Several theorists have distinguished between a goal-directed, purposive aspect of attention and a more stimulus-driven, reflexive component (e.g., Corbetta and Shulman, 2002; Posner and Petersen, 1990). The former is volitional and guided by declarative knowledge such as expectations and schematically encoded information. According to Posner and Petersen, these functions are supported by anterior cortical circuitry, especially prefrontal areas. In contrast, the latter, more reactive system is responsive to biological or behaviourally relevant stimuli, especially those not currently attended to. These functions are associated with more posterior neural networks in the temporoparietal and ventral frontal cortex. Conventionally, these two systems are referred to as ‘top down’ and ‘bottom up’, corresponding to controlled and automatic modes of information processing respectively. The two systems are assumed to operate in a complementary manner. For example, I am using top-down attentional strategies to write this paragraph, but if the doorbell rings this will capture attention whether or not I was expecting it, or indeed whether or not it was a welcome interruption: the doorbell captures my attention whether it is goal relevant or not. Another example is when stimuli that are task or goal relevant (e.g., finding a mail box in an unfamiliar neighbourhood, or finding a friend in a crowded room) become more salient or ‘pop out’ from the background. These examples illustrate how the effective and efficient pursuit of goals is facilitated by the seamless operation of top-down and bottom-up processes. When preferred goals become less preferred, this facilitation nonetheless continues, especially when the former goals have acquired the high incentive value ascribed to addiction-relevant stimuli.

This apparently automatic top-down modulation is usually, like much implicit processing, helpful. As pointed out above, without this capacity, it would take much longer to detect a familiar face in the crowd, or a mailbox in a congested street. All well and good, as long as one is single minded, or fortunate, enough to live a simple and uncluttered life. A more typical scenario perhaps is to be at one’s desk struggling with competing priorities, including a deferred lunch, when a colleague with whom you share a room arrives with his or her own freshly microwaved, aromatic lunch. Here, more cognitive control is needed to adjudicate over the maintenance of whatever is judged to be the most important goal. But that goal maintenance can be compromised by both bottom-up and top-down processes. First, the colleague’s meal can capture attention and assign the goal of ‘finding lunch’ prominence in working memory (WM). Second, if lunch becomes the goal, associated cues will acquire greater propensity to distract from other goals. A key theme in this book is that, in the context of addiction, implicit processes such as the allocation and maintenance of attention are slow to adapt to the new rule, ‘no-go’, and loyally continue to implement the ‘go’ rule that compels the individual to seek and find cues that point to appetitive rewards. This rivalry between implicit processes and deliberate efforts to resist appetitive impulses is the key dynamic in addiction and the authentic representation of ambivalence. It thus represents a legitimate target for therapeutic intervention.

The ability to search for relevant environmental clues, or cues, automatically is efficient and relatively resistant to overload. Thus, Koch and Tsuchiya (2006) concluded that on average in the visual faculty alone we have to deal with about one megabyte of raw data each second, based on an estimated one million nerve fibres leaving each human eye. It is perhaps not surprising therefore that experimental findings show that detection rate or efficiency is relatively unaffected by the amount of distracters or potential targets (i.e., whether there are 10 or 100 faces). Selective attention has evolved to deal with this vast quantity of information. Evans and Treisman (2005), for example, used a rapid serial visual presentation (RSVP) paradigm to present 288 ‘target’ images of vehicles or animals interspersed by 2,456 distracter images in visual scenes. As implied by the term RSVP, the visual scenes were presented very rapidly, in blocks of six. Each presentation took 450 milliseconds (ms), allowing just 75 ms for each scene to be processed. These investigators found up to 80% correct detection rates for target stimuli (animals or vehicles), although more contextual or semantic processing was less apparent. Thus, participants were able to indicate fairly accurately whether they saw a vehicle or an animal in a given scene, but were less adept at stating whether it was a truck or a rabbit, or if it was on the left or the right of the picture. These results pointed to rapid but crude feature analysis operating in advance of more attention-demanding identification. This remarkable ability to operate in parallel, with little or no demands on attention or need for conscious awareness, is of course advantageous in most scenarios.

