Cognitive Bias Modification and Cognitive Control Training in Addiction and Related Psychopathology

Stronger impulsive processes as a result of substance use

First, psychoactive substances affect mechanisms that underlie impulsive processes. In the current neurobiological literature on addiction, at least three of these pharmacologically enhanced forms of learning (neuroadaptations) are described that occur in different stages of substance use and abuse: incentive sensitization, negative reinforcement, and habit formation (Everitt & Robbins, 2005; Koob & Volkow, 2010; Robinson & Berridge, 2003). We briefly describe these mechanisms here, noting that although the underlying mechanisms are different, they all result in stronger impulsive processes geared toward substance use.

In their incentive sensitization theory of addiction, Robinson and Berridge (1993, 2003) separate two processes underlying positive reinforcement: “liking” and “wanting” (quotation marks are used to distinguish the underlying neural processes from the subjective experience). “Liking” refers to a stimulus’s hedonic impact, whereas “wanting” refers to the ability of a stimulus to evoke approach behavior. Whereas “liking” and “wanting” usually go hand in hand (for good evolutionary reasons; Nesse & Berridge, 1997), they dissociate as addiction develops (Berridge, Robinson, & Aldridge, 2009; Robinson & Berridge, 1993, 2003, 2008). As a result, individuals can respond with “wanting” to drug cues despite reduced “liking.” The enhanced “wanting” response to drug-related cues is related to mesolimbic dopaminergic pathways, with a central role for the ventral striatum. Although most theorizing in this area is based on animal research, some attempts have been made to dissociate wanting from liking in humans using subjective (Ostafin, Marlatt, & Troop-Gordon, 2010), behavioral (Hobbs, Remington, & Glautier, 2005), and neurocognitive measures (Leyton, 2007; Vollstadt-Klein et al., 2012). For example, Hobbs and colleagues (2005) found that adding an aversive taste reduced consumption of fruit juice in both light and heavy drinkers. Whereas the same manipulation also decreased beer consumption in light drinkers, it did not decrease beer consumption in heavy drinkers, despite reduced subjective liking. From the present perspective, it is important that incentive sensitization has been related to a number of cognitive biases: an attentional bias (AB) for the substance (Field & Cox, 2008; Robinson & Berridge, 2003, 2008), an approach bias for the substance (Field, Kiernan, Eastwood, & Child, 2008; Robinson & Berridge, 2008; Wiers, Rinck, Dictus, & van den Wildenberg, 2009), memory associations with approach and arousal (Palfai & Ostafin, 2003; Wiers, van Woerden, Smulders, & de Jong, 2002), and subjective craving (Field, Munafo, & Franken, 2009; Robinson & Berridge, 2008).

In addition to “liking” and “wanting” (both positive reinforcement), there is a long history of negative reinforcement as an important motivational process in addiction, especially in relation to tolerance and withdrawal (Koob & Volkow, 2010; Siegel, 1999; Solomon & Corbit, 1974). Classically conditioned drug cues (e.g., a specific context) elicit opponent processes that counter the effects of the drug. As a result, the drug effect is moderated, resulting in increased tolerance. The opponent process is also assumed to elicit a withdrawal-like state with strong negative affect, which leads to a motivational state aimed at remedying this state. As a result of prolonged drug use, reward functions fail to return within the normal homeostatic range (a so-called allostatic state; Koob & Le Moal, 2008). At a subjective level this is experienced as “anhedonia,” sometimes labeled “the dark side of addiction” (Koob & Le Moal, 2008). These changes are associated with neural circuits involving the extended amygdala (Koob & Volkow, 2010). Anhedonia is temporarily remedied through drug use, but in the long run this aggravates negative affect.

In addition to these motivational processes in addiction, habit formation has gained prominence in addiction research (Everitt & Robbins, 2005; Tiffany, 1990). Recent evidence has emphasized that in the etiology of addiction, this process is pharmacologically enhanced, with the result that after prolonged drug use, the addictive behavior can persist despite (lack of) reward or even punishment and can thus develop from an impulsive outcome-focused behavior to compulsive habitual behavior (Everitt et al., 2008; Everitt & Robbins, 2005; Vanderschuren & Everitt, 2004). This change from impulsive to compulsive drug use has been associated with a shift from ventral to more dorsal striatal processing in response to drug cues (Everitt & Robbins, 2005; Vollstadt-Klein et al., 2010). One interesting implication of the habit perspective on addiction is that it predicts that cognitive biases become smaller after long addiction, and there is some evidence supporting this shift in long-term addiction (Loeber et al., 2009).

