Psychological models of drug addiction propose that through repeated pairings, cues and stimuli that are often found to co-occur with the presence of a drug-reward, and consequently can be perceived as signalling the availability of the drug-reward, acquire strong incentive motivational properties (incentive salience) through classical conditioning (Stewart, 1999). Research during the past decade has shown that these drug-associated stimuli (relative to nondrug-related stimuli) are able to grab attention (attentional bias), induce subjective craving and guide behaviour towards obtaining the substance (Field et al., 2008).
Attentional biases towards alcohol-related stimuli have consistently been observed in alcohol-dependent patients (Cox et al., 2000; Loeber et al., 2009) and are thought to be important in predicting relapse rates (Cox et al., 2002). However, biases to alcohol-related stimuli have also been found in nondependent social drinkers (Townshend and Duka, 2001; Field et al., 2004). Biases in social drinkers can lead to increases in feelings of alcohol craving, as well as to excessive drinking (Field and Eastwood, 2005), perhaps contributing to behaviours such as binge drinking (Field et al., 2008). Investigating biases in individuals before the development of compulsive alcohol use may therefore provide information with regard to the development of alcohol dependence and/or may provide strategies for the reduction of behaviours such as binge drinking.
Attentional biases to drug-related stimuli have been typically assessed using two types of approaches: one that tests the overt allocation of attention to such stimuli and another that examines the interfering effects of such stimuli on current behaviour. Dot-probe tasks and eye tracking methods have been used to assess the overt allocation and engagement of attention. Specifically in the alcohol dot-probe task, alcohol-associated and neutral stimuli are replaced by a dot probe that requires a motoric response. Nonalcohol-dependent social drinkers show biases in the initial orientation and engagement of attention as they are faster at responding to probes that replace alcohol-associated, relative to neutral, stimuli (Townshend and Duka, 2001; Field et al., 2004). In contrast, in tasks such as the alcohol Stroop, participants must call out the ink colour of words with either alcohol-associated or neutral meaning. Social drinkers display an inability to fully ignore the meaning of alcohol-related coloured words, and, therefore, task interference is larger than when the coloured word has a neutral meaning (Sharma et al., 2001; Bruce and Jones, 2004; Fadardi and Cox, 2008).
However, although it is generally accepted that biases to alcohol-related stimuli are the result of the conditioned incentive motivational properties that these stimuli acquire (Robinson and Berridge, 1993; Robinson and Berridge, 2000; Ryan, 2002; Franken, 2003), the mechanisms through which alcohol-related stimuli produce interfering effects on attention, as observed in alcohol Stroop tasks, have not been widely researched.
There is evidence that incidental information with strong motivational salience can interfere with selective attention to task-relevant stimuli. For example, visual search for physically salient targets is slowed by the presence of task-irrelevant stimuli that are associated with monetary rewards (Anderson et al., 2011). In addition, on the classic Stroop task (Stroop, 1935), although associating the ink colour of the coloured word (i.e. the ‘to-be-attended’ stimulus feature) with monetary reward facilitates selective attention, behavioural interference is more pronounced when the coloured word (i.e. the ‘to-be-ignored’ stimulus feature) refers to a reward-predicting colour (Krebs et al., 2010). One explanation for these findings may be that the top–down cognitive control mechanisms necessary for resolving distracter interference, by enhancing the attentional processing of the task/goal-relevant information (Posner and Petersen, 1990; Botvinick et al., 2001), are weakened when the task/goal-irrelevant distracters represent motivationally salient stimuli. The term ‘cognitive control’ refers to the multitude of processes that are necessary for adapting one’s behaviour to achieve a particular goal. Within the context of the present article, the term ‘cognitive control’ will be used within the domain of visual selective attention: it will refer to those processes that resolve interference at a perceptual level through the attentional amplification of task-relevant information.
