://doi.org/10.1037/0022-006X.53.2.189Google Scholar

Collins, R. L., Vincent, P. C., Yu, J., Liu, L. & Epstein, L. H. (2014). A behavioral economic approach to assessing demand for marijuana. Experimental and Clinical Psychopharmacology, 22(3), 211–221. https://doi.org/10.1037/a0035318Google Scholar

Comer, S. D., Bickel, W. K., Yi, R., et al. (2010). Human behavioral pharmacology, past, present, and future: Symposium presented at the 50th annual meeting of the Behavioral Pharmacology Society. Behavioural Pharmacology, 21(4), 251–277. https://doi.org/10.1097/Fbp.0b013e32833bb9f8CrossRefGoogle ScholarPubMed

Dariotis, J. K. & Johnson, M. W. (2015). Sexual discounting among high-risk youth ages 18–24: Implications for sexual and substance use risk behaviors. Experimental and Clinical Psychopharmacology, 23(1), 49–58. doi: 10.1037/a0038399Google Scholar

DeGrandpre, R. J., Bickel, W. K., Hughes, J. R. & Higgins, S. T. (1992). Behavioral economics of drug self-administration. Psychopharmacology, 108(1–2), 1–10.CrossRefGoogle ScholarPubMed

DeGrandpre, R. J., Bickel, W. K., Hughes, J. R., Layng, M. P. & Badger, G. (1993). Unit price as a useful metric in analyzing effects of reinforcer magnitude. Journal of the Experimental Analysis of Behavior, 60(3), 641–666.Google Scholar

Du, W., Green, L. & Myerson, J. (2002). Cross-cultural comparisons of discounting delayed and probabilistic rewards. The Psychological Record, 52(4), 479–492.Google Scholar

Epstein, L. H., Dearing, K. K. & Roba, L. G. (2010). A questionnaire approach to measuring the relative reinforcing efficacy of snack foods. Eating Behaviors, 11(2), 67–73. https://doi.org/10.1016/j.eatbeh.2009.09.006Google Scholar

Epstein, L. H., Salvy, S. J., Carr, K. A., Dearing, K. K. & Bickel, W. K. (2010). Food reinforcement, delay discounting and obesity. Physiology & Behavior, 100(5), 438–445. doi: 10.1016/j.physbeh.2010.04.029Google Scholar

Evenden, J. L. & Ryan, C. N. (1996). The pharmacology of impulsive behaviour in rats: The effects of drugs on response choice with varying delays of reinforcement. Psychopharmacology, 128(2), 161–170. doi: 10.1007/s002130050121Google Scholar

Fagerström, K. O. (1978). Measuring degree of physical dependence to tobacco smoking with reference to individualization of treatment. Addictive Behaviors, 3(3–4), 235–241. https://doi.org/10.1016/0306-4603(78)90024-2Google Scholar

Felton, M. & Lyon, D. O. (1966). The post‐reinforcement pause. Journal of the Experimental Analysis of Behavior, 9(2), 131–134.Google Scholar

Few, L. R., Acker, J., Murphy, C. & MacKillop, J. (2012). Temporal stability of a cigarette purchase task. Nicotine & Tobacco Research, 14(6), 761–765. https://doi.org/10.1093/ntr/ntr222Google Scholar

Field, M., Santarcangelo, M., Sumnall, H., Goudie, A. & Cole, J. (2006). Delay discounting and the behavioural economics of cigarette purchases in smokers: The effects of nicotine deprivation. Psychopharmacology, 186(2), 255–263. https://doi.org/10.1007/s00213-006-0385-4Google Scholar

Gray, J. C., Amlung, M. T., Palmer, A. A. & MacKillop, J. (2016). Syntax for calculation of discounting indices from the monetary choice questionnaire and probability discounting questionnaire. Journal of the Experimental Analysis of Behavior, 106(2), 156–163. doi: 10.1002/jeab.221Google Scholar

Green, L., Myerson, J., Shah, A. K., Estle, S. J. & Holt, D. D. (2007). Do adjusting-amount and adjusting-delay procedures produce equivalent estimates of subjective value in pigeons? Journal of the Experimental Analysis of Behavior, 87(3), 337–347. doi: 10.1901/jeab.2007.37-06Google Scholar

Greenwald, M. K. & Hursh, S. R. (2006). Behavioral economic analysis of opioid consumption in heroin-dependent individuals: Effects of unit price and pre-session drug supply. Drug and Alcohol Dependence, 85(1), 35–48. https://doi.org/http://dx..org/10.1016/j.drugalcdep.2006.03.007Google Scholar

Griffiths, R. R., Bigelow, G. E. & Henningfield, J. E. (1980). Similarities in animal and human drug-taking behavior. In Mello, N. K. (Ed.), Advances in Substance Abuse (Volume 1). Greenwich, CT: JAI Press, pp. 1–90.Google Scholar

Griffiths, R. R., Brady, J. V. & Bradford, L. D. (1979). Predicting the abuse liability of drugs with animal drug self-administration procedures: Psychomotor stimulants and hallucinogens. In Thompson, T. & Dews, P. B. (Eds.), Advances in Behavioral Pharmacology (Volume 2). New York: Academic Press, pp. 163–208.Google Scholar

Heatherton, T. F., Kozlowski, L. T., Frecker, R. C. & Fagerström, K. O. (1991). The Fagerström test for nicotine dependence: A revision of the Fagerström Tolerance Questionnaire. British Journal of Addiction, 86(9), 1119–1127. https://doi.org/10.1111/j.1360-0443.1991.tb01879.xGoogle Scholar

Henley, A. J., DiGennaro Reed, F. D., Kaplan, B. A. & Reed, D. D. (2016). Quantifying efficacy of workplace reinforcers: An application of behavioral economic demand to evaluate hypothetical work performance. Translational Issues in Psychological Science, 2(2), 174–183. https://doi.org/10.1037/tps0000068Google Scholar

Herrmann, E. S., Hand, D. J., Johnson, M. W., Badger, G. J. & Heil, S. H. (2014). Examining delay discounting of condom-protected sex among opioid-dependent women and non-drug-using control women. Drug and Alcohol Dependence, 144, 53–60. doi: 10.1016/j.drugalcdep.2014.07.026Google Scholar

