• The exercise of free will is choosing among actions but with certain limitations.
  • The constraints include biopsychosocial factors that affect peoples addiction risk.
  • Automatic reactions to addiction-related stimuli also appear to limit free will.
  • Addictive behaviors can, however, be changed and replaced by pursuit of other goals.


Certain people are at risk for using alcohol or other drugs excessively and for developing problems with their use. Their susceptibility might arise from a variety of factors, including their genetic make-up, brain chemistry, family background, personality and other psychological variables, and environmental and sociocultural variables. Moreover, after substance use has become established, there are additional cognitive-motivational variables (e.g., substance-related attentional bias) that contribute to enacting behaviors consistent with the persons motivation to acquire and use the substance. People who are at such risk are likely to choose to use addictive substances even though doing so entails negative consequences. In the sense of complete freedom from being determined by causal factors, we believe that there is no such thing as free will, but defined as ability to make choices from among multiple options, even though the choices are ultimately governed by natural processes, addicted individuals are free to choose. Although they might appear unable to exercise this kind of free will in decisions about their substance use, addictive behaviors are ultimately always goal-directed and voluntary. Such goal pursuits manifest considerable flexibility. Even some severely addicted individuals can cease their use when the value of continuing the use abruptly declines or when the subjective cost of continuing the use is too great with respect to the incentives in other areas of their lives. Formal treatment strategies (e.g., contingency management, Systematic Motivational Counseling, cognitive training) can also be used to facilitate this reversal.


Addiction;Cognitive bias;Current concerns;Free will;Incentive value;Motivation;Susceptibility

Free will can be characterized in a number of different ways. According to Baumeisters (2014) definition, resting on that of Haggard, Mele, O'Connor, and Vohs (2010), free will is “the capacity for free action [which means] that the person could do different things in the same situation” (p. 236). This definition is susceptible to varying interpretations. First, one could take “could do” to mean that nothing in the physical environment or range of the persons physical capabilities could prevent the alternative actions and that there are no other constraints. A second interpretation of “could do” might focus on the process of choosing and rule out self-destructive choices that run strongly contrary to the choosers values, such as drawing a gun on police officers (assuming that suicide is not valued positively) or publicly engaging in behavior that would inevitably be viewed by others as offensive, such as violating ones societys strict codes of dress or conduct (assuming that the person positively values social acceptance).

The distinction between these two interpretations is between free will in the sense of choosing according to ones preferences without regard to situational constraints, such as social pressure, versus in the sense of choosing according to ones preferences realistically, that is, in light of the foreseeable consequences, given the situational opportunities and constraints, if any. In other words, the exercise of free will is construed in the present article as choosing from among actions that are feasible given the existing physical limitations or insuperable social constraints. The physical constraints include those imposed by the limitations of brain functions, including brain pathologies ( Fenton & Wiers, 2016) but also by the “balance between impulsive and reflective processes” (Wiers, Field, & Stacy, 2014, p. 39) that may be the result of the addictive substance itself. Otherwise, however, choices are determined by additional causal factors, such as reward sensitivity ( Jonker et al., 2016 ;  van Hemel-Ruiter et al., 2015), executive processes such as goal inhibition ( Goldstein et al., 2001; van Hemel-Ruiter et al., 2015 ;  Wiers et al., 2015b), goal motivation, goal value, and even goal prediction and availability (Volkow, Fowler, & Wang, 2004), memory associations (Ames et al., 2014) for the addictive behavior relative to associations for alternative goals to choose from, and, the alternative goals' values (Kalivas & Volkow, 2005), availability, and levels of automaticity. Ambivalence attributable to conflicts between foreseeable outcomes of a decision, for example between brief strong enjoyment and subsequent loss of an alternative opportunity, may render a decision difficult to make. It is nevertheless made “freely” in the sense of free will employed here.

A third possibility might be to interpret “could do” as making choices free from psychological determinants of action. Such determinants would include genetic determinants of neural and hormonal functions and situational elicitations of behavioral responses that have been previously shaped by environmental influences. This interpretation of free will would, however, deny a role for causality. It would accordingly be inconsistent with scientific understanding of human behavior (see also Baer, Kaufman, & Baumeister, 2008). In this latter sense, free will would not, from a conventionally deterministic scientific viewpoint, exist.

A critic might point to the human capacity for originality, for thinking of creative solutions, even surprising ones, including circumventing seemingly daunting obstacles to a goal pursuit. Having created a solution, the person then continues the goal pursuit in the newly indicated direction. From a scientific perspective, such solutions would result from previously established associative pathways in the brain, perhaps along with quasi-random confluences of stimuli that might lead to experiencing those associations in new ways. That is, the original or creative insights would still have been determined by the flow of prior events. In what sense, then, does this portray freely willed action?

Baumeister (2014) also noted that causality takes place at a variety of levels, from the atomic to the social; that the path from one level to another is often hard to trace; and that there is often a probabilistic factor that defies precise prediction of outcomes. None of these considerations, however, seems determinative regarding the existence of free will. The fact that at this time the relationships between levels are poorly understood cannot demonstrate the existence of free will. Neither can the existence of unexplainable probabilistic variation, inasmuch as will presumably directs behavior stably at particular outcomes that have certain properties of value and probability of attainability, as described briefly in a subsequent section. Does it make sense to equate “free” with irreducibly unexplainable?