In addiction, however, cues acquire an artificially high salience both because of their motivational significance and the associated high frequency with which they have been processed. The highly evolved faculty to detect goal-relevant or survival-related stimuli is thus recruited to detect drug-related stimuli. Further, it appears that this preferential processing is somewhat immune from neglect or repression. The refractory period known as the ‘attentional blink’ (Raymond et al., 1992) occurs when two masked targets (T1 and T2) are presented within very brief periods of each other (usually less than 400 ms) and embedded in a stream of distracters. T2 is usually poorly identified when it is presented within a short time interval of T1. Participants appear not to notice the second of the two targets. Previously (Ryan, 2002b), I speculated that the attentional blink might exert its inhibitory influence in the context of addiction because the multiplicity of cues would overload its apparent limited capacity. I speculated that this might contribute to inconsistent responses to appetitive cues across various domains of cue reactivity such as craving, somatovisceral arousal and addictive behaviour as outlined by Carter and Tiffany (1999). Subsequent findings indicated that this may not be the case. Waters et al. (2007) found that attentional blink was reduced for smoking words, compared with neutral words, among a sample of 55 heavy smokers. The participants had to identify two target words (T1 and T2), which were either smoking related or neutral and embedded in 14 distracters. All 16 stimuli were presented rapidly, appearing on the screen for just 130 ms. The attentional blink appeared to operate as normal with neutral stimuli, inhibiting identification or recognition of T2 when it immediately followed T1. Performance was significantly less impaired when it came to identifying smoking-related stimuli.

A more recent study with problem gamblers (Brevers et al., 2011) generated similar findings in a controlled study. Here, the attentional blink ‘survived’ at 200 ms, but not at 400 ms, in the group of 35 problem gamblers that they recruited. With a longer time interval there is presumably less competition for attentional resources and the subtle attentional blink is not apparent. However, it does appear that, under conditions of more restricted attentional resources, smoking- or gambling-related words are more likely to be consciously identified than neutral words by individuals who are addicted smokers or compulsive gamblers. It is reasonable to hypothesize that this facilitation of substance- or gambling-related cues could also operate with other types of addiction. This implies that motivationally relevant cues appear to be resistant to key inhibitory processes such as the attentional blink that operate entirely outside conscious control. It seems that when evaluative processes assign a positive valence to addiction-related stimuli they ensure it is a long-term investment.

The attentional blink phenomenon highlights the inherently competitive nature of cognitive processing. In any given environment or situation there is invariably more information than an individual can either want, or need, to process. Desimone and Duncan (1995) proposed that visual stimuli are not processed independently at the neuronal level but interact with each other in a mutually suppressive way. Thus, presentation of two or more stimuli can lead to less neuronal activation in parts of the visual cortex because different neuronal representations can be mutually suppressive. In effect, stimuli competing for neural representation partially ‘cancel themselves out’ as soon as they are available in the receptive or perceptual field. Information is thus processed in a competitive manner, beginning at the single-cell level. Clearly, this ‘bottom-up’ selective process can be shaped by evolutionary imperatives or motivational priorities. Finding somewhere to eat in unfamiliar surroundings or identifying a friend’s or a loved one’s face in a large crowd is no doubt facilitated by biasing of filtering out the hundreds or possibly thousands of distracters.

Beck and Kastner (2009) placed the biased-competition model in a broader neurocognitive framework, particularly in terms of ‘top-down’ influences. They refer to an ‘attentional template’, in which a sought-after or valued object is held in working memory in order to promote target selection and filter out distracters. This, of course, implicates executive control and areas such as the prefrontal cortex. Here, I propose that habituated drug users and gamblers will have acquired an attentional template, in effect a mindset, which infiltrates the intrinsic competitive nature of cognitive processing. A cognitive therapist will perhaps recognize the schema-like properties in this concept, although here I envisage a less generic cognitive structure. For example, a cognitive schema that constructs a worldview congruent with threat or failure can accommodate a wide range of stimuli, situations and scenarios, but the template proposed here is far more specific and focal, in keeping with the attentional template outlined by Beck and Kastner (2009). It also accords with recent reappraisals suggesting that the capacity of working memory is considerably less than ‘the magical number seven plus or minus two’ proposed by Miller (1956). Jonides et al. (2008) reviewed the literature and concluded that capacity, the number of items directly accessible or the focus of attention, could be limited to four plus or minus one, with perhaps only one piece of information being the focus of attention on a moment to moment basis.