Herrnstein, R. J. (1971). Quantitative hedonism. Journal of Psychiatric Research, 8(3–4), 399–412. https://doi.org/Doi 10.1016/0022-3956(71)90033-1CrossRefGoogle ScholarPubMed

Hodos, W. (1961). Progressive ratio as a measure of reward strength. Science, 134(3483), 943–944. Retrieved from www.ncbi.nlm.nih.gov/pubmed/13714876Google Scholar

Hurlbut, S. C. & Sher, K. J. (1992). Assessing alcohol problems in college students. Journal of the American College Health Association, 41(2), 49–58. https://doi.org/10.1080/07448481.1992.10392818Google Scholar

Hursh, S. R. (1980). Economic concepts for the analysis of behavior. Journal of the Experimental Analysis of Behavior, 34(2), 219–238. https://doi.org/http://dx..org/10.1901/jeab.1980.34-219Google Scholar

Hursh, S. R. (1984). Behavioral economics. Journal of the Experimental Analysis of Behavior, 42(3), 435–452. https://doi.org/http://dx..org/10.1901/jeab.1984.42-435Google Scholar

Hursh, S. R. (1993). Behavioral economics of drug self-administration – An introduction. Drug and Alcohol Dependence, 33(2), 165–172. https://doi.org/10.1016/0376-8716(93)90058-XGoogle Scholar

Hursh, S. R. (2014). Behavioral economics and analysis of consumption and choice. In McSweeney, F. K. & Murphy, E. S. (Eds.), The Wiley Blackwell Handbook of Classical and Operant Conditioning. West Sussex, UK: John Wiley & Sons, pp. 275–305.Google Scholar

Hursh, S. R. & Roma, P. G. (2013). Behavioral economics and empirical public policy. Journal of the Experimental Analysis of Behavior, 99(1), 98–124. https://doi.org/10.1007/s00213-008-1120-0Google Scholar

Hursh, S. R. & Silberberg, A. (2008). Economic demand and essential value. Psychological Review, 115(1), 186–198. https://doi.org/10.1037/0033-295x115.1.186Google Scholar

Hursh, S. R., Madden, G. J., Spiga, R., DeLeon, I. G. & Francisco, M. T. (2013). The Translational Utility of Behavioral Economics: The Experimental Analysis of Consumption and Choice. Washington, DC: American Psychological Association. https://doi.org/http://dx..org/10.1037/13938-008Google Scholar

Jacobs, E. A. & Bickel, W. K. (1999). Modeling drug consumption in the clinic using simulation procedures: Demand for heroin and cigarettes in opioid-dependent outpatients. Experimental and Clinical Psychopharmacology, 7(4), 412–426. https://doi.org/10.1037//1064-1297.7.4.412Google Scholar

Jarmolowicz, D. P. & Lattal, K. A. (2010). On distinguishing progressively increasing response requirements for reinforcement. The Behavior Analyst, 33(1), 119–125.Google 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. 12–22.Google Scholar

Jarmolowicz, D. P., Bickel, W. K. & Gatchalian, K. M. (2013). Alcohol-dependent individuals discount sex at higher rates than controls. Drug and Alcohol Dependence, 131(3), 320–323. doi: 10.1016/j.drugalcdep.2012.12.014Google Scholar

Jarmolowicz, D. P., Landes, R. D., Christensen, D. R., et al. (2014). Discounting of money and sex: Effects of commodity and temporal position in stimulant-dependent men and women. Addictive Behaviors, 39(11), 1652–1657. doi: 10.1016/j.addbeh.2014.04.026Google Scholar

Jarmolowicz, D. P., Lemley, S. M., Mateos, A. & Sofis, M. J. (2016). A multiple-stimulus-without-replacement assessment for sexual partners: Purchase task validation. Journal of Applied Behavior Analysis, 49(3), 723–729. https://doi.org/10.1002/jaba.313Google Scholar

Jarmolowicz, D. P., Reed, D. D. & Bickel, W. K. (2015). Neuroeconomics: Implications for understanding and treating addictive behavior. In Feldstein Ewing, S. W. & Filbey, F. M. (Eds.), Neuroimaging and Psychosocial Addiction Treatment: An Integrative Guide for Researchers and Clinicians. New York, NY: Palgrave Macmillan, pp. 141–157.Google Scholar

Jarmolowicz, D. P., Reed, D. D., Bruce, A. S., et al. (2016). Using EP50 to forecast treatment adherence in individuals with multiple sclerosis. Behavioural Processes, 132, 94–99. doi: 10.1016/j.beproc.2016.09.003Google Scholar

Johnson, M. W. & Bickel, W. K. (2008). An algorithm for identifying nonsystematic delay-discounting data. Experimental and Clinical Psychopharmacology, 16(3), 264–274. doi:10.1037/1064-1297.16.3.264CrossRefGoogle ScholarPubMed

Johnson, M. W. & Bruner, N. R. (2012). The Sexual Discounting Task: HIV risk behavior and the discounting of delayed sexual rewards in cocaine dependence. Drug and Alcohol Dependence, 123(1–3), 15–21. https://doi.org/10.1016/j.drugalcdep.2011.09.032Google Scholar

Johnson, M. W., Herrmann, E. S., Sweeney, M. M., LeComte, R. S. & Johnson, P. S. (2017). Cocaine administration dose-dependently increases sexual desire and decreases condom use likelihood: The role of delay and probability discounting in connecting cocaine with HIV. Psychopharmacology, 234(4), 599–612. doi: 10.1007/s00213-016-4493-5Google Scholar

Junco, R. & Cotten, S. R. (2012). No A 4 U: The relationship between multitasking and academic performance. Computers & Education, 59(2), 505–514. doi: 10.1016/j.compedu.2011.12.023Google Scholar

Kaplan, B. A., Amlung, M., Reed, D. D., et al. (2016). Automating scoring of delay discounting for the 21- and 27-item monetary choice questionnaires. The Behavior Analyst, 39(2), 293–304. doi: 10.1007/s40614-016-0070-9Google Scholar

