no-go task in problem and non-problem drinkers

Addictive Behaviors 38 (2013) 2520–2528

Contents lists available at SciVerse ScienceDirect

Addictive Behaviors

Response inhibition toward alcohol-related cues using an alcohol go/no-go task in problem and non-problem drinkers Fanny Kreusch, Aurélie Vilenne, Etienne Quertemont ⁎ Faculty of Psychology, Liège University, Liège, Belgium

H I G H L I G H T S • • • •

Response inhibition to alcohol cues is studied using a modified go/no-go task. Reduced reaction times and higher false alarms are observed for alcohol cues. Problem drinkers show faster reaction times for alcohol cues without brand logos. Alcohol brand logos affect reaction times only in non-problem alcohol drinkers.

a r t i c l e Keywords: Alcohol Inhibition Alcohol-related cues Go/no-go Brand logo Cognitive bias

i n f o

a b s t r a c t Background: Previous results suggested that alcohol abusers and alcohol dependent patients show cognitive biases in the treatment of alcohol-related cues, especially approach and inhibition deficit biases. Response inhibition was often tested using the go/no-go task in which the participants had to respond as quickly as possible to a class of stimuli (go stimuli) while refraining from responding to another class of stimuli (no-go stimuli). Previous studies assessing specific response inhibition deficits in the process of alcohol-related cues obtained conflicting results. The aims of the present study were to clarify response inhibition for alcohol cues in problem and non-problem drinkers, male and female and to test the effect of alcohol brand logos. Methods: Thirty-six non-problem drinker and thirty-five problem drinker undergraduate students completed a modified alcohol go/no-go task using alcohol and neutral object pictures, with or without brand logos, as stimuli. An additional control experiment was carried out to check whether participants' awareness that the study tested their response to alcohol might have biased the results. Results: All participants, whether problem or non-problem drinkers, showed significantly shorter mean reaction times when alcohol pictures are used as go stimuli and significantly higher percentages of commission errors (false alarms) when alcohol pictures are used as no-go stimuli. Identical effects were obtained in the control experiment when participants were unaware that the study focused on alcohol. Shorter reaction times to alcohol-related cues were observed in problem drinkers relative to non-problem drinkers but only in the experimental condition with no brand logos on alcohol pictures. The addition of alcohol brand logos further reduced reaction times in light drinkers, thereby masking group differences. There was a tendency for female problem drinkers to show higher rates of false alarms for alcohol no-go stimuli, although this effect was only very close to statistical significance. Conclusions: All participants exhibited a cognitive bias in the treatment of alcohol cues that might be related to the positive emotional value of such alcohol-related cues. Stronger cognitive biases in the treatment of alcohol cues were observed in problem drinkers, although differences between problem and non-problem drinkers were relatively small-scale and required specific experimental parameters to be uncovered. In particular, the presence of alcohol brand logos on visual alcohol cues was an important experimental parameter that significantly affected behavioral responses to such stimuli. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction ⁎ Corresponding author at: Centre de Neuroscience Cognitive et Comportementale, Université de Liège, Boulevard du rectorat 5/B32, 4000 Liège, Belgium. Tel.: +32 4 366 21 05; fax: +32 4 366 28 59. E-mail address: [email protected] (E. Quertemont). 0306-4603/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.addbeh.2013.04.007

Alcohol dependence is characterized by alcohol craving and consumption despite destructive consequences on physical, social and occupational fields (Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition, DSM-IV, American Psychiatric Association,

F. Kreusch et al. / Addictive Behaviors 38 (2013) 2520–2528

1994; Lowman, Hunt, Litten, & Drummond, 2000). Several recent models of the development of alcohol dependence refer to dualprocess theories suggesting that addictive behaviors are the result of an imbalance between two neurocognitive systems (Deutsch & Strack, 2006; Wiers & Stacy, 2006; Wiers et al., 2007). On one hand, an appetitive system triggers automatic and fast appetitive responses to alcohol-related cues. According to the model, this system might become sensitized following chronic alcohol consumption (Robinson & Berridge, 1993). On the other hand, a conscious executive system is supposed to regulate consumption behaviors, but this system is compromised after regular alcohol abuse (Parsons, 1998). As a consequence, when alcohol abusers are confronted with alcohol-related cues, there is an automatic activation of the appetitive system, leading to approach behaviors toward alcohol and alcohol craving (Wiers et al., 2007). As the inhibitory system is compromised, their ability to inhibit these responses is reduced, which leads to an apparent compulsive use of alcohol (Wiers et al., 2007). Regarding the inhibitory system, many published studies showed a reduced general ability of inhibition in alcoholics. For example, they have difficulties delaying gratification (Bickel & Marsch, 2001; Bjork, Hommer, Grant, & Danube, 2004), they are less efficient in the manipulation of information in working memory (Bechara & Martin, 2004; Noël et al., 2001), they show an impaired response inhibition (Noël et al., 2001; Zago-Gomes & Nakamura-Palacios, 2009) and they have a poorer capacity of flexibility (Ratti, Bo, Giardini, & Soragna, 2002). Some results suggest that a weak response inhibition might precede the development of alcohol dependence. For example, poor inhibition performances were reported in children from families at high risk for developing alcoholism (Hill, Lowers, Locke, Snidman, & Kagan, 1999; Sher et al., 1991) and in untreated social drinkers (Montgomery, Fisk, Murphy, Ryland, & Hilton, 2012). Furthermore, inhibition capacities during childhood were shown to predict future drinking problems (Nigg et al., 2006). While inhibition deficits might be a cause or a consequence of alcohol abuse (or even both of them together), it is clear that they contribute to maintain alcohol abuse. Chronic alcohol consumption not only impairs executive functions but also leads to an automatic tendency of the appetitive system to process alcohol-related cues. This has been demonstrated using several experimental paradigms and especially by testing attentional biases for alcohol-related cues. Several studies demonstrated that alcohol abusers and alcohol dependent patients show such attentional biases (for review see Field & Cox, 2008). For example, heavy drinkers are slower than light drinkers to name the colors of alcohol-related words in a modified-Stroop paradigm (Field, Christiansen, Cole, & Goudie, 2007). This suggests that heavy drinkers have difficulties disengaging their attention from these stimuli. Using a visual dotprobe paradigm, Townshend and Duka (2001) also show that heavy drinkers are faster to detect a target when displayed behind an alcohol-related picture. By manipulating the time interval between the presentations of the alcohol-related picture and the target in the visual dot-probe task, Noël et al. (2006) found an attentional bias for alcohol-related cues in detoxified alcoholic patients with a 50 ms time interval, but not with 500 ms. This suggests that detoxified alcoholic patients are characterized by an initial orientation bias toward alcohol-related cues. In addition to attentional biases, approach biases for alcohol-related stimuli were also reported in heavy drinkers. Using approach/avoidance tasks, two studies showed that heavy drinkers are faster to provide approach responses toward alcohol-related pictures relative to avoid responses (Field, Kiernan, Eastwood, & Child, 2008; Wiers, Rinck, Dictus, & van den Wildenberg, 2009). Taken together, these results support the theory that alcohol-related cues are processed in a relatively automatic and spontaneous way in heavy drinkers. With the addition of the inhibition deficits reported above, it was hypothesized that alcohol-related cues capture attention and elicit approach behaviors in alcohol abusers, eventually leading to uncontrolled alcohol consumption.