Although there is much debate regarding the relative contributions of these different pharmacologically enhanced learning processes to different phases of addiction (Everitt et al., 2008; Koob & Le Moal, 2008; Robinson & Berridge, 2008), we emphasize that from the dual-process perspective, they all result in strengthened impulsive processes: Increased incentive salience results in cues related to the substance capturing attention and eliciting an approach tendency (Berridge et al., 2009; Robinson & Berridge, 1993, 2003, 2008); negative affect may trigger an urge to take new drugs through sometimes unconscious associative processes (Baker, Piper, McCarthy, Majeskie, & Fiore, 2004); and habitual drug-related responses become increasingly hard to control (de Wit & Dickinson, 2009; Everitt & Robbins, 2005). Note that this review is not exhaustive and that other neurocognitive mechanisms have been implied in the development of addiction, such as decreased self-insight (Goldstein et al., 2009). Furthermore, although neurobiological addiction research has emphasized the neuroadaptations underlying addiction as markers of addiction as a “brain disease” (Leshner, 1997), at least to some extent, similar cognitive biases have been reported for nonsubstance addictive behaviors, such as gambling (Brevers et al., 2011), and for excessive or deviant sexual behaviors (Gray, Brown, MacCulloch, Smith, & Snowden, 2005), highlighting the continuum between addictive and other motivated behaviors (Kopetz et al., in press). Although there is neurobiological evidence that neural changes as a consequence of addiction are long lasting, if not forever (Koob & Volkow, 2010; Robinson & Berridge, 2003), there are ample examples of heavily addicted individuals who successfully quit their addiction with or without intervention (Heyman, 2009; Miller, 1998). Hence, the “brain disease” perspective is important for developing a better understanding of addiction, but this should not leave the patient (or therapist!) with the idea that addicted patients are helpless “slaves of passion” (Wiers, Field, & Stacy, in press). In fact, some of the training processes described herein may help patients to increase control over their strong impulsive processes.

Weaker reflective processes as a result of substance use

There is evidence that heavy alcohol and drug use is associated with relatively weak reflective processing (Verdejo-Garcia, Lawrence, & Clark, 2008), defined in dual-process models as the combination of the ability to control impulses (cognitive control) and the motivation to control, related to long-term outcomes (Hofmann, Friese, & Wiers, 2008; Wiers et al., 2007). First, note that impulsivity ² concerns a well-supported antecedent to early alcohol and drug use (H. de Wit, 2009; Verdejo-Garcia et al., 2008). Related, a relatively weak development of executive functions has been reported in children of alcoholics, related to externalizing problem behaviors and risk for addiction (Nigg et al., 2004; Sher, 1991). Impulsivity is also a risk factor for the escalation from substance use to compulsive drug use (Belin, Mar, Dalley, Robbins, & Everitt, 2008).

What is the evidence for substance-induced impairments of reflective processes? First, there is relatively strong experimental evidence from animal research that excessive use of alcohol and drugs (especially during adolescence) has strong effects on subsequent brain development involving cognitive and emotional regulatory processes (Crews & Boettiger, 2009; Nasrallah, Yang, & Bernstein, 2009). The difficulty with this line of evidence lies in the generalization to humans, wherein control over emotional processes develops longer. The second line of evidence comes from human studies demonstrating that adolescent binge drinking and heavy use of marijuana is associated with impaired executive functions (Tapert et al., 2004, 2007). However, these studies are predominantly cross sectional and can therefore not establish a causal effect of these substances on subsequent brain development, although dose-response relationships and the converging animal literature are suggestive of such a link. Scarce longitudinal human data using neuropsychological tests suggest that binge drinking (especially multiple detoxifications) results in brain damage (Duka et al., 2011; Loeber et al., 2009, 2010; Squeglia, Spadoni, Infante, Myers, & Tapert, 2009; Stephens & Duka, 2008) and suboptimal decision making (Duka et al., 2011; Goudriaan, Grekin, & Sher, 2007, 2011). A recent paper from the Dunedin study, in which neuropsychological testing was done before cannabis use (age 13) and again at age 38 (with several measurements of cannabis dependence in between), indicated a strong neurotoxic effect of persistent cannabis use when initiated at age 18 (Meier et al., 2012). Different drugs to some extent appear to differentially impair different executive functions, but the general picture is that substance abuse impairs reflective processing (Fernandez-Serrano, Perez- Garcia, & Verdejo-Garcia, 2011). From the dual-process perspective, these findings indicate that the control of cue-induced cognitive-motivational processes becomes increasingly hard in addiction, and some theorists have emphasized this lack of control as the defining feature in addiction (Jentsch & Taylor, 1999; Volkow, Fowler, & Wang, 2004).