Kaplan, B. A., Foster, R. N. S., Reed, D. D., et al. (2018). Understanding alcohol motivation using the alcohol purchase task: A methodological systematic review. Drug and Alcohol Dependence, 191, 117–140. https://doi.org/10.1016/J.DRUGALCDEP.2018.06.029CrossRefGoogle ScholarPubMed

Kaplan, B. A., Gelino, B. W. & Reed, D. D. (2018). A behavioral economic approach to green consumerism: Demand for reusable shopping bags. Behavior and Social Issues, 27, 20–30. https://doi.org/10.5210/bsi.v.27i0.8003Google Scholar

Kirby, K. N. & Maraković, N. N. (1996). Delay-discounting probabilistic rewards: Rates decrease as amounts increase. Psychonomic Bulletin & Review, 3(1), 100–104. doi: 10.3758/BF03210748Google 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), 78–87. doi: 10.1037/0096-3445.128.1.78Google Scholar

Koffarnus, M. N. & Bickel, W. K. (2014). A 5-trial adjusting delay discounting task: Accurate discount rates in less than one minute. Experimental and Clinical Psychopharmacology, 22(3), 222–228. doi: 10.1037/a0035973CrossRefGoogle ScholarPubMed

Kwon, M., Lee, J., Won, W., et al. (2013). Development and validation of a smartphone addiction scale (SAS). PLoS ONE, 8(2), 7.Google Scholar

Lawyer, S. R. (2008). Probability and delay discounting of erotic stimuli. Behavioural Processes, 79(1), 36–42. doi: 10.1016/j.beproc.2008.04.009Google Scholar

Lea, S. E. (1976). Titration of schedule parameters by pigeons. Journal of the Experimental Analysis of Behavior, 25(1), 43–54. doi: 10.1901/jeab.1976.25-43Google Scholar

Lemley, S. M., Kaplan, B. A., Reed, D. D., Darden, A. C. & Jarmolowicz, D. P. (2016). Reinforcer pathologies: Predicting alcohol related problems in college drinking men and women. Drug and Alcohol Dependence, 167, 57–66. https://doi.org/10.1016/j.drugalcdep.2016.07.025Google Scholar

Leslie, J. C. (2003). A history of reinforcement: The role of reinforcement schedules in behavior pharmacology. The Behavior Analyst Today, 4(1), 98–108. http://dx.doi.org/10.1037/h0100017Google Scholar

MacKillop, J. & Tidey, J. W. (2011). Cigarette demand and delayed reward discounting in nicotine-dependent individuals with schizophrenia and controls: An initial study. Psychopharmacology, 216(1), 91–99. https://doi.org/10.1007/s00213-011-2185-8Google Scholar

MacKillop, J., Murphy, J. G., Ray, L. A., et al. (2008). Further validation of a cigarette purchase task for assessing the relative reinforcing efficacy of nicotine in college smokers. Experimental and Clinical Psychopharmacology, 16(1), 57–65. https://doi.org/10.1037/1064-1297.16.1.57Google Scholar

Madden, G. J. & Bickel, W. K. (1999). Abstinence and price effects on demand for cigarettes: A behavioral-economic analysis. Addiction, 94(4), 577–588. https://doi.org/10.1046/j.1360-0443.1999.94457712.xGoogle Scholar

Madden, G. J. & Bickel, W. K. (2010). Impulsivity: The Behavioral and Neurological Science of Discounting (1st edition). Washington, DC: American Psychological Association.Google Scholar

Madden, G. J., Petry, N. M. & Johnson, P. S. (2009). Pathological gamblers discount probabilistic rewards less steeply than matched controls. Experimental and Clinical Psychopharmacology, 17(5), 283–290. https://doi.org/10.1037/A0016806Google Scholar

Mazur, J. E. (1987). An adjusting procedure for studying delayed reinforcement. In Commons, M. L., Mazur, J. E., Nevin, J. A. & Rachlin, H. (Eds.), Quantitative Analysis of Behavior: Volume 5. The Effect of Delay and Intervening Events on Reinforcement Value. Hillsdale, NJ: Erlbaum, pp. 55–73. Retrieved from https://search.proquest.com/docview/617226485?accountid=14556Google Scholar

Metrik, J., Aston, E. R., Kahler, C. W., et al. (2016). Cue-elicited increases in incentive salience for marijuana: Craving, demand, and attentional bias. Drug and Alcohol Dependence, 167, 82–88. https://doi.org/10.1016/j.drugalcdep.2016.07.027Google Scholar

Mischel, W. & Ebbesen, E. B. (1970). Attention in delay of gratification. Journal of Personality and Social Psychology, 16(2), 329–337. doi: 10.1037/h0029815Google Scholar

Mischel, W., Ebbesen, E. B. & Raskoff Zeiss, A. (1972). Cognitive and attentional mechanisms in delay of gratification. Journal of Personality and Social Psychology, 21(2), 204–218. doi: 10.1037/h0032198Google Scholar

Mischel, W., Shoda, Y. & Peake, P. K. (1988). The nature of adolescent competencies predicted by preschool delay of gratification. Journal of Personality and Social Psychology, 54(4), 687–696. doi: 10.1037/0022-3514.54.4.687Google Scholar

Mischel, W., Shoda, Y. & Rodriguez, M. L. (1989). Delay of gratification in children. Science, 244(4907), 933–938. doi: 10.1126/science.2658056Google Scholar

Mulhauser, K., Short, E. M. & Weinstock, J. (2018). Development and psychometric evaluation of the pornography purchase task. Addictive Behaviors, 84(December 2017), 207–214. https://doi.org/10.1016/j.addbeh.2018.04.016Google Scholar

Murphy, J. G. & MacKillop, J. (2006). Relative reinforcing efficacy of alcohol among college student drinkers. Experimental and Clinical Psychopharmacology, 14(2), 219–227. https://doi.org/10.1037/1064-1297.14.2.219Google Scholar

Murphy, J. G., MacKillop, J., Skidmore, J. R. & Pederson, A. A. (2009). Reliability and validity of a demand curve measure of alcohol reinforcement. Experimental and Clinical Psychopharmacology, 17(6), 396–404. https://doi.org/10.1037/a0017684Google Scholar