2521

Whereas a number of studies have investigated general inhibition functions in social drinkers and alcohol dependent patients, fewer studies have tested the response inhibition specifically to alcoholrelated cues. As alcohol abusers show general inhibition deficits together with attentional and approach biases for alcohol-related cues, the strongest response inhibition deficits might be expected for such specific stimuli in those subjects. Specific inhibition responses for alcohol-related cues were mainly tested using either the go/no-go or the stop signal tasks. In the go/no go task, the participants have to respond as quickly as possible to a class of stimuli (go stimuli) while refraining from responding to another class of stimuli (no-go stimuli). In the stop signal task, participants have to refrain a highly trained response when the response cue is followed by a specific stop signal. However, it is currently difficult to draw a clear conclusion from such studies using alcohol-related cues, as they have used different methodological parameters and reported conflicting results. Using a go/no go task with alcohol-related words, Noël et al. (2007) showed that both alcoholic patients and control participants were faster to respond when go stimuli are alcohol-related words as opposed to neutral words. However, alcoholic patients made significantly more commission and omission errors, which might be interpreted as a general response inhibition deficit in recently detoxified alcoholics. Although Noël and colleagues reported their results as an evidence of higher inhibition deficits specifically for alcohol-related words in alcoholic patients, such an interpretation was contested (Field & Cole, 2007). To test for potential differences in response inhibition toward alcohol-related cues in heavy and light drinkers, Nederkoorn, Baltus, Guerrieri, and Wiers (2009) used a modified stop signal task. Although they found no differences between heavy and light drinkers in response inhibition toward alcohol-related pictures, they observed stronger inhibition deficits in heavy drinking women but this effect was not specific to alcohol-related cues. Other studies found conflicting results regarding response inhibition toward alcohol-related cues. In a study from Rose and Duka (2008) using alcohol pictures in moderateto-heavy drinkers, slower responses were found for alcohol-related cues. In contrast, Adams, Ataya, Attwood, and Munafò (2013) showed faster responses to alcohol-related cues, although no clear differences were observed between heavy and light social alcohol users. In these two latter studies, the effects of alcohol administration were also different. In the study from Rose and Duka (2008), the administration of a moderate alcohol dose had no specific effect on response inhibition to alcohol cues in a go/no-go task, whereas alcohol administration reduced response inhibition, specifically to lexical alcohol-related cues (words), in the study from Adams et al. (2013). It is therefore difficult to draw clear conclusions from these few studies. It is however possible to point to some experimental parameters likely to affect the results. First, the alcohol consumption status of the participants is likely to play a role. Whereas a specific inhibition bias for alcohol-related cues was reported in alcohol dependent patients (Noël et al., 2007), no differences between light and heavy drinkers were found to date (Adams et al., 2013; Nederkoorn et al., 2009). However, differences between light and heavy drinkers are expected on theoretical grounds, as cognitive biases toward alcohol-related cues are expected to slowly develop with heavy alcohol consumption. Considering the conflicting nature of the results of previous studies, differences between problem and non-problem drinkers should be retested using different experimental parameters. Gender is another important parameter as stronger general inhibition deficits were observed in heavy drinking women relative to men (Nederkoorn et al., 2009). Finally, the type of alcoholrelated cues might also seriously affect the results. In the study from Adams et al. (2013), specific response inhibition deficits were recorded when alcohol-related words were used as targets, but not with alcohol pictures. In the present study we tested response inhibition toward alcoholrelated cues in problem and non-problem drinkers, men and women using a modified alcohol go/no-go task. Alcohol pictures were used

2522

F. Kreusch et al. / Addictive Behaviors 38 (2013) 2520–2528

as stimuli instead of alcohol words because such stimuli evoke better reward-related responses and are more ecological (Bruce & Jones, 2004; Townshend & Duka, 2001). When using alcohol pictures, one critical methodological parameter is the choice of including or not the brand logos of the alcoholic beverages. On one hand, the presence of brand logos is more ecological. Brand logos are an intrinsic part of the compound stimuli that are associated with alcohol consumption. As brand logos are designed to be easily discriminable and to capture attention, they might even be the most prominent part of the alcohol cues for drinkers. Adding brand logos might therefore enhance the studied cognitive biases. But on the other hand, each individual consumer has its own preference for alcoholic beverages and the specific brand logos that are used in a study may not be appropriate for all participants, leading to an increased intra-group variability. Additionally, brand logos are expected to facilitate the discrimination task between alcohol and neutral pictures, which might reduce and therefore mask group differences. Unfortunately, there is little data in the literature on the effects of brand logos in alcohol studies. Most studies on the physiological, cognitive, and neural responses to alcohol-related stimuli do not mention whether brand logos are displayed on the alcohol beverage pictures (e.g. Field, Mogg, Zetteler, & Bradley, 2004; Petit et al., 2012; Tapert et al., 2003). In the present study, two experimental conditions, with or without brand logos, were therefore compared in order to see whether they affect participants' responses. The first aim of the study was to test for differences between problem and non-problem drinkers in a modified go/no go task. We expected stronger cognitive biases specifically for alcohol-related pictures in problem drinkers as evidenced either by shorter reaction times for alcohol go stimuli or by a higher percentage of commission error for alcohol no-go stimuli. The second aim of the study was to test for the addition of brand logos on the picture stimuli of the alcohol modified go/no-go task. We hypothesized that alcohol brand logos would enhance cognitive biases for alcohol pictures. Finally, the third aim was to test for differences between male and female problem drinkers. On the basis of previously published studies, we expected higher cognitive biases for alcohol-related cues in female problem drinkers than in male problem drinkers. In the second part of the study, a control experiment was carried out in order to check whether the instructions given to the participants and their awareness that the study focuses on alcohol responses did not bias the results. A slightly different procedure was developed to avoid any reference to alcohol in the presentation of the study and the instructions given to the participants before the go/no-go task.

2. Experiment 1 2.1. Method 2.1.1. Participants Undergraduate students (40 women and 35 men) from the University of Liège were recruited for the study. They were first invited to fill the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). On the basis of their AUDIT score, they were classified as problem or non-problem drinkers. Additionally, only students who consumed at least one drink per week were included in the light drinker group. An appointment for the experiment was made with participants meeting these inclusion criteria. Some participants were excluded from the sample a posteriori on the basis of their drug consumption or the presence of psychiatric symptoms as evidenced by the SCL-90 (Derogatis, 1977), which was administered at the end of the behavioral task. For the SCL-90, exclusion criteria were scores of 70 or higher on any subscale. Regular use of other psychoactive substances (except nicotine) was also used as an exclusion criterion. One male who reported serious head injuries during childhood and three males who reported regular use of cannabis were