Individual differences in associations and reflective processes

In many recent studies, impulsive cognitive motivational processes are assessed with implicit or indirect measures (De Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009; Roefs et al., 2011). Different measures are used to this end: open-ended measures of first associations, derived from basic memory research (Stacy, Ames, & Grenard, 2006; Stacy & Wiers, 2010); various reaction time measures, including the most frequently used Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998); priming measures (Fazio, 1990); and physiological measures, varying from eye movements (Friese, Bargas-Avila, Hofmann, & Wiers, 2010; Schoenmakers, Wiers, & Field, 2008) to measures of brain activation in response to subliminal stimuli (Childress et al., 2008; Oei, Rombouts, Soeter, van Gerven, & Both, 2012). Importantly, all measures need to be validated regarding their “implicit” properties (De Houwer et al., 2009). Note that there is evidence from mathematical modeling that frequently used measures such as the IAT should not be regarded as “process-pure” indicators of impulsive processes (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005; Sherman et al., 2008). Despite these issues, the overall picture is that many studies have demonstrated that implicit and explicit measures predict unique variance in addictive behaviors, as two meta-analyses have shown (Reich, Below, & Goldman, 2010), with similar findings in other forms of psychopathology (Roefs et al., 2011).

A number of recent studies tested the hypothesis from dual-process models that the impact of relatively automatic processes on addictive behaviors is moderated by individual differences in reflective processes (Stacy et al., 2004; Wiers et al., 2007). These studies used different measures of impulsive processes—open-ended measures (Grenard et al., 2008), varieties of the IAT (Friese & Hofmann, 2009; Houben & Wiers, 2009; Thush et al., 2008; Wiers, Beckers, Houben, & Hofmann, 2009), and varieties of the Alcohol Approach Avoidance Test to assess automatically activated approach tendencies (Peeters et al., 2012; Peeters et al., in press; Sharbanee et al., in press). Individual differences in the relative strength of reflective processes were assessed with tests of WMC (Grenard et al., 2008; Sharbanee et al., in press; Thush et al., 2008); with other general tests of executive control (EC), such as the Classical Stroop Test (Houben & Wiers, 2009; Peeters et al., 2012; Peeters et al., in press; Wiers, Beckers, et al., 2009); and with a report measure of self-control (Friese & Hofmann, 2009). Participants were either high-risk adolescents (Grenard et al., 2008; Peeters et al., 2012; Peeters et al., in press; Thush et al., 2008) or undergraduate students (Friese & Hofmann, 2009; Houben & Wiers, 2009; Wiers, Beckers, et al., 2009). Outcome behaviors were alcohol use and problems (Friese & Hofmann, 2009; Grenard et al., 2008; Houben & Wiers, 2009; Peeters et al., 2012; Peeters et al., in press; Thush et al., 2008), smoking (Grenard et al., 2008), and aggression after alcohol (Wiers, Beckers et al., 2009). In all of these studies, the outcome behavior was more strongly predicted by impulsive processes in individuals with relatively weak EC than in individuals with relatively strong EC, in line with findings regarding other impulsive behaviors, such as candy eating in dieters (Hofmann, Friese, & Roefs, 2009; Hofmann, Gschwendner, et al., 2008). Note, however, that two studies did not find this pattern of results, and both used varieties of a more symbolic task to assess approach tendencies, the stimulus response compatibility (SRC) or manikin task (Christiansen, Cole, Goudie, & Field, 2012; van Hemel-Ruiter, de Jong, & Wiers, 2011), in which control processes play a major role (Cousijn et al., 2012). Some of these studies found that the measure of explicit attitudes or expectancies better predicted the outcome behavior in individuals with relatively strong EC (Friese & Hofmann, 2009; Thush et al., 2008), in line with findings in other domains (Hofmann, Gschwendner, et al., 2008). However, one recent study tested the latter hypothesis only (i.e., no implicit measures were assessed) and did not find that alcohol use was better predicted by alcohol expectancies in high versus low EC individuals (Littlefield, Verges, McCarthy, & Sher, 2011). Perhaps some explicit measures, such as simple positive expectancies, tap into both more impulsive and more reflective processes (Wiers & Stacy, 2010).