Clearly, if only one piece of information were defined as simply an item or a number we would be unable to achieve a simple mental arithmetic task such as adding five and six. Jonides and colleagues therefore proposed that attention indeed focuses on just one piece of information but this is defined not as a single item or stimulus but a ‘functional context’ that could be defined by time, stimulus characteristics or momentary task relevance. When applied to that very particular type of goal driven behaviour that defines addiction this emphasizes in effect how single minded we can be in pursuing goals in a highly motivated manner. The complex issue of working-memory capacity will not be resolved in these chapters, but the trend does appear to be one that emphasizes how restrictive working-memory capacity is at any given moment. This scarce resource, vital to effective self-regulation, is the prize available to whatever stimulus representation is ‘first past the post’, and it is indeed a case of the winner takes all.

Traditional models of addiction assume that mental preoccupation with one’s favourite drug or compulsion would activate craving and urgency that in turn serves as a final common pathway to drug seeking and drug taking. Cognitive models of attention ascribed a more multifaceted role to attention. Norman and Shallice (1986), for example, attempted to explicate the role that attention plays in the control of action. They first of all point out that the term ‘automatic’ has at least four different meanings:

• the way certain tasks can be carried out in the absence of awareness, for example walking down a familiar pathway;
  
• the manner in which an action may be initiated without deliberate attention or awareness, for example sipping from a glass of water or picking peanuts from a bowl;
  
• situations where attention is automatically drawn to something, for example the sudden appearance of a face at a window;
  
• the more technical use of the term in contemporary cognitive psychology where actions or tasks are deemed automatic if they do not appear to interfere with other tasks, that is are not constrained by limited capacity.

Norman and Shallice were particularly concerned with volitional and involitional regulation of action. Their starting point was that action or complex task performance appears to rely on the interaction of automatic processes ‘supervised’ or regulated by deliberate attentional control mechanisms that could either suppress unwanted actions or enhance desired ones. Norman and Shallice assign a precise role to attention. They contrast the relatively slow (at least 100 ms) pace of processing steps for deliberate control of attention with the requirement for skilled action sequences to be initiated with accuracy to the nearest 20 ms. Instead, therefore, of attention being involved in the ‘micromanagement’ or moment-by-moment regulation of behaviour, Norman and Shallice (2000) proposed that attentional resources are relevant primarily at the initiation and determination of a given action schema. They assume, plausibly, that competition between numerous potential action schemas is where the real battle for scarce cognitive resources is joined. They invoke the theoretical mechanism termed ‘contention scheduling’ to adjudicate in this conflict. They propose (p. 379) that this process ‘resolves competition for selection, preventing competitive use of common related structures, and negotiating co-operative, shared use of common structures or operations when that is possible’. Consequently, once the given action schema has reached the necessary activation level it is once again a case of winner takes all.

Drawing on another sporting metaphor, ‘a level playing field’ does not exist in the context of addiction: appetitive cues have acquired a competitive advantage when it comes to attracting the attentional resources necessary to activate behavioural schemata. This is indeed a transient advantage, but in the highly competitive context of information processing milliseconds do matter. This is likely to be largely stimulus-driven or ‘bottom-up’ processing. By definition, addictive behaviour is well practised and thereby encoded in behavioural or action schemata. Well practised behaviour can routinely be invoked in the absence of valid motivation. Reason (1984) referred to these as ‘action slips’. For example, one of his research participants described how, on his way to the back porch to get his car out, he put on his gardening outfit as if to work in his garden. Recently, and well before my normal bedtime, I went to the bedroom to retrieve a book but instead removed my watch and began getting ready for bed! These ‘capture errors’, while sometimes amusing, illustrate the fact that purposeful, apparently goal directed behaviour can be purely stimulus driven and involitional: in the examples mentioned above, neither gardening nor an early night was the conscious goal but these were pursued nonetheless. Note, however, that neither of the default behaviours involving the garden or the bedroom can be usually viewed as compulsive or incentivized. Accordingly, once the error is detected the correct behaviour course can be resumed, perhaps with a wry smile. This sharply contrasts with what is known about the evocative power of stimuli associated with addictive behaviour. Once these have captured attention even momentarily, they are likely to have triggered more powerful action tendencies regardless of whether this was task congruent or not. This resonates with a key theme of this text. A universal feature of cognition such as distractibility or loss of task focus or conflict monitoring assumes an altogether different meaning in the context of addictive behaviour. The magnetic effect of addictive cues can prove harder to override and other goals can fade into insignificance.