In considering the role of free will in addiction, it is necessary to make some important distinctions. One of these is between free will and conscious choice, or conscious will, as Wegner (2002) puts it. It is entirely possible to view the causal chain that leads to actions, such as imbibing addictive substances, as either determined by concrete, predictive precursors or as freely chosen, unpredictable courses of action, without committing to a position regarding the role of consciousness. Therefore, the very useful discussions regarding the causal role of consciousness in choice or will (e.g., Baumeister et al., 2011 ;  Baumeister et al., 2015) are not necessarily relevant to the consideration of free will.

It should also be noted, however, that individuals' preferences to engage in one behavior versus another will have been shaped by their genetic endowment in interaction with their past experiences. As discussed in the next section, scientific advances now permit specifying physiological, psychological, and cultural differences among individuals, which together shape their preferences regarding their use of addictive substances. Whether one wishes to characterize the resulting choices as truly “free” depends on how one understands such freedom. Yes, people generally have a number of possible choices before them, in which case their choices are made freely, but in our view their ultimate choices are formed by the various physical and experiential determinants that we later describe.

In any event, to say that their choices have been predetermined is in no way inconsistent with the view that people can think critically and can often devise creative, even surprising solutions for reaching their goals. After all, individuals' patterns of thought, including their associative pathways that affect their creative solutions, rest on a foundation of brain functions and previous experiences that enable them to envision and map out their futures.

To obtain a sense of how free will is experienced in everyday life, Stillman, Baumeister, and Mele (2011) asked undergraduate student volunteers to describe instances in which their actions reflected the exercise of free will and other instances in which free will was not a factor, as, for instance, when the action was perceived as completed under duress. Raters then assessed the narratives with regard to a set of dimensions. The characteristic that best distinguished the two classes of action was described as goal attainment, which was rated much higher in narratives of freely chosen actions than in narratives of more constrained actions. This finding meshes nicely with the goal theory of current concerns (e.g., Klinger & Cox, 2011), which avoids pondering the concept of freedom of will but recognizes the centrality of goal pursuits in the brains architecture and more broadly in the capacity for survival of members of the animal kingdom.

The sections that follow explore what is known about the motivational factors and constraints regarding alcohol use. It does so in order to assess the extent to which excessive alcohol consumption can be considered a free choice versus one over which the drinker lacks control.

1. The value of drinking alcohol

Cox and Klinger, 1988; Cox and Klinger, 1990; Cox and Klinger, 2004 ;  Cox and Klinger, 2011a have proposed a motivational model of alcohol use. The model summarizes the major variables that contribute to—or detract from—the value of drinking alcohol, and it shows how these variables pass through a motivational track that culminates in a persons decision to drink—or not to drink—alcohol. The decision to drink, or not to do so, is voluntary in the sense that the drinker can exercise control over it. Nevertheless, the nature of the control is itself predictable and determined. That is, for example, insofar as variables that increase the value of drinking alcohol apply to a particular individual, weight will be added to that persons decisions to drink alcohol as opposed to not doing so. Conversely, if variables that detract from the value of drinking alcohol apply to an individual, weight will be added to this persons decisions not to drink.

It is important to clarify how value is defined in the science of motivation. Incentives are the objects, events, or situations that have positive or negative value, in the sense that a person would like to get, obtain, or retain them (in the case of positive incentives), or the person would like to prevent, avoid, or be free of them (in the case of negative incentives). The value attributed to an incentive is, therefore, the expectation that a desirable change in the persons feeling—i.e., his or her affect—will occur if he or she acquires a positive incentive or gets rid of a negative incentive. Drinking alcohol might be a positive incentive for one person; it might be a negative incentive for another person; or it more likely might have both positive and negative value for the same individual.

In the following sections, we discuss the major categories of variables that contribute to the positive or negative value of drinking alcohol. These variables include biological, sociocultural, environmental, and psychological factors. When, as a result of the influence of these variables, a person highly values drinking alcohol, there is a strong likelihood that he or she will choose to drink. In our view, this person will have freely (in the limited sense described above) decided to drink, even though the decision might be neither prudent nor healthy.

On a terminological note, it is important to recognize that the term addiction is descriptive and predictive but not explanatory. It labels a likelihood of repeated behaviors of a certain kind, typically behaviors disapproved of by the user of the term and most likely carrying serious disadvantages for the addicted individual, at least in the view of the person using the term addicted. The addictive behavior is commonly also ascribed to the addicts inability to cease use, but addiction reflects likelihood of choices, not lack of capability to act.

2. Drinking alcohol tends to run in families

It has long been recognized that excessive drinking and alcohol problems tend to run in families (Orford, 1984). However, the similarity in drinking patterns among family members could be due to (a) various environmental factors within the family context (Orford, 1984) that help to define the value of drinking for a particular family, (b) genetic factors that help to determine the extent to which drinking alcohol is pleasurable or aversive, or (c) the interaction between environmental and genetic factors. In an effort to separate the relative contribution of environmental and genetic factors to the development of alcohol use disorders (AUDs), studies have compared the prevalence of AUDs in monozygotic and dizygotic twins who were reared by their biological parents with those who were reared by adoptive parents. In a meta-analysis of these studies, Verhulst, Neale, and Kendler (2015) concluded that the heritability of AUDs is substantially greater than the proportion of shared environmental variance (0.49 versus 0.10).