Hence, the overall pattern of results is consistent with the hypothesis that in individuals with low EC, measures of impulsive processes better predict the (addictive) behavior of interest, whereas in high EC individuals, measures of reflective processes have (often but not always) been found to be the better predictors. Note that the stronger prediction of addictive behaviors in low EC individuals holds after controlling for trait impulsivity and sensation seeking (Wiers, Ames, Hofmann, Krank, & Stacy, 2010).

Situational manipulation of associations and reflective processes

Some studies have manipulated self-control resources, sometimes referred to as “ego depletion” (Baumeister, 2003). Self-control and effortful control depend on a limited-capacity resource, sometimes compared with a muscle. After the muscle is strained (“ego depletion”), it becomes harder to override a subsequent dominant response tendency (Baumeister, Bratslavsky, Muraven, & Tice, 1998). To the extent that an individual’s relatively automatic appetitive responses steer one’s addictive behavior, unless inhibited by reflective processes, it is predicted that ego depletion will increase alcohol and drug use (Hofmann, Friese, & Strack, 2008; Muraven, Collins, & Nienhaus, 2002). Indeed, some evidence supports this claim (Friese, Hofmann, & Wanke, 2008; Muraven & Shmueli, 2006; Ostafin, Marlatt, & Greenwald, 2008). For example, emotion suppression, known to deplete cognitive resources (Wenzlaff & Wegner, 2000), has been found to increase consumption of alcohol in a subsequent taste test (Friese et al., 2008; Ostafin et al., 2008). In both studies, implicit alcohol associations better predicted alcohol consumption after ego depletion than in nondepleted control participants.

In addition, the use of alcohol (and most other drugs) can in itself be viewed as an ego-depletion manipulation. There is strong evidence of detrimental effects of acute alcohol on executive functions and performance monitoring (Field, Wiers, Christiansen, Fillmore, & Verster, 2010; Fillmore & Vogel-Sprott, 2006; Ridderinkhof et al., 2002). Some studies tested effects of alcohol on other behaviors in which self-control plays a role (Hofmann & Friese, 2008); for example, implicit attitudes better predicted candy consumption in dieters after alcohol condition than after a nonalcoholic drink. Alcohol also impairs goal-directed control of action selection, favoring habitual responding (Hogarth, Attwood, Bate, & Munafo, 2012).

Hence, manipulations of momentary reflective capacity—whether through exhaustion (“ego depletion”), through alcohol consumption (Field et al., 2010), or through other factors, such as stress (Field & Powell, 2007; Field & Quigley, 2009; Sinha et al., 2009) or sleep deprivation (Gohar et al., 2009)—will all lead to a momentary increase of the impact of impulsive processes on behavior, whether increased alcohol or drug use or other behaviors, such as eating despite restraint goals.

Training manipulations of associations and reflective processes

The third line of evidence comes from training studies. Note that the first studies that attempted to manipulate a cognitive bias were designed to experimentally test the causal status of the bias on behavior (MacLeod, Rutherford, Campbell, Ebsworthy, & Holker, 2002). Once this had been established, CBM was tested in various clinical populations.

Addressing theoretical criticism of dual-process models

A first criticism is that if human cognition can be divided into two types of processes defined by a coherent set of mutually exclusive characteristics, then these characteristics should covary consistently for a given process. This does not appear to be the case—for instance, automatic processes may require attentional resources or be efficient but still dependent on intentions (Keren & Schul, 2009; Kruglanski & Gigerenzer, 2011). Such results suggest that a single impulsive-reflective or automatic-controlled dimension may not be tenable (Moors & De Houwer, 2006). It can be argued, however, that this is by no means fatal to dual-process models—it has long been pointed out that all meaningful behavior is likely to involve a mixture of automatic and controlled processes (De Houwer et al., 2009; Moors & De Houwer, 2006).

Second, even if a true fundamental division between two types of processes is accepted, the relationship between dual processes and the implementation of these processes in cognitive or neural systems raises questions. In a critical review (Keren & Schul, 2009), it was argued that much of the evidence used to support the existence of such dissociable systems is problematic. We briefly note that some theorists have described the relation between dual-process concepts and the brain by tentatively mapping specific brain regions to either the impulsive (“reflexive”) or reflective system. In these models, typically emotional and motivational systems provide the drive and frontal cortices the control (Heatherton & Wagner, 2011; Satpute & Lieberman, 2006). Our metaphorical horse has now become a motivational brain circuit, and the rider, a control circuit. Criticisms on dual-system models apply to these models. However, it should be noted that in the broader neurocognitive literature, control is commonly defined as the top-down biasing of ongoing subordinate processes rather than as a separate systems (Cohen, Braver, & O’Reilly, 1996; O’Reilly, Noelle, Braver, & Cohen, 2002; Ridderinkhof, Forstmann, Wylie, Burle, & van den Wildenberg, 2011). Therefore, a meaningful distinction between controlled and automatic processes does not depend on the separability of systems.