Recent functional MRI (fMRI) studies have provided indirect evidence for the involvement of cognitive control mechanisms in attentional bias to drug-related stimuli, albeit without using direct measures of cognitive control. These studies examined the brain regions associated with the processing of task-irrelevant drug-related distracters in cocaine addicts (Goldstein et al., 2007) and smokers (Luijten et al., 2011b). They showed that regions associated with conflict monitoring and the resolution of distracter interference (e.g. dorsal parts of the anterior cingulate; Fassbender et al., 2009) were significantly activated in addicts compared with controls (Goldstein et al., 2007; Luijten et al., 2011b), suggesting that addicts required more attentional control to focus on task-relevant information and suppress the processing of task-irrelevant drug-associated stimuli.
In a further study on cocaine users, Hester and Garavan (2009) tested the interfering effect of task-irrelevant cocaine-related images on the performance of a working memory (WM) task of varying difficulty. They found that the presence of cocaine-related (relative to neutral) pictures significantly impaired performance under the high WM load condition and led to a significant increase in the blood oxygen level-dependent signal in the lateral inferior frontal cortex (IFC), an area associated with top–down cognitive control. This increase in activation was negatively correlated with the behavioural interference caused by the cocaine-related images. Thus, those addicts that showed the highest activation in this region performed best on the WM task. The authors suggested that the pattern of results is consistent with an inability, under high cognitive demands, to suppress the processing of distracting, but salient, drug-related stimuli in cocaine addicts and that successful suppression of drug-related stimuli was attributable to the inhibitory functions of the IFC. It is important to note that increasing WM load has been shown to affect the ability to selectively attend to a target and ignore conflicting distracters (De Fockert et al., 2001). Consequently, although Hester and Garavan’s (2009) results could be mediated through an effect on cognitive control mechanisms, the use of a WM task rather than a task that directly assesses selective attention in the presence of interference, makes it difficult to directly attribute the activation seen in IFC, as well as the pattern of behavioural data, to a modulation of cognitive control mechanisms in the presence of drug-related stimuli.
The present investigation extends this previous work in two ways: first, we examined whether social alcohol drinkers show similar patterns of behaviour in the presence of alcohol-related stimuli as do cocaine addicts with cocaine-related stimuli; and second, we used a dual paradigm similar to that used by Hester and Garavan (2009), with the difference being that it encompassed, as its primary task, a direct measure of cognitive control, the Eriksen flanker task (Eriksen and Schultz, 1979). The flanker task requires participants to classify a target stimulus while ignoring adjacent distracters (flankers) that either interfere (incongruent condition) or not (congruent condition) with the processing of the target. Cognitive control is used, under the incongruent condition, to resolve the interference elicited by the incongruent flankers and focus attention on the target stimulus. The flanker effect (the ability to ignore the flanker stimuli and suppress the response tendencies they elicit) is thought to be a direct measure of cognitive control (De Fockert et al., 2001) and is indexed by the difference in response time between the incongruent and congruent conditions. Large difference scores are indicative of poorer cognitive control. Our dual task (termed as the concurrent flanker/alcohol attentional bias task, CFAAT) involved performing the Eriksen flanker task (Eriksen and Schultz, 1979) in the concurrent presence of backgrounds of neutral or alcohol-related images. A blank grey background served as a further control condition.
It was predicted that if the tendency of alcohol-related stimuli to grab attention at the expense of contextually-relevant information is mediated by an attenuation of cognitive control mechanisms, the concurrent presentation of alcohol-related background images, by comparison with neutral and grey background displays, should lead to increased reaction times under the most demanding incongruent condition of the flanker task, as participants would need to exert cognitive control to focus on the target and ignore both the task-irrelevant alcohol-related stimuli and the incongruent flankers.
Six male and 10 female university students were recruited from the University of Sussex subject pool through an advertisement asking for right-handed social drinkers [i.e. not occasional drinkers (Townshend and Duka, 2001) but individuals who drank more than 2 Units/week, every week, as determined by the Alcohol Use Questionnaire (AUQ; Mehrabian and Russell, 1978) according to which 1 Unit is equivalent to 8 g of ethanol and refers to a small glass of wine, half-a-pint of beer or a shot of spirits straight or mixed], between 18 and 35 years of age, with English as their first language. Exclusion criteria included the following: a history of psychiatric, neurological or physical conditions; under treatment for drug or alcohol dependence; and the use of any medication for any psychological or physical condition at the time of the study (excluding contraceptives, but including paracetamol). Participants were also excluded if they were regular users of cannabis or if they smoked more than 20 cigarettes daily. However, all participants were nonsmokers.