Murphy, J. G., MacKillop, J., Tidey, J. W., Brazil, L. A. & Colby, S. M. (2011). Validity of a demand curve measure of nicotine reinforcement with adolescent smokers. Drug and Alcohol Dependence, 113(2–3), 207–214. https://doi.org/10.1016/J.DRUGALCDEP.2010.08.004Google Scholar

Myerson, J. & Green, L. (1995). Discounting of delayed rewards: Models of individual choice. Journal of the Experimental Analysis of Behavior, 64(3), 263–276. doi: 10.1901/jeab.1995.64-263Google Scholar

Myerson, J., Baumann, A. A. & Green, L. (2014). Discounting of delayed rewards: (A)theoretical interpretation of the Kirby questionnaire. Behavioural Processes, 107, 99–105. doi: 10.1016/j.beproc.2014.07.021Google Scholar

Myerson, J., Green, L. & Warusawitharana, M. (2001). Area under the curve as a measure of discounting. Journal of the Experimental Analysis of Behavior, 76(2), 235–243. doi: 10.1901/jeab.2001.76-235Google Scholar

Navarick, D. J. & Fantino, E. (1972). Interresponse time as a factor in choice. Psychonomic Science, 27(1), 4–6. doi: 10.3758/BF03328868Google Scholar

O'Donnell, S. & Epstein, L. H. (2019). Smartphones are more reinforcing than food for students. Addictive Behaviors, 90, 124–133.Google Scholar

Odum, A. L., Baumann, A. A. L. & Rimington, D. D. (2006). Discounting of delayed Hypothetical money and food: Effects of amount. Behavioural Processes, 73(3), 278–284. doi: 10.1016/j.beproc.2006.06.008Google Scholar

Petry, N. M. (2000). Effects of increasing income on polydrug use: A comparison of heroin, cocaine and alcohol abusers. Addiction, 95(5), 705–717. https://doi.org/10.1046/j.1360-0443.2000.9557056.xGoogle Scholar

Petry, N. M. (2001a). A behavioral economic analysis of polydrug abuse in alcoholics: Asymmetrical substitution of alcohol and cocaine. Drug and Alcohol Dependence, 62(1), 31–39. https://doi.org/10.1016/S0376-8716(00)00157-5Google Scholar

Petry, N. M. (2001b). The effects of housing costs on polydrug abuse patterns: A comparison of heroin, cocaine, and alcohol abusers. Experimental and Clinical Psychopharmacology, 9(1), 47–58. https://doi.org/10.1037/1064-1297.9.1.47Google Scholar

Petry, N. M. & Bickel, W. K. (1998). Polydrug abuse in heroin addicts: A behavioral economic analysis. Addiction, 93(3), 321–335. https://doi.org/10.1046/j.1360-0443.1998.9333212.xGoogle Scholar

Petry, N. M. & Bickel, W. K. (1999). A behavioral economic analysis of polydrug abuse in heroin addicts. In Chaloupka, F. J., Grossman, M., Bickel, W. K. & Saffer, H. (Eds.), The Economic Analysis of Substance Use and Abuse: An Integration of Econometrics and Behavioral Economic Research. Chicago, IL: University of Chicago Press, pp. 213–250. https://doi.org/10.1046/j.1360-0443.1998.9333212.xGoogle Scholar

Pickover, A. M., Messina, B. G., Correia, C. J., Garza, K. B. & Murphy, J. G. (2016). A behavioral economic analysis of the nonmedical use of prescription drugs among young adults. Experimental and Clinical Psychopharmacology, 24(1), 38–47. https://doi.org/10.1037/pha0000052Google Scholar

Pope, H. G., Kean, J., Nash, A., et al. (2010). A diagnostic interview module for anabolic-androgenic steroid dependence: Preliminary evidence of reliability and validity. Experimental and Clinical Psychopharmacology, 18(3), 203–213. https://doi.org/10.1037/a0019370Google Scholar

Rachlin, H. (2006). Notes on discounting. Journal of the Experimental Analysis of Behavior, 85(3), 425–435. doi: 10.1901/jeab.2006.85-05Google Scholar

Rachlin, H. & Green, L. (1972). Commitment, choice and self-control. Journal of the Experimental Analysis of Behavior, 17(1), 15–22. doi: 10.1901/jeab.1972.17-15Google Scholar

Rachlin, H., Raineri, A. & Cross, D. (1991). Subjective probability and delay. Journal of the Experimental Analysis of Behavior, 55(2), 233–244. doi: 10.1901/jeab.1991.55-233Google Scholar

Reed, D. D. (2015). Ultra-violet indoor tanning addiction: A reinforcer pathology interpretation. Addictive Behaviors, 41, 247–251. https://doi.org/10.1016/j.addbeh.2014.10.026Google Scholar

Reed, D. D., Becirevic, A., Atchley, P., Kaplan, B. A. & Liese, B. S. (2016a). Validation of a novel delay discounting of text messaging questionnaire. The Psychological Record, 66(2), 253–261. doi: 10.1007/s40732-016-0167-2Google Scholar

Reed, D. D., Kaplan, B. A. & Becirevic, A. (2015). Basic research on the behavioral economics of reinforcer value. In Autism Service Delivery: Bridging the Gap Between Science and Practice. Springer Science + Business Media, pp. 279–306. https://doi.org/10.1007/978-1-4939-2656-5_10Google Scholar

Reed, D. D., Kaplan, B. A., Becirevic, A., Roma, P. G. & Hursh, S. R. (2016b). Toward quantifying the abuse liability of ultraviolet tanning: A behavioral economic approach to tanning addiction. Journal of the Experimental Analysis of Behavior, 106(1), 93–106. https://doi.org/10.1002/jeab.216Google Scholar

Reed, D. D., Kaplan, B. A., Roma, P. G. & Hursh, S. R. (2014). Inter-method reliability of progression sizes in a hypothetical purchase task: Implications for empirical public policy. Psychological Record, 64(4), 671–679. https://doi.org/10.1007/s40732-014-0076-1Google Scholar