Third, dual-process models aimed primarily at distinguishing types of processing do not naturally provide an explanation of why reflective control is exerted, at what moments, and of what kind. This is not to say that motivation plays no part in dual-process models. In some recent dual-process models of addiction, motivation is explicitly mentioned, in terms of both a reflective component, “motivation to control” (following Fazio, 1990), and in an impulsive component, “appetitive motivation,” suggesting that the two kinds of motivation should be considered as separate “boxes” from each other and from EC (e.g., Wiers & Stacy, 2006). Such descriptive models provide a necessary first step in structuring conceptual elements relevant to addiction. The next question is how the processes involved in reflective processing are themselves reinforced (Gladwin, Figner, Crone, & Wiers, 2011), a topic undergoing a much-needed revival of interest (Ridderinkhof, Cohen, & Forstmann, 2012). Hence, the question is not “Why is the reflective system motivated to control?” but rather “When the reflective system exerts control, why does it work toward motivationally significant ends?” There is emerging evidence that this “motivational homunculus problem” can be solved by modeling the interplay between the development of motivation and control (Frank & Badre, 2012; Hazy, Frank, & O’Reilly, 2006, 2007). Hence, we argue that the two central issues that have the potential to be fatal flaws of dual-process models—defining impulsive and reflective processes and the relationship between control and motivation—are in fact closely related. Indeed, contemporary cognitive neuroscience suggests that the integration of motivation and control is essential to understand what control is and how control works (Harsay et al., 2011; Hazy et al., 2006, 2007; Kouneiher, Charron, & Koechlin, 2009; Ridderinkhof et al., 2012).

Our goal here is not to present a complete model but to point out that reflective and impulsive “processes” can be redefined in terms of distinguishable states in a system of overlapping functional components rather than qualitatively different processes in separable systems (Gladwin et al., 2011). These states can be defined in terms of iterative reprocessing, as proposed in research on evaluation (Cunningham & Zelazo, 2007):

Stimuli (e.g. people, objects and abstract concepts) initiate an iterative sequence of evaluative processes (the evaluative cycle) through which the stimuli are interpreted and reinterpreted in light of an increasingly rich set of contextually meaningful representations. Whereas evaluations that are based on few iterations of the evaluative cycle are relatively automatic, in that they are obligatory and might occur without conscious monitoring, evaluations based on additional iterations and computations are relatively reflective. (p. 97)

We suggest that the mechanism of iterative reprocessing also involves reflective versus impulsive response selection (Gladwin et al., 2011). With continued reprocessing, the outcome becomes more “reflective.” This view of reflective processing is closely related to the cognitive neuroscience literature and theories of conscious processing, adaptation, and control that use processing cycles and reentry as their organizing principle (Dehaene & Changeux, 2011; Edelman, 2001; Fuster, 2004). Importantly, “reinforcement” processes (Bunge, Burrows, & Wagner, 2004; Seger, 2008) form the foundations for subsequent “reprocessing.” In this way, rather than a reflective system of a set of processes acting as a homunculus exerting control to achieve some distal goal, a reflective state can be defined that changes how previously reinforced associations affect response selection. From this perspective, addictive substances affect the availability of information concerning immediate versus long-term outcomes of reflective versus impulsive responses, which affects response selection.