In the event that a participant was identified as drinking more than the Department of Health recommended weekly average (i.e. 14 Units for women and 21 Units for men), a leaflet outlining the effects of heavy drinking on physical and mental health was administered. This leaflet also included the available on-campus support services.
Written informed consent to take part in the study was provided by all participants, and the study was approved by the University of Sussex Ethics Committee.
Upon arrival at the experimental facilities, participants consented to taking part in the study and completed the AUQ (Mehrabian and Russell, 1978). The CFAAT followed.
The AUQ gives an estimate of the participants’ average weekly alcohol consumption over the previous 6 months. In addition to the quantity of alcohol consumed in units (taken as 1.5 Units for a glass of wine, 2.4 Units for a pint of beer/cider, 1 U per shot of spirit and 1.7 Units per bottle of ‘alcopops’), an overall score from the questionnaire can also be calculated on the basis of alcohol consumption, speed of drinking (number of drinks per hour), number of alcohol intoxications over the past 6 months, and the percentage of alcohol intoxications out of the times of going out drinking. Townshend and Duka (2002) previously demonstrated that the AUQ is a reliable measure of drinking quantity and drinking patterns.
Concurrent flanker/alcohol attentional bias task
The task involved the presentation of the classic Eriksen flanker task arrow stimuli (Eriksen and Schultz, 1979), superimposed on background, and task-unrelated displays of either alcohol-associated images or neutral images. A plain grey background was also used and served as a further control condition (see Fig. 1 for task design and an example of a single trial).
Each trial began with the presentation of a central fixation cross for a jittered duration of 850–1150 ms. This variable fixation time allowed for disparity in the intertrial interval and ensured that participants were unable to predict the onset of the stimulus display. The stimulus display was subsequently presented for 500 ms, followed by a blank 1250-ms response interval.
Each stimulus display consisted of a row of five arrows, a central ‘target’ and two flanking arrows on either side, superimposed on the centre of the background display. The participants’ task was to ignore the flankers and respond by pressing the key marked ‘L’ or the key marked ‘R’ if the middle ‘target’ arrow was pointing left or right, respectively. They were told to respond as quickly and as accurately as possible, using the index and middle fingers of their right hand. Flankers pointed either in the same direction as the ‘target’ (e.g. <<<<<; congruent condition) or in the opposite direction (e.g. <<><<; incongruent condition: bold font added only for illustration).
Each of the four possible ‘target’ and flanker combinations (‘target’ pointing left or right and flankers pointing left or right) was presented at the centre of each of a total of 60 different backgrounds. These included 20 alcohol-related images (e.g. pictures of beer cans), 20 neutral pictures, and 20 plain grey backgrounds of equal size as the pictures. Thus the entire task involved a single experimental block of 240 trials in total. The neutral pictures were matched (see Fig. 1 for examples of matching) with the alcohol-related images for perceptual complexity, the presence or absence of humans in the picture and luminance. Matching was confirmed by the ratings of four independent observers before the stimuli were used. These series of stimuli have been used in previous studies by us (Townshend and Duka, 2001, 2005) and others (Vollstadt-Klein et al., 2012). In addition, the neutral images were derived from one semantic category that would be familiar to a student population (stationery objects; e.g. dictionaries), providing a better control condition for comparison with the alcohol-related stimuli. Presentation order of each stimulus display was pseudorandomized so that the same background condition and the same congruency condition were not presented on more than three consecutive trials.
All images measured 300×400 pixels in size and were grey-scaled versions of the pictures used in a visual probe task that demonstrated that (a) heavy drinkers, in comparison with occasional social drinkers (average weekly alcohol consumption of 38 and 3 Units, respectively), show an attentional bias towards alcohol-related stimuli (Townshend and Duka, 2001) and (b) a priming dose of alcohol (relative to placebo) increases orienting responses to alcohol-related stimuli in moderate-to-heavy social drinkers (Duka and Townshend, 2004). We recorded reaction time to correct responses (latency) and accuracy of responding (% of correct responses) to the direction of the ‘target’ under both the congruent and the incongruent condition of the flanker task, in the presence of neutral, alcohol-related and plain grey backgrounds. Total task duration was 15 min and included a short practice block of 36 trials. During this practice block, participants had to reach 70% accuracy in their performance to proceed to the experimental block. Every participant reached this criterion.