Reed, D. D., Naudé, G. P., Salzer, A. R., et al. (2020). Behavioral economic measurement of cigarette demand: A descriptive review of published approaches to the Cigarette Purchase Task. Experimental and Clinical Psychopharmacology. Advance online publication. doi: 10.1037/pha0000347Google Scholar

Richards, J. B., Mitchell, S. H., de Wit, H. & Seiden, L. S. (1997). Determination of discount functions in rats with an adjusting-amount procedure. Journal of the Experimental Analysis of Behavior, 67(3), 353-366. doi: 10.1901/jeab.1997.67-353Google Scholar

Richards, J. B., Zhang, L., Mitchell, S. H. & de Wit, H. (1999). Delay or probability discounting in a model of impulsive behavior: Effect of alcohol. Journal of the Experimental Analysis of Behavior, 71(2), 121–143. doi: 10.1901/jeab.1999.71-121Google Scholar

Roberts, J. A., Petnji Yaya, L. H. & Manolis, C. (2014). The invisible addiction: Cell-phone activities and addiction among male and female college students. Journal of Behavioral Addictions, 3(4), 254–265. doi: 10.1556/JBA.3.2014.015Google Scholar

Ross, E. & Kenakin, T. (2001). Pharmacodynamics – Mechanisms of drug action and the relationship between drug concentration and effect. In Hardman, J. G. & Gilman, A. J. (Eds.), The Pharmacological Basis of Therapeutics (10th edition). London: McGraw.Google Scholar

Samuelson, P. A. (1937). A note on measurement of utility. Review of Economic Studies, 4, 155–161.Google Scholar

Saville, B. K., Gisbert, A., Kopp, J. & Telesco, C. (2010). Internet addiction and delay discounting in college students. Psychological Record, 60(2), 273–286.Google Scholar

Shoda, Y., Mischel, W. & Peake, P. K. (1990). Predicting adolescent cognitive and self-regulatory competencies from preschool delay of gratification: Identifying diagnostic conditions. Developmental Psychology, 26(6), 978–986. doi: 10.1037/0012-1649.26.6.978Google Scholar

Skinner, B. F. (1953). Science and Human Behavior. Macmillan: Oxford.Google Scholar

Skinner, B. F. (1961). What is the experimental analysis of behavior? Journal of the Experimental Analysis of Behavior, 9(3), 213–218. https://doi.org/http://dx.doi.org/10.1037/11324-008Google Scholar

Sobell, L. C. & Sobell, M. B. (1992). Timeline follow-back. In Litten, R. Z. & Allen, J. P. (Eds.), Measuring Alcohol Consumption. Totowa, NJ: Humana Press, pp. 41–72. https://doi.org/10.1007/978-1-4612-0357-5_3Google Scholar

Sobell, M. B. & Sobell, L. C. (1973). Individualized behavior therapy for alcoholics – Republished. Behavior Therapy, 4(1), 49–72. https://doi.org/https://doi.org/10.1016/S0005-7894(73)80074-7Google Scholar

Stafford, D., LeSage, M. G. & Glowa, J. R. (1998). Progressive-ratio schedules of drug delivery in the analysis of drug self-administration: A review. Psychopharmacology, 139(3), 169–184. https://doi.org/10.1007/s002130050702Google Scholar

Strickland, J. C., Lile, J. A. & Stoops, W. W. (2017). Unique prediction of cannabis use severity and behaviors by delay discounting and behavioral economic demand. Behavioural Processes, 140, 33–40. doi: 10.1016/j.beproc.2017.03.017Google Scholar

Strickland, J. C., Lile, J. A., Rush, C. R. & Stoops, W. W. (2016). Comparing exponential and exponentiated models of drug demand in cocaine users. Experimental and Clinical Psychopharmacology, 24(6), 447–455. https://doi.org/10.1037/pha0000096Google Scholar

Strickland, J. C., Reynolds, A. R. & Stoops, W. W. (2016). Regulation of cocaine craving by cognitive strategies in an online sample of cocaine users. Psychology of Addictive Behaviors, 30(5), 607–612. https://doi.org/10.1037/adb0000180Google Scholar

Vincent, P. C., Collins, R. L., Liu, L., et al. (2017). The effects of perceived quality on behavioral economic demand for marijuana: A web-based experiment. Drug and Alcohol Dependence, 170, 174–180. https://doi.org/10.1016/j.drugalcdep.2016.11.013Google Scholar

Weinstock, J., Mulhauser, K., Oremus, E. G. & D’Agostino, A. R. (2016). Demand for gambling: Development and assessment of a gambling purchase task. International Gambling Studies, 16(2), 316–327. https://doi.org/10.1080/14459795.2016.1182570Google Scholar

Winger, G., Hursh, S. R., Casey, K. L. & Woods, J. H. (2002). Relative reinforcing strength of three methyl-d-aspartate antagonists with different onsets of action. Journal of Pharmacology and Experimental Therapeutics, 301(2), 690–697.Google Scholar

Yoon, J. H. & Higgins, S. T. (2008). Turning k on its head: Comments on use of an ED50 in delay discounting research. Drug and Alcohol Dependence, 95(1–2), 169–172. doi: 10.1016/j.drugalcdep.2007.12.011Google Scholar

Yurasek, A. M., Murphy, J. G., Clawson, A. H., Dennhardt, A. A. & MacKillop, J. (2013). Smokers report greater demand for alcohol on a behavioral economic purchase task. Journal of Studies on Alcohol and Drugs, 74(4), 626–34. https://doi.org/http://dx.doi.org/10.15288/jsad.2013.74.626Google Scholar

Adamson, S. J. & Sellman, J. D. (2003). A prototype screening instrument for cannabis use disorder: The Cannabis Use Disorders Identification Test (CUDIT) in an alcohol-dependent clinical sample. Drug and Alcohol Review, 22(3), 309–315.Google Scholar

Albrecht, U., Kirschner, N. E. & Grüsser, S. M. (2007). Diagnostic instruments for behavioural addiction: an overview. GMS Psycho-Social Medicine, 4.Google Scholar

Alexander, D. (2003). A marijuana screening inventory (experimental version): description and preliminary psychometric properties. The American Journal of Drug and Alcohol Abuse, 29(3), 619–646.Google Scholar