But what is transmitted genetically that could cause an AUD to develop? Efforts are under way to identify the specific variations on the human genome that contribute to the genetic risk for AUDs (e.g., Juraeva et al., 2015; Yan et al., 2014 ;  Ystrom et al., 2014). Although progress has been made, the exact determinants of genetic risk for AUDs are not yet fully understood. Nevertheless, some conclusions can be drawn. For instance, the degree to which alcohol affects the neurotransmitters in the brain seems to be partly genetically determined (Tabakoff & Hoffman, 2013). For some individuals, alcohol might strongly reduce anxiety through the release of gamma-aminobutyric acid (GABA). For other people, drinking alcohol might strongly enhance reward-related stimuli through the release of dopamine (Berridge, 2007). Another reaction to alcohol that is likely genetically determined is the alcohol flush reaction (Dickson, James, Heath, et al., 2006). It includes a variety of unpleasant bodily reactions in people with a deficiency in aldehyde dehydrogenase—the enzyme that metabolizes acetaldehyde (the toxic first metabolite of alcohol)—when they drink alcohol. The alcohol flush reaction seems to be genetically determined in that deficits in aldehyde dehydrogenase are common among people of East Asian ancestry, unlike those of European ancestry (Dickson et al., 2006).

The various biochemical reactions to alcohol described here affect the value that individual drinkers place on drinking alcohol, either increasing or decreasing the degree of reward that a person derives from drinking alcohol. Again, when an individual attributes great value to drinking alcohol, that person will likely choose to drink. It is a decision that is freely made (in the limited sense described above) on the basis of the payoff that this person expects to derive from drinking.

3. Sociocultural and environmental influences

The value that a person attributes to drinking alcohol is also strongly influenced by the society in which the person lives (Greenfield and Room, 1997; Orford, 2001; Room, 2013 ;  Wild, 2002). For instance, the society helps to define the circumstances in which it is appropriate to drink and how much alcohol should be drunk. Individuals within a society are explicitly and implicitly reinforced when their drinking corresponds to the societal norms, and they are explicitly and implicitly censured when their own drinking deviates from societal norms. These societal reactions to an individuals drinking help to solidify the value that each person places on drinking alcohol.

The differences in societal views about drinking are reflected in the patterns of alcohol consumption within particular countries. For example, an international study of heavy drinking among university students (Dantzer, Wardle, Fuller, Pampalone, & Steptoe, 2006) found that university students in some southern European countries (e.g., Italy, France, Greece) had low rates of heavy episodic drinking compared to students in some of the northern European countries (viz., Belgium, Ireland, Poland, Netherlands, Slovakia) and in North America (e.g., United States) and South America (e.g., Columbia). There were, however, exceptions to the general conclusion about the difference between northern and southern European countries in the rates of heavy drinking. For example, students in Germany, like those in Italy, had a low rate. Similar to the results of other studies, in school-based surveys from 13 European countries, Kuntsche et al. (2015) found that adolescents from southern and central Europe drank more frequently than those from northern Europe, but adolescents from northern European countries reported being intoxicated more frequently. The cultural differences in drinking seemed to be due to stronger social (e.g., “because it helps you enjoy a party”), enhancement (e.g., “because you like the feeling”), and coping (e.g., “because it helps you when you feel depressed or nervous”) motives for drinking in northern Europe than in southern and central Europe.

In addition to cultural attitudes about drinking within a society, environmental influences also affect the value that individuals place on drinking alcohol. Taxes and advertisements of alcohol are two such influences. For instance, it has been demonstrated that within particular countries, increases in taxes on alcohol lead to reductions in alcohol consumption generally and reductions specifically in binge and other kinds of excessive drinking that carry negative health-related consequences (Staras et al., 2014 ;  Xuan et al., 2015). Presumably, raising taxes on alcoholic beverages reduces money that might otherwise be used to purchase other positively valued goods and services or requiring additional negatively valued work to purchase them, thus increasing drinkers' ambivalence about drinking. Reductions in taxes, on the other hand, are associated with increases in consumption and consequent increases in alcohol-related morbidity and mortality (Zatoński, Sulkowska, Zatoński, Herbeć, & Muszyńska, 2015). In other words, tax increases lower the net value of drinking alcohol, thereby making it more likely that individuals will control their drinking; reductions in taxes have the opposite effect.

The goal of alcohol advertisements is, of course, to increase the attractiveness of drinking alcohol (i.e., its value), thereby increasing the amount of alcohol that people consume. Various research studies have clearly demonstrated that there is, in fact, a direct relationship between the amount of alcohol that people consume and their exposure to alcohol advertisements. This relationship, for example, has been demonstrated among underage drinkers with regard both to the amount of alcohol and the particular brands that they consume (Morgenstern et al., 2011; Ross et al., 2014 ;  Ross et al., 2015; Siegel et al. cited by Barry [2], 2015; Siegel et al., 2016), and it has also been shown that alcohol advertisements influence young peoples perceptions of the normativeness of drinking (Martino et al., 2016). Conversely, restrictions on alcohol advertisements have been found to be inversely associated with the prevalence of hazardous drinking (Bosque-Prous et al., 2014).

Any of these sociocultural and environmental influences that add weight to the positive value that a person expects to derive from drinking alcohol will make it more likely that the person will actually decide to drink. Again, however, we emphasize that the decision is made voluntarily in the sense that nothing in the environment coerces it (Cox and Klinger, 1988; Cox and Klinger, 1990; Cox and Klinger, 2004 ;  Cox and Klinger, 2011a. This view is consistent with the view that addiction is a special case of—rather than an exception to—normal motivational processes (Köpetz et al., 2013; Lewis, 2017 ;  Orford, 2001), even in the case that the resulting behavior appears to be self-destructive (Köpetz et al., 2013).