For each participant, trials with reaction times of less than 200 ms and more than 1200 ms were removed from the analysis. In addition, participants were considered outliers and were excluded from the analysis if their mean response latency or response accuracy under the congruent grey background condition of the task deviated by more than two SDs from the population mean.
Kolmogorov–Smirnov tests for normality, run for latency scores (i.e. latency under the Grey_Congruent, Grey_Incongruent, Neutral_Congruent, Neutral_Incongruent, Alcohol_Congruent and Alcohol_Incongruent conditions), indicated that normality had not been violated (P=0.20 in all cases). The accuracy data, however, showed ceiling effects, probably because of the fact that all participants were required to achieve 70% accuracy during the practice block before being allowed to proceed to the experimental block. Consequently, the accuracy data (Table 1) were transformed using squared-root transformations before analyses (Fig. 2b).
For the analysis of the data derived from the CFAAT, two repeated measures 3×2 analyses of variance were computed (one for latency and one for accuracy) with background (grey background vs. neutral images vs. alcohol-related images) and flanker congruency (congruent vs. incongruent) as within-subject factors. Main effects of background were explored with paired-sample t-tests. Significant background×congruency interactions were examined using post-hoc analyses of variance, which tested for differences between the background conditions in each level of congruency separately. Subsequently, any significant simple effects were examined using Bonferroni-corrected paired-sample t-tests.
We computed the ‘flanker effect’ (FE) in the presence of alcohol-related images and in the presence of neutral images (i.e. FE_Neutral, and FE_Alcohol). It was indexed by the difference in the latency scores between the incongruent and the congruent conditions of the flanker task (i.e. latency incongruent>latency congruent; De Fockert et al., 2001). High FE scores are indicative of impaired cognitive control. We then computed the difference score between the FE_Alcohol index and the FE_Neutral index (i.e. FE_Diff=FE_Alcohol>FE_Neutral), as this represents the effect of the alcohol-related content on cognitive control. The FE_Diff score was subjected to a correlational analysis with participants’ average weekly alcohol intake (in units; as assessed using the AUQ). This correlation was used to examine whether there is a relationship between the interference evoked by the presentation of alcohol-related pictures, relative to the neutral images, on the flanker task and participants’ alcohol use.
A total of 16 volunteers completed the study. However, the data from two participants were excluded from the analysis because their mean latency or accuracy score in the congruent, grey-background condition deviated by more than two SDs from the population mean, indicating that these participants were not engaging in the task as they should have been (see Table 2 for participants’ demographic data).
There were no sex differences in the average weekly alcohol use or overall AUQ score (t<1.8, NS, in both cases).
Significantly longer latencies [main effect of background: F(2,26)=15.36, P<0.001, ηp2=0.54] were observed in the presence of the alcohol-related images by contrast to the grey and the neutral background displays (t>4.2, P<0.001, in both cases). As expected, latencies were also longer under the incongruent than under the congruent condition of the task [main effect of congruency: F(1,13)=147.42, P<0.001, ηp2=0.92].
These main effects were qualified by a significant background×congruency interaction [F(2,26)=6.98, P<0.005, ηp2=0.35; Fig. 2a]. Under the congruent condition, participants responded slower in the presence of both neutral and alcohol-related images compared with plain grey backgrounds (t>2.3, P<0.05, in both cases). By contrast, under the incongruent condition, response latencies were significantly slower when the alcohol-related pictures served as backgrounds compared with when the neutral and the plain grey displays were presented (t>3.2, P<0.01, in both cases). Thus the presence of alcohol-related (relative to the control) backgrounds led to slowed reaction time under the most demanding condition of the flanker task.