Dijksterhuis and Aarts (2010) reviewed evidence from neuroscience, cognitive psychology and social cognition in order to understand the relation between goals, attention and consciousness. One of their conclusions was that goal-directed volitional behaviour can be initiated by events or stimuli occurring outside awareness. An example of this would be presenting a subliminal stimulus or incidental ambient exposure to words such as ‘success’. This challenges the intuitively appealing notion that voluntary action relies on conscious decision making. Earlier experimental evidence indicating that consciousness was not necessary to trigger volitional behaviour was provided by the seminal studies of Libet et al. (1983). They designed an experimental paradigm that required participants to make a free choice about when to simply move their index fingers. The participants were asked to report when they made the decision to move one of their fingers and predictably this proceeded the action. Crucially however, electrophysiological measures revealed action potentials about one second prior to conscious awareness of making a decision. As Dijksterhuis and Aarts (2010) point out, the findings are not necessarily easy to interpret, because the participants were not deciding whether to move their finger but when to move their finger. Dijksterhuis and Aarts (2010, p. 469) nonetheless concluded in their review that there is abundant evidence that ‘people become consciously aware of an act only after they unconsciously decide to engage in it. In addition, at least some volitional behaviour does not require any conscious awareness at all: Goals and motivation can be unconsciously primed’. Purposeful, goal-directed behaviour nonetheless requires attention; otherwise, it would be unfocused and rarely successful. Dijksterhuis and Aarts (2010) proposed that attention and consciousness, while intuitively linked, can be differentiated. They applied a 2 × 2 taxonomy whereby stimuli are either attended to or not and whether they are reportable or not. Thus, attention can be engaged as in pursuing goal-directed behaviour in the absence of conscious awareness and, conversely, we can be conscious of stimuli without paying much, if any, attention to them.

Incentive-focused models of addiction (Robinson and Berridge, 1993; Ryan, 2002b; Franken, 2003; Kavanagh et al., 2005) assume that substance-related stimuli acquire a motivational salience that gives them a head start in the competition to gain attention. Thus, a substance cue ‘grabs attention, becomes attractive and “wanted,” and thus guides behavior to the incentive’ (Robinson and Berridge, 1993, p. 261). I attempted to place this in a more cognitive context by proposing that ‘cue reactivity and the experience of craving are meaningfully related to perceptual and cognitive processes that occur before, during, and after cue exposure’ (Ryan, 2002b, p. 68); the prediction is that there should be a strong, possibly reciprocal, relationship between escalating craving and increasing attentional bias. Preliminary findings (Ryan, 2002a) provided somewhat indirect support for this, with multiple-regression analysis revealing that indices of alcohol dependence, such as scores on the Severity of Alcohol Dependence Questionnaire (Stockwell et al., 1979) and self-reported alcohol consumption predicted the degree of interference on a Modified Stroop test. This task requires the participant to name the colour of the ink used to print words that are either neutral (in this case not related to alcohol) or significant, such as ‘Wine’, ‘Binge’ or ‘Vodka’. Interference was measured by comparing the reaction time to colour name alcohol-related words as opposed to neutral words. It is thought that this superficially simple task reflects the facility with which an individual can inhibit the prepotent tendency to read the word or conversely the extent to which the word meaning distracts the individual by capturing attention. In functional terms it appears to index automaticity.

Field and his colleagues recently conducted a meta-analysis of 68 datasets investigating the relationship between attentional bias, measured by a range of techniques such as eye movement tracking and the visual probe, and measures of subjective craving (Field et al., 2009). They found a modest relationship of r = 0.19, further reduced to r = 0.13 following a statistical correction for publication bias, between attentional bias and expressions of craving. This suggests 4% and 2% of shared variance, respectively. Mining subsets of the data produced a more nuanced set of findings. First, in the subset of 12 studies in which craving was experimentally manipulated a more robust correlation of r = 0.23 was apparent among participants with higher induced craving, compared with r = 0.08 among those whose subjective craving was not thus manipulated. Second, when Field et al. analysed subsets of data that used direct measures of attentional bias, such as eye-movement tracking, they found a more robust correlation, r = 0.36. Third, regardless of how attentional bias was measured, there was a higher correlation between cues associated with caffeine, cannabis, cocaine and heroin compared with cues associated with alcohol and tobacco. Finally, further stratification of the data indicated that there was no apparent difference in the relationship between attentional bias and craving amongst those who were engaged in treatment and those who were not actively seeking treatment.