4. Personality and other psychological factors

A personality characteristic that has often been identified as a risk factor for alcohol and other substance misuse is behavioral disinhibition (Bogg & Finn, 2010)—particularly in the form of impulsivity (Harnett et al., 2013; Loxton et al., 2015; Mackinnon et al., 2014; Mikheeva and Tragesser, 2016; Thompson et al., 2015 ;  Wood et al., 2013). Another such characteristic is negative emotionality (Mackinnon et al., 2014 ;  Mikheeva and Tragesser, 2016). Impulsive individuals value drinking alcohol for the enhancement that it brings; depressed and anxious individuals value drinking because they perceive that it will alleviate their negative emotions.

Another factor that determines the value that drinking alcohol has for a particular individual is the other incentives in the persons life that are available, or potentially available, for the person to pursue and enjoy. Excessive drinkers, including those who develop an AUD, seem explicitly or implicitly to compare the value that they attribute to drinking alcohol with the value that they can derive from incentives in other areas of their lives (Cox & Klinger, 2011a). The chemical effects that drinkers seek to achieve from drinking alcohol occur rapidly, and the satisfaction that could be achieved from more distant goals might seem relatively unattractive, especially to drinkers who exhibit delay discounting, i.e., those who disproportionately devalue delayed rewards (Moody, Franck, Hatz, & Bickel, 2016). In fact, it has been demonstrated that as perceived access to other incentives decreases, peoples motivation to drink alcohol increases (e.g., Vuchinich and Tucker, 1996 ;  Vuchinich and Tucker, 1998). Additionally, it has been shown that problematic drinkers are more likely to resolve their drinking problem if they have a satisfying life and adequate resources for coping with frustrations (Cox et al., 2002; Moos and Moos, 2007; Murphy et al., 2005; Tucker et al., 2009 ;  Tucker et al., 2006). In short, drinkers who previously had a drinking problem have been able to resolve it if they perceive that that they have access to sufficiently compelling competing incentives. Their decision to give up drinking would have been made in the same voluntary manner as their earlier decisions to imbibe. Specifically, it would have been made when drinkers came to view the downside of drinking alcohol as outweighing the value of continuing to drink. Such a decision might be made even among people who are severely addicted.

5. Are addictive behaviors motivated or automatic? A false dichotomy1

One property frequently attributed to addiction is automaticity. That is, the behavior, such as alcohol consumption, follows uncontrollably from given circumstances. That property would appear to abrogate free will. What follows examines this idea as it applies to alcohol use.

To begin with, addictive behaviors are malleable with regard to both their forms and probability of occurrence. They are, like other behaviors beyond simple reflexes, goal-directed, and voluntary (see also, e.g., Heyman, 2009). That is, addicted individuals can voluntarily modify their instrumental acts to accord with what is needed to obtain addictive substances in particular circumstances. Notwithstanding contrary opinions in the field that addictive behaviors, or at least some of their components, are automatic and beyond voluntary control, the evidence indicates that they are modifiable.

Automatic suggests that under given conditions a response has a high probability of occurring. It may also include acts that originate through unconscious processes ( Huang & Bargh, 2014). Some authors (e.g., Feil et al., 2010) use the term “compulsive” to describe addictive instrumental (e.g., “seeking”) and consummatory (e.g., “taking”; Vanderschuren & Everitt, 2005) actions.

Such terms imply mechanical rigidity, but the literature indicates that behaviors associated with addiction are nevertheless modifiable. For example, automaticity is perhaps most often attributed to attentional biases. Yet such alcohol-related behaviors, though viewed as automatic action tendencies, can be changed through cognitive bias modification ( Cox et al., 2015 ;  Wiers et al., 2011) and mindfulness treatment (e.g., Witkiewitz, Lustyk, & Bowen, 2013). Evidence also indicates that most people who change their substance use do so on their own without formal treatment (e.g., Bischof, Rumpf, & John, 2012).

This is not to deny that ending or even moderating substance use may be difficult, often excruciatingly so. There is evidence of brain properties associated with addiction that may deepen the difficulty (Holton and Berridge, 2013 ;  Koob, 2006). Repeated consumption of the addictive substance appears to shift value from consummation to procurement of the addictive substance and to reduce the subjective value of more natural kinds of rewards, such as food and social interaction (Kalivas & Volkow, 2005).

Nevertheless, in other respects addictions result from processes of the kind that produce strivings for high-value goals in general. That they are so highly valued gives at least the persistence and intensity of these goal pursuits the appearance of automaticity. However, goal-directedness is particularly characterized by responding to obstacles by altering ones responses, whether by increasing sheer forcefulness or trying novel approaches to circumvent the obstacles. Pioneer researcher Wolfgang Köhler (1925) labeled this response flexibility Umweg (detour) behavior.

The goal theory of current concerns, from its first formulations, recognized this principle by labeling the first stage following an encounter with an obstacle the invigoration stage (Klinger, 1975 ;  Klinger, 1977). When in need, addicts regularly display such detour behavior in the sometimes ingenious ways they find for obtaining or concealing their substances, such as diverting them from hospital stores and patients.

One route to apparent automaticity is repetition of goal-directed behavioral sequences. Frequently repeating sequences of initially separate actions integrates their action units into longer, relatively seamless action streams that require little conscious control to unfold, as in skilled playing of a musical instrument. However, opposing this kind of automatization (i.e., response integration, Klinger, 1971) to goal-directedness creates a false dichotomy. Even integrated action sequences are subject to modification according to circumstances, as when a violinist in a public performance voluntarily changes fingering to compensate for a suddenly broken string. Of course, such flexibility depends on a degree of practice and knowledge, but the point here is that the behavior is ruled by the desire to optimize attainment of goals and is not simply mechanical.