excluded from the study. This led to a final sample of 71 participants. Demographic data are presented in Table 1. 2.1.2. Measures 2.1.2.1. Barratt Impulsiveness Scale version 11 (BIS-11; Patton, Stanford, & Barratt, 1995). The French version of the BIS-11 is a self-reported questionnaire which consists of 30 items assessing trait impulsivity (Baylé et al., unpublished). Each item is reported on a four-point scale. Scores range from 30 to 120 with higher scores indicating high impulsivity. The BIS-11 is divided into three factors: motor impulsiveness, cognitive impulsiveness and non-planning impulsiveness. As impulsivity might affect responses in the go/no-go task, the scores on the BIS-11 were recorded for use as a potential covariate. However, as there were no differences between problem and non-problem drinkers on none of the BIS-11 subscales, it was finally not used in covariance analyses. 2.1.2.2. Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993). The AUDIT questionnaire includes ten multiple-choice items measuring alcohol consumption (questions one to three), dependence (questions four to six) and alcohol-related problems (questions seven to ten). Questions one to eight are scored from zero to four and questions nine and ten are scored zero, two or four. The maximum score on the AUDIT is 40. In the present study, a cut-off score of 11 was set for the recruitment of problem drinkers (Fleming, Barry, & MacDonald, 1991) and cut-off score of maximum 7 for non-problem drinkers, who are usually qualified as low risk drinkers (Saunders et al., 1993). 2.1.2.3. Alcohol consumption. Alcohol use was assessed with a selfreported measure based on the timeline follow-back method (Sobell & Sobell, 1990). Participants reported how many standard alcohol drinks (= 10 g) they had consumed during the previous week by indicating how many alcoholic drinks they had drunk on each day. Furthermore, the number of days they drank more than six drinks of alcohol (= 60 g) on a single occasion during the past two weeks was recorded (Wiers, Hoogeveen, Sergeant, & Gunning, 1997). 2.1.2.4. Craving. Craving was assessed before and after the go/no-go task with a 100 mm Visual Analogue Scale. Participants had to indicate how much they wanted to drink at the moment on a scale ranging from “absolutely no urge” (score of 0) to “an irresistible urge” (score of 100) (van den Wildenberg et al., 2007). 2.1.2.5. Drug consumption. The use of eleven drugs was also assessed: tobacco, Cannabis, amphetamines, XTC, cocaine, hallucinogenic drugs, magic mushrooms, sedatives, opiates, volatile solvents and other club drugs. For each drug, participants had to choose between “never Table 1 Demographic table (mean and SD).

Age Gender (male/female) Educational level SCL-90 Somatization Obsessive–compulsive Interpersonal sensitivity Depression Anxiety Hostility Phobic anxiety Paranoid ideation Psychoticism

Non-problem drinkers (n = 36)

Problem drinkers (n = 35)

21.1 (2.3) 12/24 14.1 (1.9)

21.2 (2.4) 19/16 14 (1.9)

53 (3.5) 55.8 (5) 55.4 (4.4) 55.1 (4.7) 53.9 (3.8) 53.2 (3.4) 51.6 (3.6) 55.6 (5.2) 51.6 (2.3)

52.9 (3.8) 55.5 (3.4) 55 (3.6) 55.4 (3.9) 53.6 (3.7) 55.1 (4.9) 51.4 (2.4) 54.5 (3.8) 52.1 (2.5)

F. Kreusch et al. / Addictive Behaviors 38 (2013) 2520–2528

2523

used”, “one or two uses”, “several uses” or “regular uses” (van den Wildenberg et al., 2007).

The whole task took approximately 8 min and was performed with E-Prime software (Psychology software tools, Inc.).

2.1.2.6. Symptom Checklist-90—Revised (SCL-90-R, Derogatis, 1977). The French version of the SCL-90-R (Fortin & Coutu-Wakulczyk, 1985) is a self-administered psychopathological assessment questionnaire. The SCL-90-R includes nine subscales: somatization, obsessive– compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation and psychoticism. It includes 90 items with five alternatives for each item ranging from 0 (none) to 4 (very much). The SCL-90-R scores are converted to standard T-scores.

2.1.4. Design and procedure Participants were tested one by one in a small and quiet room. They first signed a consent form prior to their participation and filled some of the questionnaires, i.e. demographic data, alcohol and drug consumption questionnaires and pretest craving. Then, they started the modified go/no-go task. At the end of the task, they filled the rest of the questionnaires: posttest craving, BIS-11 and SCL-90. The procedure was approved by the Ethical Committee of the Psychology Faculty from the University of Liège.

2.1.3. Go/no-go modified for alcohol Response inhibition toward alcohol cues was recorded through a modified version of the classical go/no-go task. The modification involved the use of alcohol-related and neutral pictures to specifically test inhibition capacities toward alcohol-related cues. In this task, several pictures are presented successively to the participants. Participants had to respond when a go stimulus is presented (go trial) and to refrain from responding when a no-go stimulus is presented (no-go trial). Pictures of alcoholic drinks and neutral objects were included in the task. Additionally, the same pictures were presented either with clearly visible brand logos or without these brand logos in independent blocks. The pictures were selected on the basis of the results of an unpublished pilot study among 84 candidate stimuli from an internet database (www.istockphoto.com and other commercial websites). This pilot study involved 35 participants (different from the participants of the present study) and assessed the discriminability of the pictures with and without brand logos. On that basis, 15 pictures were selected for each category (alcoholic drinks with and without brand logos; neutral objects with and without brand logos). The selected alcohol pictures included bottles of beer and wine, glasses of beer, wine, liquor and cocktails. The selected neutral object pictures included objects typically used in the office, such as pen, ruler, stapler, computer mouse, usb stick, etc. In experiment 1, two picture combinations were administered: 1) alcoholic drink pictures as go stimuli vs. neutral object pictures as no-go stimuli and 2) neutral object pictures as go stimuli vs. alcoholic drink as no-go stimuli. These combinations were successively presented using pictures with or without brand logos. Moreover, all participants went through each block twice with a counterbalanced order of the type of go stimuli. Finally, this procedure led to the administration of 8 blocks to each participant. The block order was counterbalanced across participants. Before the start of each block, an instruction indicated to which type of stimuli the participant had to respond, i.e. which type of stimuli was the go stimulus. Each block included 15 go trials and 5 no-go trials. When a go stimulus was presented, participants had to press the space bar of a keyboard as fast and as accurately as possible. Participants first completed one practice block, which was similar to the test blocks (20 trials). The pictures were presented in the center of the screen during 500 milliseconds (ms), with an inter-stimulus interval of 1000 ms.

2.1.5. Data analysis Dependent variables of the study were the mean reaction times (RTs) on go trials and the percentages of false alarm errors, defined as a response on a no-go trial, on which the participant was instructed to refrain from responding. Participant reaction times on go trials and percentages of false alarm errors were analyzed with 2 (logo presence) × 2 (picture type) × 2 (gender) × 2 (drinking group) mixed analyses of variance (ANOVA). These ANOVAs used gender and drinking group as between-subject variables and picture type and logo presence as within-subject variables. Effect sizes were calculated as partial eta-squared (ηp2). Tukey post-hoc analyses were used in case of significant interactions or main effects. The reaction time raw data were normalized with logarithmic transformations prior to the analyses in order to meet the assumptions of homogeneity of variances and normality. However, for the sake of clarity, means of the raw values are presented in the figures. All the statistical analyses were performed using the software package Statistica 9 (StatSoft, Inc., Maisons-Alfort, France). 2.2. Results 2.2.1. Group characteristics Table 2 summarizes the results for the alcohol-related variables and the subscales of impulsivity on the BIS-11 questionnaire for problem and non-problem drinkers, men and women. Problem and nonproblem drinkers showed significant differences in units of alcohol consumed on the previous week (t(38) = 6.49; p b 0.0001 for female; t(29) = 6.46; p b 0.0001 for male), in mean AUDIT score (t(38) = 10.84; p b 0.0001 for female; t(29) = 11.25; p b 0.0001 for male) and in number of days they drank more than 6 drinks on the two previous weeks (t(38) = 7.92; p b 0.0001 for female; t(29) = 6.11; p b 0.0001 for male). In contrast, the groups showed no significant differences for motor impulsiveness (t(38) = 0.38; NS for female; t(29) = 1.16; NS for male), cognitive impulsiveness (t(38) = 1.42; NS for female; t(29) = 2.02; NS for male) and nonplanning impulsiveness (t(38) = 1.41; NS for female; t(29) = 1.50; NS for male). Finally, there was a significant difference on mean craving scores (before and after the task) between the two groups (t(69) = 4.45; p b 0.0001). Problem drinkers showed higher levels of craving compared to non-problem drinkers. Moreover, craving levels