Alexander, D. E. & Leung, P. (2004). The Marijuana Screening Inventory (MSI‐X): Reliability, factor structure, and scoring criteria with a clinical sample. The American Journal of Drug and Alcohol Abuse, 30(2), 321–351.Google Scholar

Alexander, D. & Leung, P. (2006). The Marijuana Screening Inventory (MSI-X): Concurrent, convergent and discriminant validity with multiple measures. The American Journal of Drug and Alcohol Abuse, 32(3), 351–378.Google Scholar

American Psychiatric Association. (2000). Diagnostic Criteria from DSM-IV-TR. Washington, DC: American Psychiatric Publishing.Google Scholar

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). Washington, DC: American Psychiatric Association Publishing.Google Scholar

Andreassen, C. S., Griffiths, M. D., Hetland, J. & Pallesen, S. (2012). Development of a work addiction scale. Scandinavian Journal of Psychology, 53(3), 265–272.Google Scholar

Andreassen, C. S., Griffiths, M. D., Pallesen, S., et al. (2015). The Bergen Shopping Addiction Scale: Reliability and validity of a brief screening test. Frontiers in Psychology, 6. doi: https://doi.org/10.3389/fpsyg.2015.01374.Google Scholar

Andreassen, C. S., Torsheim, T., Brunborg, G. S. & Pallesen, S. (2012). Development of a Facebook addiction scale. Psychological Reports, 110(2), 501–517.Google Scholar

Armstrong, L., Phillips, J. G. & Saling, L. L. (2000). Potential determinants of heavier internet usage. International Journal of Human-Computer Studies, 53(4), 537–550.Google Scholar

Babor, T. F., de la Fuente, J. R., Saunders, J. & Grant, M. (2001). The Alcohol Use Disorders Identification Test: Guidelines for use in Primary Health Care. WHO Publication number 89.4, World Health Organization. Accessed November 7, 2018 from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.505.4146&rep=rep1&type=pdfGoogle Scholar

Bamber, D., Cockerill, I. M. & Carroll, D. (2000). The pathological status of exercise dependence. British Journal of Sports Medicine, 34(2), 125–132.Google Scholar

Bancroft, J. & Vukadinovic, Z. (2004). Sexual addiction, sexual compulsivity, sexual impulsivity, or what? Toward a theoretical model. Journal of Sex Research, 41(3), 225–234.Google Scholar

Barke, A., Nyenhuis, N. & Kröner-Herwig, B. (2012). The German version of the internet addiction test: a validation study. Cyberpsychology, Behavior, and Social Networking, 15(10), 534–542.Google Scholar

Barth, R. J. & Kinder, B. N. (1987). The mislabeling of sexual impulsivity. Journal of Sex & Marital Therapy, 13(1), 15–23.Google Scholar

Bashford, J., Flett, R. & Copeland, J. (2010). The Cannabis Use Problems Identification Test (CUPIT): Development, reliability, concurrent and predictive validity among adolescents and adults. Addiction, 105(4), 615–625.Google Scholar

Benotsch, E. G., Kalichman, S. C. & Kelly, J. A. (1999). Sexual compulsivity and substance use in HIV-seropositive men who have sex with men: Prevalence and predictors of high-risk behaviors. Addictive Behaviors, 24(6), 857–868.Google Scholar

Berczik, K., Szabó, A., Griffiths, M. D., et al. (2012). Exercise addiction: Symptoms, diagnosis, epidemiology, and etiology. Substance Use & Misuse, 47(4), 403–417.Google Scholar

Blanchard, K. A., Morgenstern, J., Morgan, T. J., Lobouvie, E. W. & Bux, D. A. (2003). Assessing consequences of substance use: Psychometric properties of the inventory of drug use consequences. Psychology of Addictive Behaviors, 17(4), 328.Google Scholar

Blow, F. C., Gillespie, B. W., Barry, K. L., Mudd, S. A. & Hill, E. M. (1998). Brief screening for alcohol problems in elderly populations using the Short Michigan Alcoholism Screening Test – Geriatric Version (SMAST-G). Alcoholism: Clinical and Experimental Research, 22(3). Paper presented at: Research Society on Alcoholism; June 20–25, 1998; Hilton Head Island, SC.Google Scholar

Boubeta, A. R., Salgado, P. G., Folgar, M. I., Gallego, M. A. & Mallou, J. V. (2015). PIUS-a: Problematic Internet Use Scale in adolescents. Development and psychometric validation. Adicciones, 27(1), 27–63.Google Scholar

Boudreau, B. & Poulin, C. (2007). The South Oaks Gambling Screen-revised Adolescent (SOGS-RA) revisited: A cut-point analysis. Journal of Gambling Studies, 23(3), 299–308.Google Scholar

Brand, M., Laier, C., Pawlikowski, M., et al. (2011). Watching pornographic pictures on the Internet: Role of sexual arousal ratings and psychological–psychiatric symptoms for using Internet sex sites excessively. Cyberpsychology, Behavior, and Social Networking, 14(6), 371–377.Google Scholar

Brenner, V. (1997). Psychology of computer use: XLVII. Parameters of Internet use, abuse and addiction: the first 90 days of the Internet Usage Survey. Psychological Reports, 80(3), 879–882.Google Scholar

Brewer, J. A. & Potenza, M. N. (2008). The neurobiology and genetics of impulse control disorders: relationships to drug addictions. Biochemical Pharmacology, 75(1), 63–75.Google Scholar

Browne, M., Goodwin, B. C. & Rockloff, M. J. (2018). Validation of the Short Gambling Harm Screen (SGHS): A tool for assessment of harms from gambling. Journal of Gambling Studies, 34(2), 499–512.Google Scholar

Buelens, M. & Poelmans, S. A. (2004). Enriching the Spence and Robbins' typology of workaholism: Demographic, motivational and organizational correlates. Journal of Organizational Change Management, 17(5), 440–458.Google Scholar

Burke, H. R. & Marcus, R. (1977). MacAndrew MMPI alcoholism scale: Alcoholism and drug addictiveness. The Journal of Psychology, 96(1), 141–148.Google Scholar

