Cannabis intoxication inhibits avoidance action tendencies: a field study in the Amsterdam coffee shops

Transcription

1 Psychopharmacology (2013) 229: DOI /s ORIGINAL INVESTIGATION Cannabis intoxication inhibits avoidance action tendencies: a field study in the Amsterdam coffee shops Janna Cousijn & Robin W. M. Snoek & Reinout W. Wiers Received: 25 October 2012 / Accepted: 26 March 2013 / Published online: 18 April 2013 # Springer-Verlag Berlin Heidelberg 2013 Abstract Rationale Experimental laboratory studies suggest that the approach bias (relatively fast approach responses) toward substance-related materials plays an important role in problematic substance use. How this bias is moderated by intention to use versus recent use remains unknown. Moreover, the relationship between approach bias and other motivational processes (satiation and craving) and executive functioning remains unclear. Objectives The aim of this study was to investigate the cannabis approach bias before and after cannabis use in real-life setting (Amsterdam coffee shops) and to assess the relationship between approach bias, craving, satiation, cannabis use, and response inhibition. Methods Cannabis, tobacco, and neutral approach and avoidance action tendencies were measured with the Approach Avoidance Task and compared between 42 heavy cannabis users with the intention to use and 45 heavy cannabis users shortly after cannabis use. The classical Stroop was used to Janna Cousijn and Robin W. M. Snoek contributed equally to this paper. J. Cousijn : R. W. M. Snoek : R. W. Wiers Addiction Development and Psychopathology (ADAPT)-lab, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands J. Cousijn Amsterdam Institute for Addiction Research, Department of Psychiatry, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands J. Cousijn Department of Psychology, Leiden University, Leiden, The Netherlands J. Cousijn (*) Weesperplein 4, 1018 XA Amsterdam, The Netherlands j.cousijn@gmail.com measure response inhibition. Multiple regression analyses were conducted to investigate relationships between approach bias, satiation, craving, cannabis use, and response inhibition. Results In contrast to the hypotheses, heavy cannabis users with the intention to use did not show a cannabis approach bias, whereas intoxicated cannabis users did show an approach bias regardless of image category. This could be attributed to a general slowing of avoidance action tendencies. Moreover, craving was negatively associated with the approach bias, and no relationships were observed between the cannabis approach bias, satiation, prior cannabis use, and response inhibition. Conclusion Cannabis intoxication in a real-life setting inhibited general avoidance. Expression of the cannabis approach bias appeared not to be modulated by satiation or response inhibition. Keywords Approach Avoidance Task. Approach bias. Cannabis. Cannabis abuse. Craving. Satiation. Response inhibition Introduction Individuals with a substance use disorder are often aware of the potential negative consequences of their substance use, but still continue with these self-destructive behaviors (Stacy and Wiers 2010). According to dual-process theories of addiction, this paradox arises from an imbalance between a substanceoriented motivational system and a compromised reflective system (Stacy and Wiers 2010; Bechara 2005). The motivational system is characterized by fast relatively automatic processes and evaluates stimuli in terms of their affective and motivational significance. After repeated substance use, the motivational system is thought to become sensitized and conditioned toward substance-related cues (Stacy and Wiers

2 168 Psychopharmacology (2013) 229: ). The reflective system is characterized by slower regulatory processes. To stop or override the automatically triggered motivations to engage in substance use, one must have sufficient executive resources, which are often compromised in individuals with a substance use disorder (Bechara 2005; Grenard et al. 2008; Houben and Wiers 2009; Houben et al. 2011; Thush et al. 2008). Cognitive biases are thought to be behavioral manifestations of the underlying motivational processes to engage in substance use. Indeed, substance-related cues tend to grab attention (attentional bias; Bradley et al. 2003; Field and Cox 2008; Jones et al. 2003) and activate approach action tendencies (approach bias; Bradley et al. 2008; Field et al. 2008; Ostafin and Palfai 2006; Palfai and Ostafin 2003; Wiers et al. 2009) in substance-abusing individuals. These biases appear to be common for various substances of abuse including heroin, cocaine, alcohol, tobacco, and cannabis. Most models of addiction predict that the biases are strongly correlated with subjective craving and with quantitative substance use (Field et al. 2009; Stacy and Wiers 2010). Moreover, the relationship between subjective craving and cognitive biases is suggested to be bidirectional. However, studies investigating the relationship between craving and cognitive biases have reported contradictory findings. In a recent meta-analysis on the relation between the attentional bias and subjective craving, only a modest correlation of 0.19 was found (Field et al. 2009). Additional analyses revealed that the relationship was stronger when subjective craving was high. The authors concluded that attentional bias and craving are weakly related phenomena. The strength of the biases may also depend on other motivational mechanisms reflecting the current incentive value of substance use. This predicts moderating effects of substance availability, prior substance use, and satiation (satiation being defined as the satisfaction one feels with respect to a specific substance) on the expression of cognitive biases (Field et al. 2009; Watson et al. in press). Moreover, it has repeatedly been found that the association between motivational processes and measures of substance use is moderated by executive functions (Grenard et al. 2008; Houben and Wiers 2009; Peeters et al. 2012; Thush et al. 2008). The influence of motivational processes on substance use has been found to be stronger in individuals with relatively poor inhibitory control over (inappropriate) behavior (Wiers et al. 2010b). These findings suggest that the biases are strongest when substance (ab)users have the intention and the opportunity to use. In turn, satiety may (temporarily) decrease the bias through a temporary devaluation of the incentive value of substance use. Interestingly, a study investigating the attentional bias in alcohol users in the pub showed that attentional bias decreased with increasing alcohol consumption in participants who had been binge drinking (Schoenmakers and Wiers 2010). These findings support the idea that the attentional bias is strongest before the initiation of substance use, but weakens as satiation increases and motivation to use decreases. The Netherlands provides a unique cultural setting to investigate cannabis use. Cannabis outlets (Dutch coffee shops ) offer a real-life setting in which individuals can buy and use cannabis. In a laboratory study, we recently showed that the cannabis approach bias predicted cannabis use in heavy cannabis users (Cousijn et al. 2011). However, the relationship between approach bias and motivational processes (i.e., intention to use, satiation, and craving) and executive functioning remains unclear. Moreover, how these processes generalize to real-life behavior remains to be tested. The aim of this study therefore was to investigate the cannabis approach bias before and after cannabis use in real-life setting (Amsterdam coffee shops) and to assess the relationship between approach bias, craving, satiation, history of cannabis use, and response inhibition. In various Amsterdam coffee shops, we measured the approach bias, craving, satiation, prior cannabis use, and response inhibition in heavy cannabis users with the intention to use cannabis (pre-cannabis group) and heavy cannabis users after cannabis intake (post-cannabis group). Since most cannabis users also smoked cigarettes, we also measured the smoking approach bias to control potential effects of nicotine use. We hypothesized that satiation, craving, and prior cannabis use would be significant predictors of the cannabis approach bias in both the pre-cannabis group and the post-cannabis group. Moreover, the approach bias would be lower in the postcannabis group since these participants would be more satiated and experience less craving. Finally, response inhibition was expected to act as a moderator between the approach bias and cannabis use: A stronger correlation was expected between the bias and prior cannabis use in cannabis users with low executive functioning. Materials and methods Participants Participants were 82 male and 8 female cannabis users, aged 18 59, who were recruited and tested in five different cannabis outlets (coffee shops) in Amsterdam by a single experimenter. Roughly half of the approached coffee shop costumers declined participation. Potential participants had to use cannabis at least once per week and were excluded if they were under the influence of alcohol (Field et al. 2005) or if they had ever been treated for a psychiatric disorder. This was verified with a short questionnaire. If all inclusion criteria were met, participants were allocated to the precannabis and post-cannabis groups based on when they last used cannabis; participants in the pre-cannabis group had