Table 2 Group characteristics (mean and SD). Female non-problem drinkers (n = 24) Alcohol use (units/last week) AUDIT ≥6 units in one occasion/last two weeks BIS-11 Motor impulsiveness Cognitive impulsiveness Non-planning impulsiveness

3.04 (2.58) 3.30 (1.89) 0.33 (0.70) 16.87 (4.03) 19.46 (3.11) 22.50 (4.38)

⁎ Significantly different from light drinkers of the same gender at p b 0.0001.

Female problem drinkers (n = 16) 20.62 (12.98) ⁎ 15.19 (4.76) ⁎ 3 (1.41) ⁎ 17.31 (2.49) 20.94 (3.39) 24.5 (4.39)

Male non-problem drinkers (n = 12) 1.08 (1.31) 3.42 (2.06) 0.08 (0.29) 16.17 (3.74) 18.42 (3.17) 22.08 (2.99)

Male problem drinkers (n = 19) 25.47 (12.95) ⁎ 17.68 (4.06) ⁎ 4.05 (2.22) ⁎ 17.42 (2.29) 21.00 (3.64) 24.68 (5.47)

F. Kreusch et al. / Addictive Behaviors 38 (2013) 2520–2528

significantly increased after administration of the go/no-go task in problem drinkers (t(34) = −3.3; p = 0.002) but not in non-problem drinkers (t(35) = −1.73; NS).

Mean percentage of false alarm error on no-go trials

2524

Non-problem drinkers

Problem drinkers

*** 30

2.2.2. Go/no-go reaction times on go trials The ANOVA on mean RTs revealed a significant main effect of picture type (F(1,67) = 59.2; p b 0.0001; ηp2 = 0.47): participants had shorter latencies for alcohol-related stimuli. There was also a significant main effect of the presence of the brand logo (F(1,67) = 14.5; p = 0.0003; ηp2 = 0.18). RTs were shorter in the presence of brand logos. Furthermore, there were significant interactions gender × logo presence (F(1,67) = 15.9; p = 0.0001; ηp2 = 0.19) and group × logo presence (F(1,67) = 6; p = 0.01; ηp2 = 0.08). Tukey post-hoc tests indicated a statistically significant effect of the presence of the brand logo in men (p = 0.0002) and in non-problem drinkers (p = 0.01) who demonstrated slower responses in the absence of logos. The second-order interaction group × picture type × logo presence was also significant (F(1,67) = 7.9; p = 0.006; ηp2 = 0.10). Post-hoc analysis reveals that non-problem drinkers show significantly slower responses for alcohol in the absence of the brand logo relative to the condition where brand logos are present (p = 0.01). This difference is not observed in problem drinkers (p = 0.99) (Fig. 1).

Fig. 2. Mean percentage of false alarm errors (SEM) for neutral object and alcoholic drink stimuli, for male and female problem and non-problem drinkers. *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001.

2.2.3. Go/no-go false alarm errors on no-go trials The ANOVA on mean percentages of false alarms showed a significant main effect of picture type (F(1,67) = 10.64; p = 0.001; ηp2 = 0.14), with a higher number of false alarms for alcohol stimuli. In contrast, main effects for group, gender and logo presence were not statistically significant. The interaction group × gender × picture type was very close to statistical significance (F(1,67) = 3.91; p = 0.052; ηp2 = 0.06). Female problem drinkers showed a higher percentage of false alarms for alcoholic drinks relative to the other groups (Fig. 2).

for their levels of anxiety using the State-Trait Anxiety Inventory (STAI, Spielberger, 1983). Three participants with high to severe anxiety (scores of 55 or higher on the STAI) were excluded from the study. The final sample included 66 participants (mean age of 20.04 ± 2.24; range of 17–26 years). The participants were randomly assigned to one of the two conditions: “aware of the purpose of the study” or “unaware of the purpose of the study” (n = 33/condition; 15 females and 18 males in the unaware condition; 16 females and 17 males in the aware condition).

3. Experiment 2

3.1.2. Measures The same questionnaires as in study 1 were administered: Barratt Impulsiveness Scale version 11 (BIS-11; Patton et al., 1995), Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993), follow-back alcohol consumption and drug consumption. However, in order to shorten the study and because anxiety is the most often encountered psychopathological disorder in students, the SCL-90 was replaced by the STAI (Spielberger, 1983) to check the levels of anxiety. The Approach and Avoidance of Alcohol Questionnaire (AAAQ, McEvoy, Stritzke, French, Lang, & Ketterman, 2004) was also included.

3.1. Method 3.1.1. Participants Eighty-one undergraduate students (40 women and 41 men) were recruited for the study within the confines of the University of Liège. Some participants were excluded from the sample a posteriori on the basis of the following exclusion criteria. Twelve participants were excluded from the study because they did not consume at least one alcohol drink per week. Participants were also screened

Reaction time on Go trials (in ms)

500

Non-problem drinkers

Problem drinkers

480

***

460

** 440 420 400 380 360 Logo absence Logo presence Logo absence Logo presence

Neutral object

Alcoholic drink

Fig. 1. Mean RTs (SEM) for neutral object and alcoholic drink stimuli, with or without brand logos, for problem and non-problem drinkers. *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001.

20

10

0 Female

Male

Neutral object

Female

Male

Alcoholic drink

3.1.2.1. The State-Trait Anxiety Inventory for adults form Y (STAI form Y, Spielberger, 1983). The STAI is a self-report questionnaire used to assess immediate and general anxiety. The STAI form Y consists of 40 questions divided into two groups of 20 questions: the first group measuring anxiety as a state (how people feel at a particular moment in time) and the second group measuring the anxiety as a trait (how people generally feel, independently of the situation). For the 20 questions about anxiety state, participants had to rate on a 4-point intensity scale ranging from not at all to very much so and for the 20 questions about anxiety trait they rated on a 4-point frequency scale ranging from almost never to almost always. The total score is therefore between 20 and 80 for each group of questions. 3.1.2.2. The Approach and Avoidance of Alcohol Questionnaire (AAAQ, McEvoy et al., 2004). The AAAQ is a 20-item self-report questionnaire developed to assess both inclinations to drink and inclinations to not drink. Participants had to indicate the extent to which they agree or not with each item on a 9-point scale ranging from “not at all” (0) to “very strongly” (8). Items 0, 2, 4, 8 and 13 refer to average urge to drink alcohol. Items 3, 7, 10 and 11 refer to compulsive urge to drink alcohol and the other items refer to alcohol avoidance.