Another general principle is that having chosen to pursue a goal alters cognitive processing, beginning with changes in the individuals priorities for processing stimuli (Klinger, 1978 ;  Klinger, 2013). People become more attentive to all kinds of goal-related cues and are more likely to recall them and to think about them spontaneously. Consistent with this principle, alcohol addicts give processing priority to alcohol-related cues (Cox, Fadardi, & Pothos, 2006). These in turn further whet appetites for alcohol consumption (Cox et al., 2006; Field and Cox, 2008; Field et al., 2009a ;  Field et al., 2009b). A similar process operates with regard to food. Importantly from the standpoint of modifiability, interfering with goal-related imagery reduces consumption (May, Kavanagh, & Andrade, 2015). Addicts also display heightened delay discounting, in which rewards of ostensibly equal general value for the individual are treated as if more valuable the sooner that they can be obtained. In the case of alcohol use, delay of potentially rewarding alternative activities thus discounts their value (see Cox et al., 2002 ;  Noël et al., 2010).

The theory presented here shares some features with dual-process models of addiction (e.g., Moss and Albery, 2009 ;  Wiers et al., 2006) in which automatic processes such as addiction-related attentional bias and controlled processes (deliberate responses) are both involved in addictive behaviors. Although both kinds of processes play a role in decisions about using an addictive substance, the decision, however conflicted and difficult, is in our view always ultimately under the persons voluntary control and remains goal-directed. The evidence suggests only that the drug-seeking behavior can become repetitive. Seeking a target (i.e., a goal object or event) such as an addictive substance remains under conscious control even if it is a habitual, seemingly automatized behavioral sequence triggered by internal or external cues.

As we have argued, addiction reflects a major shift in the relative incentive values of the substances that have attained the status of an individuals goal objects, both substance-related and other ones. In accordance with Expectancy X Value decision theory (Van Eerde & Thierry, 1996), this shift changes decisions and renders it more difficult (i.e., less likely) for a person to stop pursuing the addictive goal. As incentives and goals change in potency and nature (e.g., Kalivas & Volkow, 2005), the direction of decision-making also changes. Nevertheless, although values affect voluntary choices, the addictive behaviors are directly controlled by those voluntary choices rather than being purely pharmacologically, quasi-robotically controlled acts (see also Heyman, 1996; Heyman, 2013 ;  Russell and Davies, 2009). It is of interest that the extent to which interviewed drug users subsequently reduce their use is predicted by the extent to which they express commitment to such change in their interviews (Amrhein, Miller, Yahne, Palmer, & Fulcher, 2003).

Consistent with this view, even severely addicted individuals may undergo natural remission when further changes in values and expectancies produce the decision that the benefits of changing their addictive behavior outweigh the emotional costs of continuing the consumption of the addictive substance (Bischof et al., 2012). Addictive behaviors can, therefore, also succumb to the behavior-shaping administration of rewards and punishments as, for example, by applying contingency management treatments (Stitzer & Petry, 2006). Another approach that works on modifying the value and expectancy components of goal choices, Systematic Motivational Counseling (Cox & Klinger, 2011b), has also been found to generate changes in patterns of use (Cox et al., 2003 ;  Fuhrmann et al., 2011). The section that follows reviews the research on addiction-related cognitive biases and their modification, with resulting changes in consumption of substances.

6. Cognitive automaticity and free choice

The previous sections discussed how choices are intertwined with goal setting, goal management, and goal pursuits. Usually, even if people wish to, they cannot simply copy and paste ways of doing things. It is, however, also true that the majority of the daily activities that people undertake, even those involving preferences for food, art, sports, movies, and so on, follow an algorithm that has been established in the brain. The more often one repeats the behavior, the more established it becomes. This section discusses selective evidence on automatic cognitive processes that are not under conscious control and how they are related to decision-making and goal-seeking behaviors. The evidence described here challenges peoples desire to implement their free will, especially with regard to well-established addictive behaviors, which entail strong emotions.

The emotionality that is associated with goal pursuits (referred to as value) has a great impact on the cognitive system, and the relationship is reciprocal. In fact, theories and research suggest that sometimes decisions are totally intuitive or based on nonconscious inner (visceral) desires (see Dunn, Evans, Makarova, White, & Clark, 2012; M. J. Robinson, Fischer, Ahuja, Lesser, & Maniates, 2016).

With reference to one variety of apparent automatization, T. E. Robinson and colleagues (Robinson and Berridge, 1993; Robinson and Berridge, 2000; Robinson and Berridge, 2001 ;  Robinson and Berridge, 2008) explained how repeating drug-use behaviors leads to alterations in the brains reward circuits. They coined the term incentive sensitization to refer to these goal-specific brain pathways. M. J. Robinson et al. (2016) also argued that when incentive sensitization develops into wanting an object, i.e., experiencing a visceral desire for it (i.e., a nonconscious, inner desire), this initiates a behavioral chain of goal pursuits even in the absence of liking the object of the goal. Such visceral wanting can be triggered by sheer exposure to cues that are associated with the goal, which in the case of addictive behaviors might be stimuli related to alcohol, drugs, or forbidden food. This view is consistent with Tiffanys (1999) conclusion from reviewing cue-reactivity studies that cues can actually elicit visceral reactions, and that conscious feelings of craving are not a necessary part of substance use or of relapse ( Wray, Gass, & Tiffany, 2013).

Executive cognitive functioning (ECF) is also important in emotion regulation and goal-related behaviors. Within ECF, two subsystems have been identified, each with distinctive, but to some extent overlapping, neural substrates that are involved in decision-making and motivated behavior. One is a so-called hot cognitive system, which is responsible for regulating affect, emotions, and behavior that is motivated toward achieving a valued goal (i.e., an incentive). The other is a so-called cold cognitive system, which is responsible for regulating less affective, abstract, and non-contextual tasks and problem solving ( Dolcos and McCarthy, 2006; Hongwanishkul et al., 2005 ;  Iordan et al., 2013).