This argument is not intended to entail a full-fledged model but primarily serves as a proof of principle regarding important theoretical criticisms. However, the preliminary model (termed the reinforcement/reprocessing model of reflectivity, or R3 model; Gladwin et al., 2011) may be of use in generating hypotheses and providing a theoretically strong framework for interpreting results. This perspective implies that the timing of events plays an essential role in relatively reflective versus impulsive processing. In line with this prediction, it was recently found that more delayed automatic affective reactions to tempting food stimuli were correlated more strongly with conscious food cravings than immediate automatic affective reactions (Hofmann, van Koningsbruggen, Stroebe, Ramanathan, & Aarts, 2010). Presumably, extending the interstimulus interval in the task used allowed for the iterative reprocessing of tempting stimuli until a conscious craving response was generated (in line with the elaborated intrusion theory of craving; Kavanagh, Andrade, & May, 2005). Further evidence for a role of time-dependent processes was found in AB research. In alcohol-dependent patients, an approach bias is found at cue presentation, which later reverses into an avoid bias (Noel et al., 2006; Vollstadt-Klein et al., 2010), similar to the vigilance-avoidance pattern found in anxiety (Mathews & MacLeod, 2005). Time dependence has also been found for the effects of alcohol-related distracters (Gladwin & Wiers, 2012).

What are we training?

As anticipated, the horse-rider metaphor has been helpful in generating novel interventions, but some of the outcomes were hard to reconcile with the metaphor and may be better understood from the iterative reprocessing alternative. For example, the finding that attentional retraining in alcoholic patients successfully changed the relatively slow AB, while leaving the AB for short presentations unaffected (Schoenmakers et al., 2010), may be understood as CBM influencing reprocessing once triggered by the initial appraisal of motivationally relevant processes, without changing the initial appraisal (Frijda, 1986). The category-specific inhibition training (Houben, Nederkoorn, et al., 2011) appears to teach participants a category (e.g., alcoholic drinks) as a stop signal, and as a result, this category is evaluated more negatively even in an implicit task (suggesting a change in relatively early processing). From the present perspective, all of these interventions may help to overcome the dominant response tendency to approach alcohol-related stimuli. As a patient in the first clinical study of approach bias CBM explained: “When I see the beer section in a supermarket now, I immediately turn away from it while remembering the joystick training.” The training may provide the patient with a longer time window for decision making (i.e., iterative reprocessing) or may “buy the patient time” from the tendency to react impulsively (cf., Lewis, 2011). One crucial aspect of CBM training is that the alternative response is triggered when needed, in a bottom-up fashion, by relevant stimuli. In fact, that may be the added value of CBM above CBT and motivational interventions, for which alternative goals are discussed but no specific alternative responses are trained in such a concentrated way.

From this perspective, one may wonder how general WMC training may have reduced problem drinking (Houben, Wiers, & Jansen, 2011). Findings indicated that WMC was improved by the training, but how was it triggered in high-risk situations? One possible explanation concerns incidental learning: Participants were aware that the goal of the training was to help them to cut down their drinking, and in many sessions, alcohol use and problems were also assessed. Perhaps this context already created enough associations to make the increased control capacity available for participants who lacked control when facing a drink, owing to their relatively strong automatically activated associations. Clearly, this requires further study.

More broadly, one area in need of further research is to what extent it is more efficient to train domain-general capacities (such as attentional control or WMC) or control in relation to a specific domain (triggered by domain-specific stimuli, as in CBM). One interesting suggestion would be to test what would happen in a CBM training of an irrelevant domain. If the active ingredient in training is domain general, one might train alcoholic patients with fear pictures and anxious patients with alcohol pictures. We would predict that it would make a difference because an important aspect of training is that control processes are triggered in time by stimuli related to the problem domain. Hence, automatic activation of control processes appears to be crucial (cf. Bijleveld, Custers, & Aarts, 2009). Preliminary evidence in line with this suggestion comes from a study in anxiety showing that training attention toward and away from threat attenuated social anxiety, whereas in the control condition it did not (Klumpp & Amir, 2010).

A related question is to what extent effects of one type of CBM generalize to another bias (e.g., if we do attentional retraining, does this also affect an approach bias for a substance?). Initial work demonstrates that effects of one type of CBM may generalize to other biases in anxiety (Amir, Bomyea, & Beard, 2010) and depression (Everaert, Koster, & Derakshan, 2012). We are presently investigating this issue in addiction. It may also be wise to consider older cognitive literature on optimizing generalization of training effects (Hertel & Mathews, 2011).

Optimizing training by integrating motivation and control

From the present perspective, CBM most likely works by extending the time window within which decision making takes place—in other words, by training relatively more reflective decision making. In this narrow context, it may be helpful to train an alternative behavioral option. For example, in the case of alcohol, often nonalcoholic drinks have been chosen as a contrast category, which is made more salient in the experimental condition. The reasoning behind this was that in many social situations for which one is at risk to drink alcohol despite long-term motivation to abstain, the logical choice will be between an alcoholic and nonalcoholic drink (Ostafin & Palfai, 2006; Wiers et al., 2002). This narrow response window training will probably profit from a context in which more broadly defined alternative goals are explored (in more conventional CBT and motivational interviewing).