F. Kreusch et al. / Addictive Behaviors 38 (2013) 2520–2528

3.1.3. Design and procedure Participants were first invited to sign a consent form prior to their participation. They were then randomly assigned to one of two experimental conditions. In the “aware of the purpose of the study” condition, participants were explicitly invited to take part to a study whose purpose was to investigate their reactions to alcohol stimuli. They started the study by filling questionnaires about their alcohol consumption (AUDIT, follow-back alcohol consumption and drug consumption) and the STAI. At the end of the go/no-go task, participants filled the AAAQ and the BIS-11. In the “unaware of the purpose of the study” condition, no reference was made to alcohol before the go/no-go task. Therefore, the alcohol questionnaires were deferred after the go/no-go task. Participants were invited to take part to a study whose purpose was to investigate concentration and reaction times. They started the study by filling the BIS-11 and the STAI. After completion of the go/no-go task, two questions were asked to check the experimental manipulation. First, participants were asked what was the purpose of the study and at what time they discovered that purpose. Then the real purpose of the study was explained to the participants and they were asked again if they had guessed that the study was related to alcohol (yes/no). Three of participants in the “unaware condition” were excluded from the analyses because they mentioned a study on alcohol in their answer to the first question. Eight participants also answered “yes” at the second question, but were not excluded from the analyses because these were clearly

post-hoc answers. However, it is noteworthy that removal of these subjects from the analyses does not significantly alter the results. Finally, after debriefing, the participants filled the questionnaires on alcohol consumption (AUDIT, follow-back alcohol consumption and drug consumption) and the AAAQ. 3.1.4. Data analysis Reaction times on go trials and percentages of false alarms on no-go trials were analyzed with a 3 (picture type: alcohol, soft or neutral) × 2 (condition) mixed ANOVA. In the second part of the study, a 2 (picture type) × 2 (condition) mixed ANOVA was used to analyze the results of the go/no-go task using alcoholic drinks vs. neutral objects. In these ANOVAs, the condition (aware vs. unaware of the purpose) was included as a between-subject variable and picture type as a within-subject variable. Effect sizes were calculated as partial eta-squared (ηp2). Tukey post-hoc analyses were used in case of significant interactions or main effects. The reaction time raw data were normalized with logarithmic transformations prior to the analyses in order to meet the assumptions of homogeneity of variances and normality. 3.2. Results 3.2.1. Group characteristics Differences between the experimental groups (aware or unaware of the purpose of the study) on units of alcohol consumed on the previous week (t(61) = − 0.50; NS), on the AUDIT score (t(61) = 1.19; NS) and on the mean number of days they drank more than 6 drinks (t(61) = 0.37; NS) were not statistically significant. Furthermore, the groups did not differ on any of the impulsivity scales (motor impulsiveness (t(61) = − 1.30; NS), cognitive impulsiveness (t(61) = − 0.09; NS) and non-planning impulsiveness (t(61) = − 0.68; NS)), nor on the three sub-scales of craving (inclined/indulgent factor (t(61) = 0.45; NS), obsessed/compelled factor (t(61) = 0.67; NS) and resolved/regulated factor (t(61) = 0.26; NS)). 3.2.2. Go/no-go reaction times on go trials In the first part of experiment 2, the two-way ANOVA computed on reaction times revealed a significant main effect of picture

500 Aware of the purpose 480

Reaction time on Go trials (in ms)

3.1.2.3. Go/no-go modified for alcohol. As the results of experiment 1 showed that all participants, whether problem or non-problem drinkers, showed reduced mean reaction times and a higher rate of false alarms for alcohol stimuli relative to neutral stimuli, experiment 2 tested whether this effect might be due to participants' awareness that the purpose of the study was to investigate their alcohol-related behaviors. Indeed, this might act as an alcohol prime that direct attention to alcohol cues, thereby affecting participants' behaviors, similarly to what was reported in tobacco dependence (Earp, Dill, Harris, Ackerman, & Bargh, in press). The go/no-go task from experiment 1 was adapted to the purpose of experiment 2. The same pictures (except from the addition of soft drink stimuli), inter-stimulus intervals, time presentation and instruction procedure were used. However, experiment 2 included only pictures without brand logos. The go/no-go task was divided into two consecutive parts. In the first part, in order to prevent participants from guessing that the study specifically focused on alcohol, alcohol and soft drink pictures were combined in a single drink stimulus category. Two picture combinations were administered in separate blocks in the first part of experiment 2: 1) drinks as go stimuli vs. neutral objects as no-go stimuli and 2) neutral objects as go stimuli vs. drinks as no-go stimuli. The order of the blocks was counterbalanced across participants. In order to have a sufficient number of alcohol and soft drinks, the two blocks included 60 go trials and 20 no-go trials. In this way, there were 10 alcohol stimuli and 10 soft stimuli in the no-go trials. Although combined in the same trials, alcohol and soft stimuli were dissociated in the statistical analysis in order to test whether shorter reaction times for alcohols relative to neutral objects persisted in such conditions. The second part of experiment 2 included two picture combinations exactly similar to those used in the experiment 1, with 15 go trials and 5 no-go trials: 1) alcoholic drinks as go stimuli vs. neutral objects as no-go stimuli and 2) neutral objects as go stimuli vs. alcoholic drinks as no-go stimuli. The block order was counterbalanced across participants. At the beginning of the go/no-go task, all participants started with a practice block (20 trials) with garden items and office supplies. The choice of such images, not related to alcohol, early in the task helped to ensure that participants did not guess the purpose of the study. The task lasted approximately 7 min and was performed with E-Prime software (Psychology software tools, Inc.).

2525

Unaware of the purpose

460

*** 440

420

400

380

360 Neutral object

Alcoholic drink

Fig. 3. Mean RTs (SEM) for neutral object and alcoholic drink stimuli, for the group aware and unaware of the purpose of the study. *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001.

