The neuroeconomics of drug use

Neuroeconomics is an interdisciplinary research field, which emerged in the early 2000s, made up of a combination of cognitive neuroscience, psychology and behavioural economics. The primary aims of neuroeconomics are to: identify which brain regions encode subjective value (Chib, Rangel, Shimojo, & O’Doherty, 2009; Peters & Büchel, 2010); understand the brain mechanisms that underpin decision-making (Rangel, Camerer, & Montague, 2008); and use neuroscience findings to inform economic theories (Camerer, 2007). This field may open up new avenues for understanding drug use and addiction.

The brain’s computation of subjective value

The individual value assigned to an option or reward by a human is termed its subjective value. This can be measured by the amount of money a person is willing to pay for a reward (Chib et al., 2009), or the self-reported pleasure the person takes when consuming the reward (Kringelbach & Berridge, 2009). Researchers using functional magnetic resonance imaging (fMRI) have correlated subjective values of rewards with brain activity. It has been found that activity in the ventromedial prefrontal cortex (vmPFC), ventral striatum and poster cingulate cortex (Kable & Glimcher, 2007) is closely related to subjective value, suggesting these regions are involved in computation of value across different rewards. Indeed, one study demonstrated that exactly the same part of the vmPFC encodes value across money, food and toys/gifts (Chib et al., 2009). Other brain regions may be involved when making decisions about rewards, including the insula, amygdala and anterior cingulate cortex (Levy & Glimcher, 2012; Rangel et al., 2008).

Drug use and addiction

Decisions to buy and use drugs lie at the heart of recreational and problematic drug use. Indeed, addiction is sometimes considered to be a disorder of maladaptive decision-making (Redish, Jensen, & Johnson, 2008). People who are addicted often continue to use drugs even though they don’t want to in the long-term and they know it is causing them and others harm. Much behavioural economic research has been conducted with people who use drugs (Bickel, Johnson, Koffarnus, MacKillop, & Murphy, 2014). This has demonstrated that, on the whole, drugs act like any other reinforcer. As the effort/cost required to access drugs increases, and the availability of alternative rewards increases, the desire for drugs falls (Chase, MacKillop, & Hogarth, 2013; Higgins, 1997)

However, very little research, until recently, has examined what happens in the brain when people buy drugs. Are the same regions involved in the computation of drug value compared with non-drug reward value? Is there a pattern of brain activation that is associated with deciding to buy a drug vs. not? Does being addicted to a drug alter the way the brain computes value and the mechanisms that the brain uses to make decisions about rewards?

These are important theoretical questions because a number of well-known theories about addiction make the claim that decision-making in addiction is impaired (Redish et al., 2008) and specifically that goal-directed behaviour loses out to habitual behaviour (Everitt & Robbins, 2016). According to these theories, people with addiction should have different brain activity when valuing and buying drugs, compared those without.

Existing studies investigating the neuroeconomics of drug use and addiction

Only four published studies have examined the brain’s response when valuing and purchasing actual drugs (Bedi, Lindquist, & Haney, 2015; Gray et al., 2017; Lawn et al., 2019; MacKillop et al., 2014). All of these studies used fMRI while people made decisions about whether they wanted to buy drugs, which could actually be consumed later in the day.

The first study showed that the following brain regions were activated when deciding to purchase alcohol compared to deciding not to: medial prefrontal cortex (mPFC), dorsolateral prefrontal cortex (dlPFC), posterior cingulate cortex (PCC), and left anterior insula. The authors suggested that these regions are specifically involved in attention and intentionality (PPC), decisional balance (mPFC and dlPFC), and craving (insula) (MacKillop et al., 2014). The second study showed that the caudate nucleus is activated when deciding to purchase cigarettes compared to when not (Gray et al., 2017).

The third study investigated decision-making in people who use cannabis on a daily. A machine learning algorithm determined which regions activations’ significantly predicted buying cannabis vs. not. Brain regions including the PCC, caudate, putamen and insula were found to significantly predict purchase behaviour (Bedi et al., 2015).

These three studies demonstrate that the regions involved in non-drug-related decision-making are also involved in drug-related decision-making. However, none of these studies specifically investigated the relationship between brain activity and subjective value. Nor did they examine the important question of whether addiction moderates brain activity during drug valuation and purchase.

Recent neuroeconomics study with nicotine dependent individuals

Most recently, we (Lawn et al., 2019) compared nicotine dependent individuals with non-dependent, occasional cigarette smokers when they completed a neuroeconomics task in which both cigarettes and shop vouchers could be purchased for varying amounts of money. We showed that activation in the left ventral striatum and the ventromedial prefrontal cortex is positively associated with the value assigned to different amounts of cigarettes, regardless of addiction status (Figure 1). Furthermore, we found that when people decided to buy cigarettes, regions including the vmPFC, left amygdala and right nucleus accumbens were active, again regardless of addiction status (Figure 2). However, non-dependent, occasional cigarette smokers appeared to more strongly activate their posterior cingulate cortex when buying shop vouchers, compared to the nicotine dependent individuals (Figure 3).

This suggests that nicotine dependence does not disrupt the brain networks involved in valuing and purchasing cigarettes (i.e. drug reward), counter to various theories. Tentatively, nicotine dependence may be associated with perturbed non-drug reward purchase. This is supported by unpublished data that was reported earlier this year at the Society of Biological Psychiatry conference, in which dependent cannabis users did not show the expected value signal for food reward, but they did for cannabis reward (Bedi et al., 2019).