Response accuracy was higher under the grey background condition than in the presence of either neutral or alcohol-related pictures [main effect of background: F(2,26)=3.99, P<0.05, η2=0.24; simple contrasts: t>2.1, P<0.05, in both cases]. Participants were also more accurate under the congruent than under the incongruent condition of the task [main effect of congruency: F(1,13)=19.51, P<0.005, η2=0.60].
A significant background×congruency interaction [F(2,26)=3.77, P<0.05, η2=0.23; Fig. 2b] reflected more accurate responding under the grey and neutral background conditions than under the alcohol-related background condition when flankers were congruent (t>2.6, P<0.05, in both cases) but not when they were incongruent. Under the incongruent condition, response accuracy in the presence of the alcohol-related images did not differ significantly from the response accuracy in the presence of the control backgrounds (t<1.3, NS, in both cases).
The FE_Diff index (i.e. FE_Alcohol vs. FE_Neutral difference score) correlated positively with the number of alcohol units consumed per week (r=0.451; P<0.05), indicating that the more alcohol the participants consumed, the bigger the interference exerted by the alcohol-related (relative to the neutral) pictures under increased demands on cognitive control (Fig. 3).
The study was designed to examine the influence of task-irrelevant alcohol-related stimuli on cognitive control processes in nondependent social drinkers. Participants were required to exert cognitive control to suppress the processing of background, task-unrelated alcohol-associated stimuli and concentrate on a primary task – that is the Eriksen flanker task, which is thought to tap attentional control processes directly (Egner and Hirsch, 2005). It was predicted that if attentional bias to alcohol-associated stimuli is mediated by a modulation of top–down cognitive control, the concurrent presentation of alcohol-related background images, in comparison with neutral and grey background displays, should lead to increased reaction times under the most demanding incongruent condition of the flanker task, as participants would need to exert cognitive control to focus on the target and ignore both the task-irrelevant alcohol-related stimuli and the incongruent flankers.
The dual task was successful in producing interference by the task-unrelated background pictures on the primary flanker task. Performance, as indexed by both accuracy and latency, was impaired in the presence compared with the absence of the background pictures. Participants generally made more errors and responded slower in the presence of the neutral and the alcohol-related images compared with the grey background condition, and, overall, response latency and accuracy did not differ between the neutral and alcohol-related background conditions. The dual task was also successful in producing interference by the flankers, as participants responded slower and made more errors when flankers were incongruent than when they were congruent.
Consistent with our predictions and with indirect evidence suggesting a role for cognitive control processes in the mediation of drug-associated attentional bias (Goldstein et al., 2007; Hester and Garavan, 2009; Luijten et al., 2011b), it was found that under the incongruent condition of the flanker task participants responded significantly slower in the presence of alcohol-related images, relative to both neutral and grey background displays. This is suggestive of attenuation, in the presence of motivationally salient alcohol-related stimuli, of the cognitive control mechanisms necessary for focussing selective attention to the central target and ignoring the flankers. This finding supports previous data showing that incidental information with strong motivational salience can interfere with selective attention to task-relevant stimuli (Krebs et al., 2010; Anderson et al., 2011).
Importantly, the observed interference effect on response latency was not coupled with a significant difference in response accuracy. In fact, response accuracy when flankers were incongruent did not differ between the alcohol-related and the control background conditions. Thus, although participants were equally accurate at identifying the direction of the central target arrow when flankers were incongruent, it took them longer to do so in the presence of motivationally salient alcohol-related stimuli. It is possible that the accuracy scores are not a sensitive index of interference by the stimuli in nondependent social drinkers. It would be interesting to examine the task in alcoholic patients, given that in a previous study smokers, but not nonsmokers, failed to show posterror adjustments on the flanker task in the presence of smoking-related cues (Luijten et al., 2011a).
In contrast, under the congruent condition, reaction time to correct responses did not differ when alcohol-related background images, compared with neutral background images, were presented. In fact, when flankers were congruent, the presence of both neutral and alcohol-related images resulted in slower responding compared with that under the grey background condition. In addition, overall (i.e. irrespective of congruency), latencies did not differ between neutral and alcohol-related displays.