2526

F. Kreusch et al. / Addictive Behaviors 38 (2013) 2520–2528

type ((F(2;122) = 18.2; p b 0.0001; ηp2 = 0.23), whereas there was no significant main effect of the experimental condition (F(1,61) = 0.1; NS) and no interaction experimental condition × picture type (F(2,122) = 0.1; NS). Post-hoc analyses confirmed faster responses for alcohol drinks (p b 0.0001) relative to neutral objects, even when the participants were unaware of the purpose of the study. Similar results were obtained in the second part of experiment 2. There was a significant main effect of picture type (F(1,61) = 23.5; p b 0.0001; ηp2 = 0.28), but no significant main effect of the experimental condition (F(1,61) = 0.2; NS) and no significant interaction (F(1,61) = 0.1; NS). Once again, significantly faster responses were observed for alcoholic pictures, even in the “unaware” condition (Fig. 3). 3.2.3. Go/no-go false alarm errors on no-go trials In the first part of experiment 2, the two-way ANOVA computed on the percentages of false alarm errors in no-go trials revealed a significant main effect of picture type (F(2;122) = 14.76; p b 0.0001; ηp2 = 0.19), but no effect of the experimental condition (F(1,61) = 0.45; NS) and no significant interaction (F(2,122) = 1.32; NS). Similar results were also obtained in the second part of the experiment with a significant main effect of picture type (F(1,61) = 24.18; p b 0.0001; ηp2 = 0.28), but no significant main effect of condition (F(1,61) = 0.01; NS) or interaction (F(1,61) = 0.49; NS). In both parts of the experiment, the post-hoc analyses showed a higher percentage of false alarms for alcoholic drinks (p b 0.0001) relative to neutral objects. The higher percentage of false alarms for alcoholic drinks was observed even when the participants were unaware of the purpose of the study. 4. Discussion The aim of the present study was to test response inhibition toward alcohol-related cues using a modified alcohol go/no-go task in problem and non-problem drinkers, men and women, taking into account the presence or absence of the brand logo on the pictures. All participants, whether they report problematic levels of alcohol consumption or not, showed a cognitive bias toward alcohol cues as evidenced by significantly shorter reaction times when alcohol pictures are go stimuli and a higher percentage of false alarm errors when alcohol pictures are no-go stimuli. Additionally, the present results show that the inclusion of brand logos on alcohol pictures is an important experimental parameter as differences between problem and non-problem drinkers were found only in the absence of brand logos. The present results show a significant cognitive bias in the treatment of alcohol cues relative to control stimuli in all participants, including non-problem drinkers. This cognitive bias may be interpreted as an approach bias and/or a reduced capacity of response inhibition for these stimuli. In experiment 2, we tested whether the shorter reaction times and the higher percentages of false alarms for alcohol stimuli are not simply biased by the experimental context. Indeed, the participants of experiment 1 were recruited to be involved in a study on alcohol. They first had to fill a number of questionnaires on their alcohol consumption. Therefore, they were well aware that alcohol cues were critical for the topic of the study. It was therefore possible that the experimental context served as a kind of prime for alcohol responses or even that this awareness biased the results. However, the results from experiment 2 confirm shorter reaction times and higher percentages of false alarms for alcohol cues. They also show no differences between the condition “aware of the purpose of the study” and the condition “unaware of the purpose of the study” for either the mean reaction times or the percentages of false alarms. Therefore, the “alcohol” experimental context cannot explain the observed bias in the treatment of alcohol cues. Another possible explanation for this observation is the higher emotional value of alcohol stimuli relative to neutral stimuli. Several studies have tested the effects of emotional stimuli in go/ no-go tasks. When emotional stimuli are used as distractors, higher

rates of false alarms and slower go responses are observed (De Houwer & Tibboel, 2010), which suggests that emotional distractors draw attention away from the primary task. Other previous studies have reported faster go responses when positive emotional stimuli are used as go targets, relative to neutral or negative stimuli (Hare, Tottenham, Davidson, Glover, & Casey, 2005, Hare et al., 2008; Schulz et al., 2007). As alcohol is a rewarding drug and is likely to be associated with positive affects and experiences in student participants from a Belgian University, this might explain why a cognitive bias is observed even in light drinkers. Further studies could help to clarify this question. One of the hypotheses of the present study was that problem drinkers would show stronger cognitive bias in the processing of alcohol cues, as evidenced by shorter reaction times and/or higher rates of false alarms. The results support this hypothesis, but also show that such a specific bias is difficult to obtain and is highly dependent upon the experimental parameters. In problem drinkers, shorter reaction times were observed relative to non-problem drinkers but only in the experimental condition with no brand logos on the alcohol pictures. The addition of brand alcohol logos did not further decrease reaction times in problem drinkers, whereas it significantly reduced reaction times in non-problem drinkers. One possible explanation to these latter results is that the alcohol bias already reached a floor effect in problem drinkers without brand logos. In non-problem drinkers, the addition of alcohol brand logos further decreased reaction times and therefore masked the group differences. It is also noteworthy that the addition of brand logos on neutral objects did not significantly affect reaction times when they served as go stimuli (see Fig. 1). Furthermore, the addition of brand logos did not affect the percentages of false alarm errors, suggesting that it did not simply facilitate the discrimination task between alcohol and neutral object pictures. Whereas further studies will be required to test the “floor effect” hypothesis, the present results at least clearly show that the addition (or not) of alcohol brand logos is an important experimental parameter that must be carefully considered when designing a study with alcohol visual cues. The presence of brand logos has been shown to help participants with lower beer consumption practices to identify the taste of nonalcoholic and alcoholic beers (Martin, Earleywine, & Young, 1990). Problem drinkers also report a higher number of alcohol brand preferences than non-problem drinkers (Tanski, McClure, Jernigan, & Sargent, 2011), with a preference for liquor and beer (Siegel, Naimi, Cremeens, & Nelson, 2011). Considering the importance of brand alcohol logos, it is unfortunate that many studies did not mention whether brand logos were included on alcohol pictures, especially when heavy and light drinkers are compared. As shown in the present results, this might affect the magnitude of the recorded cognitive biases and contribute to explain discrepancies between the results of some studies. Regarding false alarm errors, no strong differences were obtained between problem and non-problem drinkers. There was only a tendency for female problem drinkers to show higher rates of false alarms for alcohol no-go stimuli. Although this effects was only very close to statistical significance (p = 0.052), it might be the sign of a weaker ability for female problem drinkers to inhibit a response toward an alcohol-related cue. Such a gender effect is in line with two studies indicating greater cognitive difficulties in female problem drinkers in comparison with male problem drinkers. Female binge drinkers show poorer performance in spatial working memory task than female non-binge drinkers (Scaife & Duka, 2009; Townshend & Duka, 2005) and exhibit general inhibition problems compared with female non-problem drinkers and males in both drinking groups (Nederkoorn et al., 2009). Faster brain atrophy in women after alcohol dependence compared to men is also suggested by fMRI research (Mann et al., 2005). Together, these data suggest either that women are more vulnerable to executive cognitive impairments related to problematic drinking or that deficits in response inhibition is a premorbid factor for problematic drinking in women. However, discrepant results