It appears therefore that attentional bias is significantly related to phenomenological aspects of addiction such as craving, but the dynamics of this relationship are not yet fully understood. One way to achieve greater understanding of this relationship would be to manipulate cognitive bias in clinical settings, in a manner similar to innovations with therapeutics of anxiety disorders. Findings showing that attentional bias may form part of a cognitive vulnerability factor that meaningfully relates to key components of addiction, such as craving, are scientifically important. They set the scene for more rigorous evaluation using controlled trials in clinical settings with appropriate follow-up. Some of this evidence will be examined in Chapter 7. The key question is whether cognitive bias modification, or more specifically the reversal of attentional bias, has clinical utility, whether as a stand-alone procedure or as a ‘bolted-on’ extra to existing treatments. The available evidence is reviewed in the next section.

Implicit cognition and behaviour

A definitive aspect of implicit functioning is that it occurs in advance of or outside conscious awareness or is otherwise unavailable to introspection. Findings that abstaining smokers, for example, show evidence of unconsciously processing smoking-related cues (Leventhal et al., 2008) provide further evidence that drug-relevant information can be processed outside awareness, or in advance of conscious control. These researchers presented subliminal (17 ms) pictures of smoking-related, affective and neutral stimuli to nicotine-deprived smokers, non-deprived smokers and nonsmokers. Their method allowed them to assess what cues were unconsciously ‘preferred’. Smokers deprived of nicotine for at least 12 hours prior to testing showed a bias to where the ‘unseen’ picture of smoking paraphernalia appeared on a screen. This was not apparent in nondeprived smokers or nonsmokers. This shows the essentially motivational nature of the cognitive processing in the context of addiction. When the motivational value of the stimulus is presumably increased through deprivation, it becomes more salient. Consistent with this, Ingjaldsson et al. (2003) divided detoxified alcohol-dependent individuals according to whether they were low or high on measures of craving and presented them with subliminal (20 ms) masked slides of alcohol stimuli. Psychophysiological measures of cardiovascular reactions showed that in the high-craving group the heart rate reduced immediately after exposure to subliminal alcohol cues, suggesting an orientation or alerting response. This effect was not found within the low-craving or the control group. Moreover, when presented with ‘supraliminal’, that is visible, alcohol cues, the groups did not show cardiovascular differences.

Previously (Ryan, 2002b), I proposed that the detection of drug cues is automatically facilitated regardless of the conscious goal of the restrained drug user (e.g. ‘I want to avoid triggers for using drugs’). Due to the relentless nature of this hypervigilance, drug cues tend to engage attention even if they are unpredictable and occur over a lengthy period. Once detected, the ensuing evaluative appraisal assigns the cue a positive valence by default. This primary appraisal can elicit components of cue reactivity such as physiological arousal, behaviour and cognitions (e.g. expectancies) in advance of, or parallel with, the recruitment of focal attention. These components of cue reactivity are subjected to more elaborate appraisal mediated by attentional and inferential biases. This account accentuated the role of attentional processes and primary appraisal mechanisms. I concluded (p. 74) that ‘Cognitive biases can thus subvert the most fervently held desire for abstinence or restraint by generating a model of the environment where cues for the proscribed substance are virtually ubiquitous’. Assigning an influential role to cognitive biases was neither adequate conceptually nor did it provide a viable basis for therapeutic intervention. With regard to the latter, addressing or modifying cognitive biases presents considerable challenges from a therapeutic standpoint. Certainly, conventional cognitive therapy techniques were not applicable. Moreover, while influential in maintaining cycles of addictive behaviour, automatic cognitive biases such as the preferential allocation of attention to drug-associated cues was by no means the only information processing that occurred. Controlled processes are also available and could be exploited to regulate less consciously regulated processes.