The lack of an overall difference in response latency in the presence of alcohol-associated, relative to neutral, pictures is somewhat inconsistent with previous studies using a dot-probe task, which used the same images as the ones used here to show an attentional bias to alcohol-associated stimuli in moderate and heavy social drinkers (Duka and Townshend, 2004; Townshend and Duka, 2001). A number of methodological differences, however, could account for this discrepancy. First, the images in the visual probe task are directly embedded within the primary task of responding to the position of the probe, which appears after pictures have been removed from the display. Thus participants are actively attending to these stimuli. In contrast, in the current task, the background pictures were task unrelated and served as distracters to the primary flanker task. Second, the images used here were grey-scaled versions of the images used previously. Third, participants included social drinkers whose average weekly alcohol intake was 19.7 UK units (1 U=8 g alcohol), whereas in the earlier studies (Townshend and Duka, 2001; Duka and Townshend, 2004) attentional bias was shown only in heavy alcohol drinkers with an average weekly alcohol intake of 38 U. It is possible that heavier drinkers have stronger alcohol-related associative networks and that the more frequent exposures to alcohol itself and to alcohol-associated stimuli (Stacy, 1997) could have rendered their mesolimbic dopaminergic system more sensitive to the incentive salience that these stimuli acquire (Robinson and Berridge, 2000). It is interesting therefore that the observed increase in the flanker effect in the presence of the alcohol-related images compared with neutral images (flanker effect difference score) correlated positively with the average number of alcohol units the participants consumed per week. Thus, the heavier the weekly alcohol intake, the higher the degree of interference found on cognitive control mechanisms by alcohol-associated (relative to neutral) stimuli.
Future studies should test whether light and heavy social drinkers would show differences in the interaction between the flanker task and the presence of incidental alcohol-related stimuli and whether these differences would be related to other measures of cognitive control. Further, application of EEG or functional imaging measurements, combined with behavioural measurements, would clarify the brain mechanisms involved in the interaction between the flanker task and the presence of incidental alcohol-related stimuli. Another aspect not covered in the present study is the possibility that interference was induced by an emotional response to the alcohol-related stimuli (e.g. an increase in craving; Field and Eastwood, 2005). Future studies should examine whether the interference effect derives from modulation of craving in the presence of the alcohol-related stimuli.
The present study has shown that the capture of attention by alcohol-related images in social drinkers can lead to compromised cognitive control. It remains for future research to assess whether drug (alcohol)-related stimuli-induced attentional bias is mediated by the same mechanisms governing allocation of attention to rewarding stimuli in general (e.g. Krebs et al., 2010).
This study was supported by the Medical Research Council Project Grant G0802642 to T.D. and M.F.
Conflicts of interest
There are no conflicts of interest.
Anderson BA, Laurent PA, Yantis S. Value-driven attentional capture. Proc Natl Acad Sci USA. 2011;108:10367–10371
Botvinick MM, Braver TS, Barch DM, Carter CS, Cohen JD. Conflict monitoring and cognitive control. Psychol Rev. 2001;108:624–652
Bruce G, Jones BT. A pictorial Stroop paradigm reveals an alcohol attentional bias
in heavier compared to lighter social drinkers. J Psychopharmacol. 2004;18:527–533
Cox WM, Blount JP, Rozak AM. Alcohol
abusers’ and nonabusers’ distraction by alcohol
and concern-related stimuli. Am J Drug Alcohol
Cox WM, Hogan LM, Kristian MR, Race JH. Alcohol attentional bias
as a predictor of alcohol
abusers’ treatment outcome. Drug Alcohol
De Fockert JW, Rees G, Frith CD, Lavie N. The role of working memory in visual selective attention. Science. 2001;291:1803–1806