Up to now, CBM in addiction has typically been delivered as an add-on to “treatment as usual” (usually CBT with motivational interviewing elements). ⁷ Hence, all patients receive regular treatment with a training module added: for half, the true training; for the other half, a placebo version. ⁸ This is a good design to establish additional effects in a clinical context, but it does not help to integrate the CBM and CBT. There are examples of training programs that integrate patients’ personal alternative goals into the training (Alfonso, Caracuel, Delgado-Pastor, & Verdejo-Garcia, 2011). This may help to motivate patients for the training. In addition, elements of CBT may be fine-tuned to integrate training into the total program, which would also help to motivate patients but at the same time make controlled research more difficult. This could be tackled by employing different research designs, such as multiple case series (Rozenman, Weersing, & Amir, 2012; Teachman, Marker, & Smith-Janik, 2008).

A related issue concerns the acceptability of the training for the patients. This appears to be a concern, especially for attentional retraining, both in the domain of anxiety (Beard, Weisberg, & Primack, 2011) and in addiction (Schoenmakers et al., 2010). In the latter study, participants were asked to guess whether they had received the real or placebo training variety, and they almost all guessed they received placebo training. Though positive from an experimental perspective (making placebo effects unlikely), this is less desirable from a clinical perspective: Apparently, no patients could imagine how the training could ever help them with their addiction. In anxiety, a similar conclusion was reached on the basis of qualitative interviews (Beard et al., 2011). Somewhat more positive attitudes were reached for interpretation bias retraining in anxiety (Beard et al., 2011) and approach bias retraining in addiction (Wiers et al., 2011). In the latter case, patients even protested against closing the study. These preliminary findings suggest that implementing interpretation bias and approach bias retraining may be easier in clinical practice than attentional retraining, but at the same time they also make these interventions more vulnerable for placebo effects. One way to improve acceptability of training may be to introduce motivating feedback (Fadardi & Cox, 2009). A next step could be to further introduce gamelike elements, such as gaining points that are helpful in a shell game surrounding the training, an approach that has been successful in children with attention-deficit/hyperactivity disorder (Dovis, Van der Oord, Wiers, & Prins, 2012) and might be helpful in young substance abusers with little motivation to change.

A related topic concerns the optimal delivery method. Because almost all training methods are computerized, they may be delivered over the Web. However, as noted earlier, successful training in clinical groups, at least in addiction, has always been combined with some other form of treatment. This does not necessarily mean that CBM can work only when added to traditional face-to-face CBT, because some of the elements of traditional treatment may also be given in a computerized fashion, with positive results in addictive behaviors (Riper et al., 2008; Riper et al., 2011). The combination of CBM and a brief computerized cognitive-motivational intervention has shown promising results in anxiety (Amir & Taylor, 2012), whereas Internet-delivered CBM alone has not (Carlbring et al., 2012). An additional interesting possibility is to start a training in a clinical context (with appropriate motivational context) and let patients continue the training at home over the Internet, as we are presently testing. This may help the transfer from the clinic to more difficult situations in everyday life. In addition, integration into daily life may be promoted by CBM applications for smartphones, although efficacy remains to be proven (Enock, Hofmann, & McNally, 2012).

Finally, regarding optimizing training, it should be emphasized that we are at the beginning of a journey, rather than at the end, with many practical questions that need systematic investigation. First, we do not know the optimal duration and timing of training; all we can conclude from the present literature is that more than one session appears to be needed. Second, we do not know which training or combination of training methods works best for a specific problem.