There is evidence that overlapping brain regions are involved in both of these systems, but hot ECF has been found to be more strongly associated with the orbitofrontal cortex and ventromedial regions, both of which are connected to limbic areas that are associated with emotional and social processing. Cold ECF, on the other hand, is more strongly associated with dorsolateral prefrontal areas (Tsermentseli & Poland, 2016). There is evidence from neurocognitive research that people with a history of alcohol or other substance abuse have poorer cognitive and brain functioning related to both hot and cool ECF (Moreno-Lopez et al., 2012). Insofar as this suggests a possible causal connection, it indicates that alcohol and other drug abuse may impair the brains loci that are responsible for a wide range of ECF, including inhibition, task switching, sustained attention, decision making, and planning (de la Monte and Kril, 2014; Smith et al., 2014; Terrett et al., 2014 ;  Vonmoos et al., 2014). Such impairment may reduce the individuals ability to voluntarily inhibit or deflect particular goal pursuits.

Surprisingly, evidence suggests that the adverse effects of chemicals on the brains executive functions can be traced to embryonic life. In one study, six-to-nine-year-old children whose mothers smoked during their pregnancy showed poorer performance on a task measuring hot ECF than children whose mothers were also smokers but who refrained from smoking during their pregnancy (Huijbregts, Warren, de Sonneville, & Swaab-Barneveld, 2008). These authors also observed a dose–response relationship in the childrens performance. However, as far as impulsivity and disinhibition are concerned, a genetic factor could also be implicated in view of the fact that the mothers of children with better ECF were able to successfully refrain from smoking during their pregnancy.

It is interesting that anticipation of goal-related outcomes is mainly processed in amygdala in interaction with orbital prefrontal and anterior cingulate cortex. Moreover, the interactions among these structures affect ones cue reactivity, attentional bias, and response selections (Baxter et al., 2000 ;  Murray, 2007). Therefore, a mixture of habit development and adverse effects of the abused chemicals on the brain contribute subtly to cognitive processes that underlie making decisions about whether to consume an addictive substance. Such nonconscious cognitive processes have been classified into two major types of biases: (a) attentional bias, and (b) interpretive bias Both of these can be construed as a kind of automatization. However, as indicated later, this does not necessarily indicate that they are unmodifiable.

7. Attentional bias

Attentional bias for substance-related stimuli is ones tendency to readily perceive and attend to stimuli in the environment that are related to the persons substance use. It also can be construed as an automatized phenomenon, but, again, it turns out to be malleable. Furthermore, it parallels, and can be considered a particular instance of, attentional bias toward cues associated with any significant personal goal (Cox et al., 2006).

Attentional bias tasks are based on either facilitation or interference effects. Facilitation refers to easier detection of salient stimuli (e.g., a bottle of wine) among other kinds of stimuli (e.g., a bottle of a nonalcoholic beverage). Interference, by contrast, refers to difficulty in turning ones attention away from a salient stimulus (e.g., a bottle of wine) and paying attention to another kind of stimulus. For instance, in the emotional Stroop task, the participants task is to ignore the meaning of a series of words (e.g., wine, table) and respond as quickly and accurately as possible to the color (e.g., red, blue, green, yellow) in which each word is presented. Each participants interference score is calculated by subtracting the mean reaction time for naming the color of the neutral words (e.g., table) from the mean reaction time for naming the color of salient words (e.g., wine). Presumably, the semantic meaning of the salient word takes processing priority over the focus prescribed by the task, the color of the words font, thus delaying identification of the color. The interference score is a measure of the persons attentional bias for the salient category (e.g., alcohol).

Studies suggest that indicants of individuals' cold ECF, such as impulsivity or poor inhibitory control, are correlated with their performance on attentional bias tasks (e.g., Liu et al., 2011). Therefore, it is important to establish whether (a) longer response times on measures of attentional bias can be attributed to the substance-related characteristics of the stimuli, or (b) longer response times simply result from participants' impaired ECF. Fadardi and Cox (2006) used a regression model to control for alcohol abusers' impaired ECF and concluded that the attentional bias for alcohol-related stimuli was not an artifact of the abusers' poorer cognitive ability. In a study testing the relationship between drinking motives and drinking-related interpretation biases, Woud, Becker, Rinck, and Salemink (2015) reported that ECF did not mediate the relationship between drinking expectancies and drinking-related interpretation biases.

Using various measurement techniques, such as the visual probe (Field et al., 2007 ;  Schoenmakers et al., 2007), flicker (Jones et al., 2006; Jones et al., 2003 ;  Schoenmakers et al., 2007), dual-task (Waters & Green, 2003), and Stroop (Cox et al., 2006) paradigms; eye-tracking technology (Friese, Bargas-Avila, Hofmann, & Wiers, 2010); and functional magnetic resonance imaging (fMRI; Vollstadt-Klein et al., 2012), researchers have documented attentional bias across a wide range of addictive behaviors, including alcohol abuse ( Fadardi, 2003; Fadardi and Cox, 2008; Roy-Charland et al., 2016; Sharma et al., 2001; Vollstadt-Klein et al., 2012; Wilcockson and Pothos, 2015 ;  Zetteler et al., 2006), heroin addiction ( Bearre et al., 2007 ;  Waters et al., 2012), cocaine addiction ( Castillo et al., 2010; Copersino et al., 2004; Hester et al., 2006; Kilts et al., 2014; Sokhadze et al., 2008 ;  Waters et al., 2012), marijuana use ( Cousijn et al., 2013; Field, 2005; van Hemel-Ruiter et al., 2016 ;  Vujanovic et al., 2016), and obesity ( Doolan et al., 2015; Fadardi and Bazzaz, 2011; Freijy et al., 2014; Lattimore and Mead, 2015; Nathan et al., 2012; Schmitz et al., 2014 ;  Werthmann et al., 2015).