Duka T, Townshend JM. The priming effect of alcohol
pre-load on attentional bias
-related stimuli. Psychopharmacology (Berl). 2004;176:353–361
Egner T, Hirsch J. Cognitive control mechanisms resolve conflict through cortical amplification of task-relevant information. Nat Neurosci. 2005;8:1784–1790
Eriksen CW, Schultz DW. Information processing in visual search: a continuous flow conception and experimental results. Percept Psychophys. 1979;25:249–263
Fadardi JS, Cox WM. Alcohol
and motivational structure as independent predictors of social drinkers’ alcohol
consumption. Drug Alcohol
Fassbender C, Hester R, Murphy K, Foxe JJ, Foxe DM, Garavan H. Prefrontal and midline interactions mediating behavioural control. Eur J Neurosci. 2009;29:181–187
Field M, Eastwood B. Experimental manipulation of attentional bias
increases the motivation to drink alcohol
. Psychopharmacology (Berl). 2005;183:350–357
Field M, Mogg K, Zetteler J, Bradley BP. Attentional biases for alcohol
cues in heavy and light social drinkers: the roles of initial orienting and maintained attention. Psychopharmacology (Berl). 2004;176:88–93
Field M, Schoenmakers T, Wiers RW. Cognitive processes in alcohol
binges: a review and research agenda. Curr Drug Abuse Rev. 2008;1:263–279
Franken IH. Drug craving and addiction: integrating psychological and neuropsychopharmacological approaches. Prog Neuropsychopharmacol Biol Psychiatry. 2003;27:563–579
Goldstein RZ, Tomasi D, Rajaram S, Cottone LA, Zhang L, Maloney T, et al. Role of the anterior cingulate and medial orbitofrontal cortex in processing drug cues in cocaine addiction. Neuroscience. 2007;144:1153–1159
Hester R, Garavan H. Neural mechanisms underlying drug-related cue distraction in active cocaine users. Pharmacol Biochem Behav. 2009;93:270–277
Krebs RM, Boehler CN, Woldorff MG. The influence of reward associations on conflict processing in the Stroop task. Cognition. 2010;117:341–347
Loeber S, Vollstadt-Klein S, von der Goltz C, Flor H, Mann K, Kiefer F. Attentional bias
-dependent patients: the role of chronicity and executive functioning. Addict Biol. 2009;14:194–203
Luijten M, van Meel CS, Franken IH. Diminished error processing in smokers during smoking cue exposure. Pharmacol Biochem Behav. 2011a;97:514–520
Luijten M, Veltman DJ, van den Brink W, Hester R, Field M, Smits M, et al. Neurobiological substrate of smoking-related attentional bias
. Neuroimage. 2011b;54:2374–2381
Mehrabian A, Russell JA. A questionnaire measure of habitual alcohol
use. Psychol Rep. 1978;43(3 Pt 1):803–806
Posner MI, Petersen SE. The attention system of the human
brain. Annu Rev Neurosci. 1990;13:25–42
Robinson TE, Berridge KC. The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev. 1993;18:247–291
Robinson TE, Berridge KC. The psychology and neurobiology of addiction: an incentive-sensitization view. Addiction. 2000;95(Suppl 2):S91–117
Ryan F. Detected, selected, and sometimes neglected: cognitive processing of cues in addiction. Exp Clin Psychopharmacol. 2002;10:67–76
Sharma D, Albery IP, Cook C. Selective attentional bias
related stimuli in problem drinkers and non-problem drinkers. Addiction. 2001;96:285–295
Stacy AW. Memory activation and expectancy as prospective predictors of alcohol
and marijuana use. J Abnorm Psychol. 1997;106:61–73
Stewart J. Thoughts on the interpretation of responses to drug-related stimuli. Addiction. 1999;94:344–346
Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18:643–662
Townshend JM, Duka T. Attentional bias
associated with alcohol
cues: differences between heavy and occasional social drinkers. Psychopharmacology (Berl). 2001;157:67–74
Townshend JM, Duka T. Patterns of alcohol
drinking in a population of young social drinkers: a comparison of questionnaire and diary measures. Alcohol Alcohol
Townshend JM, Duka T. Binge drinking, cognitive performance and mood in a population of young social drinkers. Alcohol
Clin Exp Res. 2005;29:317–325
Vollstadt-Klein S, Loeber S, Richter A, Kirsch M, Bach P, von der Goltz C, et al. Validating incentive salience with functional magnetic resonance imaging: association between mesolimbic cue reactivity and attentional bias
-dependent patients. Addict Biol. 2012;17:807–816
Keywords:© 2013 Lippincott Williams & Wilkins, Inc.
alcohol; attentional bias; Eriksen flanker task; human