Enhanced training

One interesting emerging possibility is to enhance training by electrical stimulation or by pharmacological means. For instance, there is emerging evidence that direct low-voltage electrical stimulation (transcranial direct current stimulation; tDCS) of the dorsolateral prefrontal cortex can temporarily enhance WMC (Fregni et al., 2005). Recent studies have attempted to further specify the nature of this enhancement, suggesting that tDCS affects component processes of WMC rather than control as a whole (Gladwin, den Uyl, Fregni, & Wiers, 2012), consistent with the present perspective. However, stimulation may also make behaviorally relevant information more accessible, in which case stimulation effects are not self-evident and may depend on the task context (Gladwin, den Uyl, & Wiers, 2012). Interestingly, tDCS also temporarily reduced craving for alcohol (Boggio et al., 2008) and cigarettes (Boggio et al., 2009). How could this be the case? Prefrontal regions support top-down biasing: an internal, cognitive response that may be required to inhibit craving-related processes. Anodal prefrontal stimulation would be expected to facilitate the activation of such biasing processes. Because tDCS also influences neuronal plasticity (Nitsche et al., 2003; Paulus, 2003), a potentially important application is the combination of tDCS with the novel training interventions described earlier. Whereas the immediate effects of tDCS are temporary, its effects on concurrent training could be more persistent, either directly via plasticity or by aiding the subject in performing the training task at a higher level. However, because tDCS does not appear to increase control as a whole but rather context-specific component processes, effects on cognitive biases can be surprising (Gladwin, den Uyl, & Wiers, 2012); therefore, the effects of tDCS-enhanced CBM should first be critically tested before it can be applied to clinical populations. Some medication (e.g., modafinil) may serve a similar goal, with indications that baseline levels of impulsivity may be crucial (Schmaal et al., in press; Zack & Poulos, 2009). In addition, there are initial positive findings regarding neurofeedback in addiction (Sokhadze, Cannon, & Trudeau, 2008), which may also be viewed as “training” but is beyond the scope of the present review.

Tailored training?

It would seem important to select participants for a specific type of CBM or training. For example, patients with a strong AB would most likely gain most by attentional retraining. One important prerequisite for this type of treatment matching is that the matching variable can be assessed in a reliable way. For AB, that is very difficult, with modest reliabilities being reported for tests of AB (Ataya et al., 2012). However, two studies found that a stronger bias at pretest predicted stronger CBM effects in anxiety (Amir, Taylor, & Donohue, 2011; Salemink & Wiers, 2011). In the first randomized clinical trial of attentional retraining in addiction (Schoenmakers et al., 2010), the pattern of the data suggested that the training effect on AB occurred only in those patients in the experimental condition with a strong AB at pretest, but the corresponding interaction was not significant, probably because of the poor reliability of the measure and low power. In the recent large replication study of approach bias retraining, a strong approach bias before training was found to moderate the training effect (Eberl et al., in press). Hence, there is some promise to the idea to select participants for CBM who show a strong bias, but the challenge is to assess this bias in a reliable-enough way to justify individual assignment to a condition. An interesting paradox appears to emerge here: Tasks that are most indirect are most easily adjusted for retraining (visual probe, AAT) because participants react to a different feature of the stimulus and because contingencies of the stimuli can be manipulated without changing the instructions. At the same time, these measures appear to be suboptimal for assessment, owing to suboptimal reliability (De Houwer & De Bruycker, 2007; Field, Caren, Fernie, & De Houwer, 2011), and therefore for determining whether an individual patient would benefit from this training. One alternative could be to use another measure for the latter purpose (e.g., SRC or IAT), but the correlations between these measures is typically low. A recent study using the relevant-feature SRC found that alcoholic patients with relatively strong alcohol avoidance tendencies had a relatively large chance to relapse (Spruyt et al., in press), a finding apparently at odds with training results in alcoholic patients, which showed that training to avoid alcohol reduces relapse (Eberl et al., in press; Wiers et al., 2011). Hence, although the SRC is more reliable than the AAT, selecting participants for approach bias retraining with the SRC seems inadequate.

Another alternative could be to assess a correlated variable that can be assessed reliably, such as the presence of a G allele in the OPRM1 gene for a strong appetitive approach bias (Wiers, Rinck, et al., 2009), but this hypothesis remains to be tested. Encouragingly, initial evidence regarding genetic moderation of AB retraining in anxiety has been found (Fox, Zougkou, Ridgewell, & Garner, 2011). One possibility is that training effects are dependent on a number of genes that moderate plasticity and learning (Belsky et al., 2009). In support of the latter view, plasticity genes have recently been found to be cumulative with respect to their moderating role on the relationship between parenting and adolescent self-regulation (Belsky & Beaver, 2011).

Other moderators, such as EC, are of interest too, as shown in interpretation bias retraining in anxiety in adolescents (Salemink & Wiers, 2012). Given the moderating role of EC in addictive behaviors, this variable is likely to be relevant in the domain of addiction as well. In the recent replication study of the clinical approach bias retraining, it was found that age moderated the effect of training and was negatively correlated with EC capacity in these alcoholic patients (Eberl et al., in press), as would be expected by the detrimental effects of alcohol and detoxifications on EC capacity (Crews & Boettiger, 2009; Duka et al., 2011).