F. Kreusch et al. / Addictive Behaviors 38 (2013) 2520–2528

have also been published. For example, better working memory performances were recorded in female binge drinkers relative to male binge drinkers (Parada et al., 2012). To our knowledge, this is the first study reporting gender differences in response inhibition toward alcohol cues in problem drinkers and further experiments are clearly required to confirm the present results. Finally the present results indicate that differences between problem and non-problem drinkers in a modified go/no-go task with alcohol visual cues are relatively small-scale and require specific experimental parameters to be uncovered. It should be noted however that the present study involved young students with a relatively short history of alcohol consumption. Alcohol cognitive biases of higher magnitude might be expected in alcohol dependent patients or in problem drinkers with a longer history of alcohol abuse. Unexpectedly, problem and non-problem drinkers in the present study did not differ on the scores of impulsivity on the BIS-11 questionnaire. High levels of behavioral impulsivity have been shown to predict a unique part of variance in hazardous drinking (Christiansen, Cole, Goudie, & Field, 2012) and are suggested to be a risk factor for difficulties in refraining a behavioral response for alcohol (Papachristou, Nederkoorn, Havermans, van der Horst, & Jansen, 2012). In some previous studies, differences in impulsivity were reported between light and heavy alcohol drinkers (Papachristou et al., 2012). The lack of such differences may contribute to explain the low magnitude of the behavioral differences between problem and non-problem drinkers in the go/no-go task in the present study. Before concluding, some limitations of the present study should be acknowledged. First, the sample included only young problem and non-problem drinkers with a relatively short history of alcohol consumption. It is likely that problem drinkers with a longer history of alcohol abuse and alcohol dependent patients would show stronger cognitive biases toward alcohol cues in a similar go/no-go task. As the present study included only university students, the generalizability of the results is limited. Further studies in alcohol dependent patients or in different populations of heavy or problem drinkers are necessary to unravel cognitive biases toward alcohol cues. Secondly, in the present study, we did not assess participants' preferences for various types of alcohol (bier, wine, liquor …). Individuals' alcohol preferences might affect their responses to the various types of alcohol-based visual cues, thereby lowering the recorded cognitive biases when non-preferred alcohol cues are shown. It is therefore possible that the present study underestimated the size of cognitive biases in problem drinkers. A future study might try to control this effect by individualizing the alcohol visual cues. In summary, the present study shows that all participants, whether problem and non-problem alcohol drinkers, exhibit a cognitive bias for alcohol cues as evidenced by significantly shorter reaction times when alcohol pictures are go stimuli and a higher percentage of false alarm errors when alcohol pictures are no-go stimuli. This cognitive bias might be related to the positive emotional value of alcoholrelated cues in student participants. Stronger cognitive biases in the treatment of alcohol cues are observed in heavy alcohol drinkers, although differences between problem and non-problem drinkers are relatively small-scale and require specific experimental parameters to be uncovered. In particular, the presence of alcohol brand logos on visual alcohol cues is an important experimental parameter that significantly affects behavioral responses. Role of funding sources Funding for this study was provided by the Fonds National de la Recherche Scientifique (FNRS). FNRS had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication. Contributors There are three authors of the manuscript: 1. Fanny Kreusch, 2. Aurélie Vilenne and 3. Etienne Quertemont. All authors conducted literature searches, provided summaries

2527

of previous research studies, designed the study and contributed to the writing and editing of the manuscript. All authors conducted and interpreted statistical analyses. Author 1 wrote the first draft of the manuscript and author 2 contributed to and approved the final manuscript. Conflict of interest All authors declare that they have no conflicts of interest which could inappropriately influence the manuscript. Acknowledgments This work was supported by the Fonds National de la Recherche Scientifique (FNRS). Fanny Kreusch is a research assistant under contract with the FNRS.

References Adams, S., Ataya, A. F., Attwood, A. S., & Munafò, M. R. (2013). Effects of alcohol on disinhibition towards alcohol-related cues. Drug and Alcohol Dependence, 27, 137–142. American Psychiatric Association (1994). Diagnostic and statistical manual of mental health disorders (4th ed ) Washington DC. Bechara, A., & Martin, E. M. (2004). Impaired decision making related to working memory deficits in individuals with substance addictions. Neuropsychology, 18, 152–162. Bickel, W. K., & Marsch, L. A. (2001). Toward a behavioral economic understanding of drug dependence: delay discounting processes. Addiction, 96, 73–86. Bjork, J. M., Hommer, D. W., Grant, S. J., & Danube, C. (2004). Impulsivity in abstinent alcohol-dependent patients: relation to control subjects and type 1–/type 2–like traits. Alcohol, 34, 133–150. Bruce, G., & Jones, B. T. (2004). A pictorial Stroop paradigm reveals an alcohol attentional bias in heavier compared to lighter social drinkers. Journal of Psychopharmacology, 18, 527–533. Christiansen, P., Cole, J. C., Goudie, A. J., & Field, M. (2012). Components of behavioural impulsivity and automatic cue approach predict unique variance in hazardous drinking. Psychopharmacology, 219, 501–510. De Houwer, J., & Tibboel, H. (2010). Stop what you are not doing! Emotional pictures interfere with the task not to respond. Psychonomic Bulletin & Review, 17, 699–703. Derogatis, L. R. (1977). Symptom Check List-90 Revised. Administration Scoring and Procedures Manual. Baltimore: Clinical Psychometric Research. Deutsch, R., & Strack, F. (2006). Reflective and impulsive determinants of addictive behavior. In R. W. Wiers, & A. W. Stacy (Eds.), Handbook of Implicit Cognition an Addiction (pp. 45–57). Thousand Oaks, CA: SAGE. Earp, B. D., Dill, B., Harris, J. L., Ackerman, J. M., & Bargh, J. A. (2013). No sign of quitting: Incidental exposure to no-smoking signs ironically boosts cigarette-approach tendencies in smokers. Journal of Applied Social Psychology (in Press). Field, M., Christiansen, P., Cole, J., & Goudie, A. (2007). Delay discounting and the alcohol Stroop in heavy drinking adolescents. Addiction, 102, 579–586. Field, M., & Cole, J. (2007). Do alcohol cues facilitate or impair cognitive processing in recently detoxified alcoholics? Commentary on Noel et al. (2007b). Psychopharmacology, 192, 299–300. Field, M., & Cox, W. M. (2008). Attentional bias in addictive behaviors: A review of its development, causes, and consequences. Drug and Alcohol Dependence, 97, 1–20. Field, M., Kiernan, A., Eastwood, B., & Child, R. (2008). Rapid approach responses to alcohol cues in heavy drinkers. Journal of Behavior Therapy and Experimental Psychiatry, 39, 209–218. Field, M., Mogg, K., Zetteler, J., & Bradley, B. P. (2004). Attentional biases for alcohol cues in heavy and light social drinkers: The roles of initial orienting and maintained attention. Psychopharmacology, 176, 88–93. Fleming, M. F., Barry, K. L., & MacDonald, R. (1991). The alcohol use disorders identification test (AUDIT) in a college sample. International Journal of the Addictions, 26, 1173–1185. Fortin, M. F., & Coutu-Wakulczyk, G. (1985). Validation et Normalisation d'une Mesure de Santé Mentale: le SCL-90-R. Report Presented at the Quebec Council of Social Research (CQRS). Faculty of Nursing, University of Montréal. Hare, T. A., Tottenham, N., Davidson, M. C., Glover, G. H., & Casey, B. J. (2005). Contributions of amygdala and striatal activity in emotion regulation. Biological Psychiatry, 57, 624–632. Hare, T. A., Tottenham, N., Galvan, A., Voss, H. U., Glover, G. H., & Casey, B. J. (2008). Biological substrates of emotional reactivity and regulation in adolescence during an emotional go-nogo task. Biological Psychiatry, 63, 927–934. Hill, S. Y., Lowers, L., Locke, J., Snidman, H., & Kagan, J. (1999). Behavioral inhibition in children from families at high risk for developing alcoholism. Journal of the American Academy of Child and Adolescent Psychiatry, 38, 410–417. Lowman, C., Hunt, W. A., Litten, R. Z., & Drummond, D. C. (2000). Research perspectives on alcohol craving: An overview. Addiction, 95, S45–S54. Mann, K., Ackermann, K., Croissant, B., Mundle, G., Nakovics, H., & Diehl, A. (2005). Neuroimaging of gender differences in alcohol dependence: Are women more vulnerable? Alcoholism, Clinical and Experimental Research, 29, 896–901. Martin, C. S., Earleywine, M., & Young, R. D. (1990). Identification of nonalcoholic and alcoholic beers: Effects of consumption practices and beer brand. Addictive Behaviors, 15, 89–93. McEvoy, P. M., Stritzke, W. G., French, D. J., Lang, A. R., & Ketterman, R. (2004). Comparison of three models of alcohol craving in young adults: A cross-validation. Addiction, 99, 482–497. Montgomery, C., Fisk, J. E., Murphy, P. N., Ryland, I., & Hilton, J. (2012). The effects of heavy social drinking on executive function: A systematic review and