Future research and conclusions

In summary, existing neuroeconomics research concerning drug use has found that regions underpinning valuation and decision-making of non-rug rewards are still critically involved when buying drugs, across cigarettes, alcohol and cannabis. Thus, the brain does not encode the value of drugs in any special, unique way. Moreover, nicotine dependence does not appear to perturb the brain systems underlying valuation and purchase of cigarettes. This is consistent with behavioural findings demonstrating that addicted cigarette smokers do not employ different psychological processes when purchasing cigarettes (Hogarth & Chase, 2011, 2012) and that addicted drug users do not become compulsive in their drug-seeking (Hart, Haney, Foltin, & Fischman, 2000; Heyman, 2013). Tentatively, however, the neural basis of non-drug reward processing may be altered in nicotine dependence; although this requires replication.

Future research should investigate how addiction moderates the neural correlates of drug valuation and purchase across a wider range of substances. Whether or not brain function while actually buying drugs is altered in people with addiction will be critical in supporting or opposing theories which rely heavily on brain malfunction, including habit-based theories (Everitt & Robbins, 2016) and the Brain Disease Model of Addiction (Volkow, Koob, & McLellan, 2016). These findings will help direct novel treatment strategies towards behavioural manipulations of rational, goal-directed decision-making or attempting to neurally disrupt automatic, habitual behaviours.


Bedi, G., Hao, X., Konova, A., Van Dam, N., Glimcher, P., & Haney, M. (2019). F206. Neural Mechanisms Guiding Choices for Cannabis and Alternative Rewards in Cannabis Smokers. Biological psychiatry, 85(10), S293.

Bedi, G., Lindquist, M. A., & Haney, M. (2015). An fMRI-based neural signature of decisions to smoke cannabis. Neuropsychopharmacology, 40(12), 2657.

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.

Camerer, C. F. (2007). Neuroeconomics: Using neuroscience to make economic predictions. The Economic Journal, 117(519), C26-C42.

Chase, H. W., MacKillop, J., & Hogarth, L. (2013). Isolating behavioural economic indices of demand in relation to nicotine dependence. Psychopharmacology, 226(2), 371-380.

Chib, V. S., Rangel, A., Shimojo, S., & O’Doherty, J. P. (2009). Evidence for a common representation of decision values for dissimilar goods in human ventromedial prefrontal cortex. Journal of neuroscience, 29(39), 12315-12320.

Everitt, B. J., & Robbins, T. W. (2016). Drug addiction: updating actions to habits to compulsions ten years on. Annual review of psychology, 67, 23-50.

Gray, J. C., Amlung, M. T., Owens, M., Acker, J., Brown, C. L., Brody, G. H., . . . MacKillop, J. (2017). The neuroeconomics of tobacco demand: an initial investigation of the neural correlates of cigarette cost-benefit decision making in male smokers. Scientific Reports, 7, 41930.

Hart, C. L., Haney, M., Foltin, R., & Fischman, M. (2000). Alternative reinforcers differentially modify cocaine self-administration by humans. Behavioural pharmacology, 11(1), 87-91.

Heyman, G. M. (2013). Quitting drugs: quantitative and qualitative features. Annual review of clinical psychology, 9, 29-59.

Higgins, S. T. (1997). The influence of alternative reinforcers on cocaine use and abuse: a brief review. Pharmacology Biochemistry and Behavior, 57(3), 419-427.

Hogarth, L., & Chase, H. W. (2011). Parallel goal-directed and habitual control of human drug-seeking: Implications for dependence vulnerability. Journal of Experimental Psychology: Animal Behavior Processes, 37(3), 261.

Hogarth, L., & Chase, H. W. (2012). Evaluating psychological markers for human nicotine dependence: Tobacco choice, extinction, and Pavlovian-to-instrumental transfer. Experimental and clinical psychopharmacology, 20(3), 213.

Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nature neuroscience, 10(12), 1625.

Kringelbach, M. L., & Berridge, K. C. (2009). Towards a functional neuroanatomy of pleasure and happiness. Trends in cognitive sciences, 13(11), 479-487.

Lawn, W., Mithchener, L., Freeman, T. P., Benattayallah, A., Bisby, J. A., Wall, M. B., . . . Morgan, C. J. (2019). Value‐based decision‐making of cigarette and nondrug rewards in dependent and occasional cigarette smokers: An FMRI study. Addiction Biology, e12802.

Levy, D. J., & Glimcher, P. W. (2012). The root of all value: a neural common currency for choice. Current opinion in neurobiology, 22(6), 1027-1038.

MacKillop, J., Amlung, M. T., Acker, J., Gray, J. C., Brown, C. L., Murphy, J. G., . . . Sweet, L. H. (2014). The Neuroeconomics of Alcohol Demand: An Initial Investigation of the Neural Correlates of Alcohol Cost–Benefit Decision Making in Heavy Drinking Men. Neuropsychopharmacology, 39(8), 1988.

Peters, J., & Büchel, C. (2010). Neural representations of subjective reward value. Behavioural brain research, 213(2), 135-141.

Rangel, A., Camerer, C., & Montague, P. R. (2008). A framework for studying the neurobiology of value-based decision making. Nature reviews neuroscience, 9(7), 545.

Redish, A. D., Jensen, S., & Johnson, A. (2008). Addiction as vulnerabilities in the decision process. Behavioral and Brain Sciences, 31(4), 461-487.

Volkow, N. D., Koob, G. F., & McLellan, A. T. (2016). Neurobiologic advances from the brain disease model of addiction. New England Journal of Medicine, 374(4), 363-371.

Leave a Reply

Your email address will not be published. Required fields are marked *