In an fMRI study, Ihssen, Cox, Wiggett, Fadardi, and Linden (2011) exposed heavy drinkers to alcohol-related pictures and to pictures related to healthy goal pursuits. They found that the parts of the brain that are involved in emotional processing (i.e., insular cortex) and reward circuitry (i.e., ventral striatum) showed greater activation in heavy drinkers than light drinkers during exposure to the alcohol-related pictures. When presented with the stimuli related to higher-order goals (such as those related to family, health, and finances), the heavy drinkers showed weaker responses in frontal areas than the light drinkers.

What does increased activity in certain loci of the brain in response to addiction-related stimuli indicate? Ihssen et al.s (2011) findings suggest that heavy drinkers have difficulty forming healthy goals as an alternative to drinking alcohol. For them, alcohol serves as a major source of emotional regulation, which means an increased likelihood of relapse after they attempt to control their drinking. Franken (2003) argued that when habitual substance users encounter a conditioned drug stimulus, their brains dopamine level in the corticostriatal circuit increases, particularly in the anterior cingulate gyrus, amygdala, and nucleus accumbens. The increase serves two purposes: (a) it draws the persons attention toward a drug-related stimulus, and (b) it results in motor preparation and hyper-attentiveness to drug-related stimuli. Together, the activation promotes substance craving and perhaps relapse. In fact, several studies have concluded that drug-related attentional bias predicts post-treatment relapse among drug-abusers (Field and Cox, 2008; Goldstein et al., 2007 ;  Marissen et al., 2006).

Evidence on the relationship between attentional bias and substance abuse and relapse suggests that ones willpower proportionately suffers from the strength of ones attentional bias for substance-related stimuli. However, despite extensive literature discussing the importance of attentional bias and its relationship to continued substance use and relapse (Field and Cox, 2008 ;  Field et al., 2009b), the evidence for this conclusion is not entirely consistent (Field, Marhe, & Franken, 2014). It should be noted that such results are based on group averages; usually the mean score of one group (e.g., heavy drinkers) was compared with the mean score of another group. In other words, it is not possible to predict whether individual substance users will show an attentional bias, and, even if they do, there is no guarantee that their outcome will be determined by their degree of attentional bias (e.g., Flaudias et al., 2013 ;  van Hemel-Ruiter et al., 2016). Attentional bias apparently interacts with a variety of circumstantial variables in its relation to subsequent variables, such as substance use (Field et al., 2016). Nevertheless, studies have shown that attentional bias training can help substance users reduce their attentional bias for addiction-related stimuli (Attwood et al., 2008; Bazzaz et al., 2016; Cox et al., 2015; Field et al., 2009a; Hester et al., 2006; Wiers et al., 2015a; Wiers et al., 2015b ;  Ziaee et al., 2016).

To conclude, attentional bias can exacerbate an addiction; however, there are ways to reduce its effects, the simplest of which is to avoid situations and stimuli that are related to the substance use. Another way is to practice overcoming the attentional bias using one of the attention retraining programs that have been specifically developed for this purpose. There is no study to suggest that substance-related attentional bias can excuse substance users' inability to control their harmful behavior. As, however, we show in the subsequent sections, the contribution of nonconscious processes to addictive behaviors is not limited to substance-related attentional bias.

8. Interpretation bias

“When you were talking, he started…” How could one complete this sentence? Would one say something such as “laughing at you” or perhaps “looking at you”? What about these sentences: “When we want to hang out, we go to a supermarket to buy…” “It was late, and she came home…”? Peoples tendency to immediately appraise an ambiguous situation in a given way (whether positive or negative) constitutes their interpretation bias, but an interpretation bias does not normally reflect reality (Butler & Mathews, 1983). Like attentional bias, it also can be modified; this means that it is not completely automatized.

Studying implicit substance-related interpretation biases relies on implicit paradigms such as word association tasks (e.g., Stacy, Ames, & Grenard, 2006). Single ambiguous words can be used to study implicit associations, whereby participants are asked to report their first spontaneous response to a given ambiguous cue (e.g., “bar”); alternatively, ambiguous phrases containing affective components (e.g., “What a spirit!”) or open-ended ambiguous scenarios can be used ( Woud et al., 2012; Woud et al., 2015a; Woud et al., 2014 ;  Woud et al., 2015b). The common finding from this line of research is that people with more alcohol-related associations tend to drink more heavily than those with fewer alcohol-related associations. It seems that the facilitation effect in alcohol-related associations is a product of both ones history of procuring alcohol and an eliciting factor that promotes craving and future alcohol use ( Woud et al., 2015b ;  Woud et al., 2015a).

Actually, there have been only a few studies that have tested the relationship between interpretation biases and alcohol or other drug use. Woud et al. (2012) reported that, compared to light drinkers, excessive drinkers generated more alcohol-related completions of the sentences. In Woud et al.s (2014) study, alcohol-dependent patients showed greater alcohol-related interpretation bias and extended the bias to emotionally vague contexts (i.e., potentially related to fear or depression) than did control patients who had either a clinically diagnosed mood disorder or an anxiety disorder. Woud et al., 2015a ;  Woud et al., 2015b found that drinkers' motives for reducing their negative affect (i.e., coping motives) were the sole predictor of their interpretation bias in favor of drinking alcohol when they were exposed to an ambiguous negative context. An example of such a scenario is: “Your study loan piles up, you failed an exam, and now you lost your wallet. You just want to forget all your problems.” Two choices were provided for overcoming the problems: drinking alcohol or going for a long walk. The participants' bias toward drinking alcohol was a significant predictor of their future drinking.