2528

F. Kreusch et al. / Addictive Behaviors 38 (2013) 2520–2528

meta-analytic study of existing literature and new empirical findings. Human Psychopharmacology, 27, 187–199. Nederkoorn, C., Baltus, M., Guerrieri, R., & Wiers, R. W. (2009). Heavy drinking is associated with deficient response inhibition in women but not in men. Pharmacology, Biochemistry, and Behavior, 93, 331–336. Nigg, J. T., Wong, M. M., Martel, M. M., Jester, J. M., Puttler, L. I., Glass, J. M., et al. (2006). Poor response inhibition as a predictor of problem drinking and illicit drug use in adolescents at risk for alcoholism and other substance use disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 45, 468–475. Noël, X., Colmant, M., Van Der Linden, M., Bechara, A., Bullens, Q., Hanak, C., et al. (2006). Time course of attention for alcohol cues in abstinent alcoholic patients: The role of initial orienting issue. Alcoholism, Clinical and Experimental Research, 30, 1871–1877. Noël, X., Paternot, J., Van der Linden, M., Sferrazza, R., Verhas, M., Hanak, C., et al. (2001). Correlation between inhibition, working memory and delimited frontal area blood flow measure by 99mTc-Bicisate SPECT in alcohol-dependent patients. Alcohol and Alcoholism, 36, 556–563. Noël, X., Van der Linden, M., d'Acremont, M., Bechara, A., Dan, B., Hanak, C., et al. (2007). Alcohol cues increase cognitive impulsivity in individuals with alcoholism. Psychopharmacology, 192, 291–298. Papachristou, H., Nederkoorn, C., Havermans, R., van der Horst, M., & Jansen, A. (2012). Can't stop the craving: The effect of impulsivity on cue-elicited craving for alcohol in heavy and light social drinkers. Psychopharmacology, 219, 511–518. Parada, M., Corral, M., Mota, N., Crego, A., Holguín, S. R., & Cadaveira, F. (2012). Executive functioning and alcohol binge drinking in university students. Addictive Behaviors, 37, 167–172. Parsons, O. A. (1998). Neurocognitive deficits in alcoholics and social drinkers: A continuum? Alcoholism, Clinical and Experimental Research, 22, 954–961. Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Factor structure of the Barratt impulsiveness scale. Journal of Clinical Psychology, 51, 768–774. Petit, G., Kornreich, C., Maurage, P., Noël, X., Letesson, C., Verbanck, P., et al. (2012). Early attentional modulation by alcohol-related cues in young binge drinkers: An event-related potentials study. Clinical Neurophysiology, 123, 925–936. Ratti, M. T., Bo, P., Giardini, A., & Soragna, D. (2002). Chronic alcoholism and the frontal lobe: Which executive functions are impaired? Acta Neurologica Scandinavica, 105, 276–281. Robinson, T. E., & Berridge, K. C. (1993). The neural basis of drug craving: An incentivesensitization theory of addiction. Brain Research Reviews, 18, 247–291. Rose, A. K., & Duka, T. (2008). Effects of alcohol on inhibitory processes. Behavioural Pharmacology, 19, 284–291. Saunders, J. B., Aasland, O. G., Babor, T. F., de la Fuente, J. R., & Grant, M. (1993). Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on early detection of persons with harmful alcohol consumption–II. Addiction, 88, 791–804. Scaife, J. C., & Duka, T. (2009). Behavioural measures of frontal lobe function in a population of young social drinkers with binge drinking pattern. Pharmacology, Biochemistry, and Behavior, 93, 354–362.

Schulz, K. P., Fan, J., Magidina, O., Marks, D. J., Hahn, B., & Halperin, J. M. (2007). Does the emotional go/no-go task really measure behavioral inhibition?: Convergence with measures on a non-emotional analog. Archives of Clinical Neuropsychology, 22, 151–160. Sher, K. J., Walitzer, K. S., Wood, P. K., & Brent, E. E. (1991). Characteristics of children of alcoholics: Putative risk factors, substance use and abuse, and psychopathology. Journal of Abnormal Psychology, 100, 427–448. Siegel, M. B., Naimi, T. S., Cremeens, J. L., & Nelson, D. E. (2011). Alcoholic beverage preferences and associated drinking patterns and risk behaviors among high school youth. American Journal of Preventive Medicine, 40, 419–426. Sobell, L. C., & Sobell, M. B. (1990). Self-report issues in alcohol abuse: State of the art and future directions. Behavioral Assessment, 12, 77–90. Spielberger, C. D. (1983). Manual for the State-Trait Anxiety Inventory STAI (Form Y). Palo Alto, CA: Mind Garden. Tanski, S. E., McClure, A. C., Jernigan, D. H., & Sargent, J. D. (2011). Alcohol brand preference and binge drinking among adolescents. Archives of Pediatrics & Adolescent Medicine, 165, 675–676. Tapert, S. F., Cheung, E. H., Brown, G. G., Frank, L. R., Paulus, M. P., Schweinsburg, A. D., et al. (2003). Neural response to alcohol stimuli in adolescents with alcohol use disorder. Archives of General Psychiatry, 60, 727–735. Townshend, J. M., & Duka, T. (2001). Attentional bias associated with alcohol cues: Differences between heavy and occasional social drinkers. Psychopharmacology, 157, 67–74. Townshend, J. M., & Duka, T. (2005). Binge drinking, cognitive performance and mood in a population of young social drinkers. Alcoholism, Clinical and Experimental Research, 29, 317–325. van den Wildenberg, E., Wiers, R. W., Dessers, J., Janssen, R. G., Lambrichs, E. H., Smeets, H. J., et al. (2007). A functional polymorphism of the mu-opioid receptor gene (OPRM1) influences cue-induced craving for alcohol in male heavy drinkers. Alcoholism, Clinical and Experimental Research, 31, 1–10. Wiers, R. W., Bartholow, B. D., van den Wildenberg, E., Thush, C., Engels, R. C., Sher, K. J., et al. (2007). Automatic and controlled processes and the development of addictive behaviors in adolescents: A review and a model. Pharmacology, Biochemistry, and Behavior, 86, 263–283. Wiers, R. W., Hoogeveen, K., Sergeant, J. A., & Gunning, B. W. (1997). Hight- and lowdose alcohol-related expectancies and the differential associations with drinking in male and female adolescents and young adults. Addiction, 92, 871–888. Wiers, R. W., Rinck, M., Dictus, M., & van den Wildenberg, E. (2009). Relatively strong automatic appetitive action-tendencies in male carriers of the OPRM1 G-allele. Genes, Brain, and Behavior, 8, 101–106. Wiers, R. W., & Stacy, A. W. (2006). Handbook of Implicit Cognition an Addiction. Thousand Oaks, CA: SAGE. Zago-Gomes, M., & Nakamura-Palacios, E. (2009). Cognitive components of frontal lobe function in alcoholics classified according to Lesch's typology. Alcohol and Alcoholism, 44, 449–457.