Butcher, J. N. & Owen, P. L. (1978). Objective personality inventories: Recent research and some contemporary issues. In Clinical Diagnosis of Mental Disorders. Boston, MA: Springer, pp. 475–545.Google Scholar

Caplan, S. E. (2002). Problematic Internet use and psychosocial well-being: Development of a theory-based cognitive–behavioral measurement instrument. Computers in Human Behavior, 18(5), 553–575.Google Scholar

Caplan, S. E. (2010). Theory and measurement of generalized problematic Internet use: A two-step approach. Computers in Human Behavior, 26(5), 1089–1097.Google Scholar

Carnes, P. (1983). The Sexual Addiction. Minneapolis, MN: CompCare Publications.Google Scholar

Carnes, P. J., Green, B. A., Merlo, L. J., et al. (2012). PATHOS: A brief screening application for assessing sexual addiction. Journal of Addiction Medicine, 6(1), 29.Google Scholar

Carnes, P., Green, B. & Carnes, S. (2010). The same yet different: Refocusing the Sexual Addiction Screening Test (SAST) to reflect orientation and gender. Sexual Addiction & Compulsivity, 17(1), 7–30.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 study. Chinese Journal of Psychology, 45, 251–266.Google Scholar

Clark, L. A., Livesley, W. J., Schroeder, M. L. & Irish, S. L. (1996). Convergence of two systems for assessing specific traits of personality disorder. Psychological Assessment, 8(3), 294.Google Scholar

Clark, L. A., McEwen, J. L., Collard, L. M. & Hickok, L. G. (1993). Symptoms and traits of personality disorder: Two new methods for their assessment. Psychological Assessment, 5(1), 81–91.Google Scholar

Clark, M. A., Michel, J. S., Zhdanova, L., Pui, S. Y. & Baltes, B. B. (2016). All work and no play? A meta-analytic examination of the correlates and outcomes of workaholism. Journal of Management, 42(7), 1836–1873.Google Scholar

Clark, M. & Calleja, K. (2008). Shopping addiction: A preliminary investigation among Maltese university students. Addiction Research & Theory, 16(6), 633–649.Google Scholar

Clopton, J. R. (1978). Alcoholism and the MMPI. A review. Journal of Studies on Alcohol, 39(9), 1540–1558.Google Scholar

Clopton, J. R., Weiner, R. H. & Davis, H. G. (1980). Use of the MMPI in identification of alcoholic psychiatric patients. Journal of Consulting and Clinical Psychology, 48(3), 416.Google Scholar

Coen, S. P. & Ogles, B. M. (1993). Psychological characteristics of the obligatory runner: A critical examination of the anorexia analogue hypothesis. Journal of Sport and Exercise Psychology, 15(3), 338–354.Google Scholar

Coleman, E. (1991). Compulsive sexual behavior: New concepts and treatments. Journal of Psychology & Human Sexuality, 4(2), 37–52.Google Scholar

Coleman, E., Miner, M., Ohlerking, F. & Raymond, N. (2001). Compulsive Sexual Behavior Inventory: A preliminary study of reliability and validity. Journal of Sex & Marital Therapy, 27(4), 325–332.Google Scholar

Currie, S. R., Casey, D. M. & Hodgins, D. C. (2010). Improving the Psychometric Properties of the Problem Gambling Severity Index. Ottawa: Canadian Consortium for Gambling Research.Google Scholar

Currie, S. R., Hodgins, D. C., Casey, D. M., et al. (2012). Examining the predictive validity of low‐risk gambling limits with longitudinal data. Addiction, 107(2), 400–406.Google Scholar

Davis, C., Brewer, H. & Ratusny, D. (1993). Behavioral frequency and psychological commitment: Necessary concepts in the study of excessive exercising. Journal of Behavioral Medicine, 16(6), 611–628.Google Scholar

de Meneses-Gaya, C., Zuardi, A. W., Loureiro, S. R. & Crippa, J. A. S. (2009). Alcohol Use Disorders Identification Test (AUDIT): An updated systematic review of psychometric properties. Psychology & Neuroscience, 2(1), 83.Google Scholar

Delmonico, D. & Miller, J. (2003). The Internet Sex Screening Test: A comparison of sexual compulsives versus non-sexual compulsives. Sexual and Relationship Therapy, 18(3), 261–276.Google Scholar

Demetrovics, Z., Király, O., Koronczai, B., et al. (2016). Psychometric properties of the Problematic Internet Use Questionnaire Short-Form (PIUQ-SF-6) in a nationally representative sample of adolescents. PLoS ONE, 11(8), e0159409.Google Scholar

Denis, C. M., Cacciola, J. S. & Alterman, A. I. (2013). Addiction Severity Index (ASI) summary scores: Comparison of the Recent Status Scores of the ASI-6 and the Composite Scores of the ASI-5. Journal of Substance Abuse Treatment, 45(5), 444–450.Google Scholar

Dhalla, S. & Kopec, J. A. (2007). The CAGE questionnaire for alcohol misuse: A review of reliability and validity studies. Clinical & Investigative Medicine, 30(1), 33–41.Google Scholar

Dhalla, S., Zumbo, B. D. & Poole, G. (2011). A review of the psychometric properties of the CRAFFT instrument: 1999–2010. Current Drug Abuse Reviews, 4(1), 57–64.Google Scholar

Dowling, N. A. & Quirk, K. L. (2009). Screening for internet dependence: do the proposed diagnostic criteria differentiate normal from dependent internet use? CyberPsychology & Behavior, 12(1), 21–27.Google Scholar

Earleywine, M., LaBrie, J. W. & Pedersen, E. R. (2008). A brief Rutgers Alcohol Problem Index with less potential for bias. Addictive Behaviors, 33(9), 1249–1253.Google Scholar

Eleuteri, S., Tripodi, F., Petruccelli, I., Rossi, R. & Simonelli, C. (2014). Questionnaires and scales for the evaluation of the online sexual activities: A review of 20 years of research. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 8(1), article 2. http://dx.doi.org/10.5817/CP2014-1-2Google Scholar

Ewing, J. A. (1984). Detecting alcoholism: the CAGE questionnaire. JAMA, 252(14), 1905–1907.Google Scholar