In summary, excessive drinkers tend more often than light drinkers to interpret ambiguous, potentially alcohol-related scenarios in an alcohol-related manner. And there is evidence with alcohol abusers (Woud, Hutschemaekers, Rinck, & Becker, 2015) that a single session of Cognitive Bias Modification-Interpretation (CBM-I) can produce changes in alcohol-related interpretation biases. Motivated by the strong evidence for a relationship between negative affect, frustration intolerance, and substance use (Bitsakou et al., 2006; Ko et al., 2008; Kraemer et al., 2015 ;  McHugh and Otto, 2012), Samifard, Fadardi, and Shamloo (2016) compared drug-users in a treatment program with non-abusers on explicit and implicit measures of frustration intolerance. On both measures, the drug users scored higher than the non-users. Examples of the scenarios that were used in the study are as follows: “Occasionally, you join your friends for walking in the wilderness. In the past, sometimes you have used drugs with your friends. You are on your way to meet your friends for the hike. On your way, you think of…” A negative target interpretation was “On your way, you think of past experiences and want to use drugs”. A positive target interpretation was “On your way, you think of your bad experiences with drugs, and you decide to stop using them”.

Next, drug-users who scored high on the temptation to use drugs and low on frustration tolerance were randomly allocated to a sham intervention or to an experimental group that received three sessions of CBM-I in order to alter their frustration-related negative bias. At post-test and a one-month follow-up, participants again completed the baseline measures. The results indicated that, compared to the sham intervention group, the experimental group showed increases in positive, drug-unrelated interpretations of ambiguous scenarios and in frustration tolerance, which were accompanied by reductions in the temptation to use drugs. The findings suggest that the use of CBM-I to promote positive interpretations can have clinical implications in the treatment of drug use. In other words, cognitive bias modification programs are a means of helping substance users to facilitate their motivation to control their harmful habit; however, making such a decision is a matter of their own choice.

9. Conclusions regarding cognitive automaticity and free choice

Due not only to the adverse effects of substance use but also to the development of various types of cognitive biases, a substance abusers brain suffers from a distortion in the flow of higher-order cognitive processes, including concentration and working memory, which might reduce the persons ability to exercise reflective processes over impulsive processes, hence disrupting controlled patterns of substance use. When executive cognitive functions that are responsible for reflective processes are impaired, the effects of various types of cognitive biases on drinking-related decisions can be multiplied. A cognitive bias, therefore, can lead a substance user to make decisions in favor of substance use despite the persons conscious vows to refrain from the use. Factors that strengthen implicit processes contribute to the persons choosing immediate goals over more long-term goals (Lewis, 2011).

Although implicit processes, such as those described here, can outweigh the impact of reflective processes in substance-related decisions and behaviors, substance use is not necessarily ruled predominantly by implicit processes. In fact, there is a general consensus among addiction researchers that if an individual has the necessary motivation and cognitive resources, the impact of implicit processes can be regulated (Becker et al., 2016; Belackova et al., 2015; Petry, 2005; Woud et al., 2015b ;  Woud et al., 2015a). The resulting decisions would exemplify the exercise of free will as this article defines it.

10. Conclusions

We take the view that the natural course of events entails causes and effects. Although, within the present state of our knowledge, there will be some unpredictable, seemingly random variation in behavior, this random variation cannot be meaningfully equated with human free will. People can, however, make choices from among an assortment of possibilities that are unconstrained by physical limits or significant social pressures. Making a choice from among such options might, in this limited sense, be viewed as having been made freely, even though the choice was predictable.

Within the present theory, the value component of decision-making depends on immediate or anticipated affective responses to the alternatives, regardless of whether these are to anticipated physical pleasures, to the conditioned inner consequences of obeying or violating moral principles, or to some other source of affective arousal. Various kinds of brain impairments, psychopathologies, and personality traits such as impulsiveness also affect decision processes. Repeated substance use itself risks physical, emotional, and social changes that may weight decision processes against cessation of use, even when the addict recognizes that cessation would carry benefits, although possibly just not enough to outweigh the experiential benefits of continued use. The present review of choices that are available to an individual about whether to use an addictive substance makes it clear that once a person has become addicted, ceasing to use the substance constitutes an often extremely difficult and improbable choice; nevertheless, cessation of the use is not impossible. The present review also makes it clear that the behaviors entailed in obtaining and consuming an addictive substance cannot be construed as entirely automatic or quasi-mechanical, in that they are to some extent flexible and sometimes even creative. Furthermore, the evidence indicates that the components involved in seeking an addictive substance and wanting to use it can at least to some extent be modified by appropriate treatment techniques.

The component that is most difficult to change is the goal to use the addictive substance, once it has become firmly established. It, in turn, guides the motivational, cognitive, and emotional components of the subsequent substance-seeking behavior. Despite their recalcitrance, addiction-related goals can be changed, or at least suppressed and superceded by other goals, often without any formal treatment but also within an empirically validated treatment program. In this limited sense, people indeed are free to choose whether or not to use an addictive substance.


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1. For an extended discussion of this topic, see Cox, Klinger, and Fadardi (2017).

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