Fagerström, K. (2011). Determinants of tobacco use and renaming the FTND to the Fagerström Test for Cigarette Dependence. Nicotine & Tobacco Research, 14(1), 75–78.Google Scholar

Feeney, J. A. & Noller, P. (1990). Attachment style as a predictor of adult romantic relationships. Journal of Personality and Social Psychology, 58(2), 281–291.Google Scholar

Feldstein, S. W. & Miller, W. R. (2007). Does subtle screening for substance abuse work? A review of the Substance Abuse Subtle Screening Inventory (SASSI). Addiction, 102(1), 41–50.Google Scholar

Ferris, J. A. & Wynne, H. J. (2001). The Canadian Problem Gambling Index. Ottawa, ON: Canadian Centre on Substance Abuse, pp. 1–59.Google Scholar

Fioravanti, G., Primi, C. & Casale, S. (2013). Psychometric evaluation of the generalized problematic internet use scale 2 in an Italian sample. Cyberpsychology, Behavior, and Social Networking, 16(10), 761–766.Google Scholar

Flowers, C. P. & Robinson, B. (2002). A structural and discriminant analysis of the Work Addiction Risk Test. Educational and Psychological Measurement, 62(3), 517–526.Google Scholar

Forcehimes, A. A., Tonigan, J. S., Miller, W. R., Kenna, G. A. & Baer, J. S. (2007). Psychometrics of the drinker inventory of consequences (DrInC). Addictive Behaviors, 32(8), 1699–1704.Google Scholar

Friedman, A. S. & Utada, A. (1989). A method for diagnosing and planning the treatment of adolescent drug abusers (the Adolescent Drug Abuse Diagnosis [ADAD] instrument). Journal of Drug Education, 19(4), 285–312.Google Scholar

Galimov, A. & Black, D. W. (2020). Prevention and treatment of compulsive buying disorder. In Sussman, S. (Ed.) The Cambridge Handbook of Substance and Behavioral Addictions. Cambridge, UK: Cambridge University Press, pp. 271–279.Google Scholar

Gambino, B. & Lesieur, H. (2006). The south oaks gambling screen (SOGS): A rebuttal to critics. Journal of Gambling Issues, 17, doi: http://dx.doi.org/10.4309/jgi.2006.17.10.Google Scholar

Gearhardt, A. N., Boswell, R. G. & White, M. A. (2014). The association of “food addiction” with disordered eating and body mass index. Eating Behaviors, 15(3), 427–433.Google Scholar

Gearhardt, A. N., Corbin, W. R. & Brownell, K. D. (2009). Preliminary validation of the Yale food addiction scale. Appetite, 52(2), 430–436.Google Scholar

Gearhardt, A. N., Corbin, W. R. & Brownell, K. D. (2016). Development of the Yale Food Addiction Scale Version 2.0. Psychology of Addictive Behaviors, 30(1), 113–121.Google Scholar

Gerstein, D., Hoffmann, J., Larison, C., et al. (1999). Gambling Impact and Behavior Study. National Opinion Research Center at the University of Chicago.Google Scholar

Ginzler, J. A., Garrett, S. B., Baer, J. S. & Peterson, P. L. (2007). Measurement of negative consequences of substance use in street youth: An expanded use of the Rutgers Alcohol Problem Index. Addictive Behaviors, 32(7), 1519–1525.Google Scholar

Gold, S. N. & Heffner, C. L. (1998). Sexual addiction: Many conceptions, minimal data. Clinical Psychology Review, 18(3), 367–381.Google Scholar

Gormally, J., Black, S., Daston, S. & Rardin, D. (1982). The assessment of binge eating severity among obese persons. Addictive Behaviors, 7(1), 47–55.Google Scholar

Grant, J. E., Atmaca, M., Fineberg, N. A., et al. (2014). Impulse control disorders and “behavioural addictions” in the ICD‐11. World Psychiatry, 13(2), 125–127.Google Scholar

Grant, J. E., Odlaug, B. L. & Chamberlain, S. R. (2017). Gambling disorder, DSM-5 criteria and symptom severity. Comprehensive Psychiatry, 75, 1–5.Google Scholar

Grant, J. E., Potenza, M. N., Weinstein, A. & Gorelick, D. A. (2010). Introduction to behavioral addictions. The American Journal of Drug and Alcohol Abuse, 36(5), 233–241.Google Scholar

Grant, J. E., Steinberg, M. A., Kim, S. W., Rounsaville, B. J. & Potenza, M. N. (2004). Preliminary validity and reliability testing of a structured clinical interview for pathological gambling. Psychiatry Research, 128(1), 79–88.Google Scholar

Griffiths, M.D. (1996). Behavioural addiction: An issue for everybody? Journal of Workplace Learning, 8(3), 19–25.Google Scholar

Grigsby, T. J. (2019). Development and psychometric properties of the tobacco and nicotine consequences scale (TANCS) to screen for cigarette and e-cigarette misuse in community settings. Addictive Behaviors, 98, 106058.Google Scholar

Grigsby, T. J., Sussman, S., Chou, C. P. & Ames, S. L. (2017). Assessment of substance misuse. In Research Methods in the Study of Substance Abuse. Cham: Springer, pp. 197–233.Google Scholar

Grubbs, J. B., Sessoms, J., Wheeler, D. M. & Volk, F. (2010). The Cyber-Pornography Use Inventory: The development of a new assessment instrument. Sexual Addiction & Compulsivity, 17(2), 106–126.Google Scholar

Grupski, A. E., Hood, M. M., Hall, B. J., et al. (2013). Examining the Binge Eating Scale in screening for binge eating disorder in bariatric surgery candidates. Obesity Surgery, 23(1), 1–6.Google Scholar

Grüsser, S. M., Thalemann, R. & Griffiths, M. D. (2006). Excessive computer game playing: Evidence for addiction and aggression?. Cyberpsychology & Behavior, 10(2), 290–292.Google Scholar

Hannifin, J. (1990). The cannabis abuse syndrome screening test: a brief report. Unpublished report. Wellington: Drugs Advisory Committee.Google Scholar