3 Psychopharmacology (2013) 229: not used cannabis on the day of the experiment, whereas participants in the post-cannabis group used it less than an hour ago. Additionally, participants in the pre-cannabis group had the intention to use cannabis at least within 2 h after the test session, which was verified with a short questionnaire. The experimenter was consequently not blinded to participants drug state. The study was approved by the Ethics Committee of the Psychology Faculty of The University of Amsterdam, and all participants signed an informed consent before participation. Questionnaires The Cannabis Use Disorder Identification Test Revised (CUDIT-R; Adamson et al. 2010) was used to assess cannabis use and related problems during the past 6 months. The Alcohol Use Disorder Identification Test (AUDIT; Saunders et al. 1993) was used to assess alcohol use and related problems during the past 6 months. Nicotine dependence was measured with the Fagerström Test of Nicotine Dependence (FTND; Heatherton et al. 1991). Subjective craving was measured with the Marijuana Craving Questionnaire Short Form (MCQ-SF). A visual analogue scale (VAS) was used to assess satiation. Participants rated the intensity on a 100 unit line from not at all to extremely. There is support for the reliability and validity of a VAS scale for food satiation (Geiselman et al. 1998; Flint et al. 2000). Additionally, participants were asked about their intention to use cannabis within 2 h after the experiment. Furthermore, demographics (age, sex, and level of education) and a detailed history of cannabis and tobacco use were obtained, including variables on daily use, weekly use, duration of use (years), and last cannabis use (hours). Classical Stroop The validated paper version of the classical Stroop task was used. The Stroop is hypothesized to be a good indicator of response inhibition (Krompinger and Simons 2011). The task consists of three subtasks. In the first subtask, participants had to read color words (red, blue, yellow, and green) printed in black ink placed in random order on a gray sheet of paper. In the second subtask, participants were instructed to name the colors of solid color patches. The last subtask consisted of naming the ink color of color words, the ink color being incongruent to the color word. Response time is, on average, longest in the last subtask. The difference between congruent and incongruent conditions is used as a measure of response inhibition. Approach Avoidance Task The AAT (Cousijn et al. 2011; Wiers et al. 2009, 2010a) was used to measure approach bias toward cannabis- and tobaccorelated stimuli. It consisted of one cannabis and one tobacco block, of which the order was counterbalanced over participants. The tobacco block was included to control potential confounding effects of tobacco use as most heavy cannabis users also smoke tobacco and the resemblance between cannabis and tobacco cigarettes may also activate approach actions toward tobacco in tobacco users (Cousijn et al. 2011). In the cannabis block, participants were presented with 12 cannabis-related images (pictures of someone using cannabis and pictures of objects associated with cannabis use) and 12 visually matched neutral images (i.e., someone holding a pen). The tobacco block was similar to the cannabis block, except that tobacco-related images were used instead of cannabisrelated images. In addition, a separate set of visually matched neutral images was used. The images were rotated 3 to the left or right, and participants had to pull (approach) or push (avoid) a joystick in response to the rotation direction, as fast as possible. Half of the participants had to push images rotated to the left and pull images rotated to the right, while the other half of the participants were given opposite instructions. Pulling gradually increased image size, whereas pushing decreased it (zooming feature). Each image was presented twice in pull and twice in push format, resulting in 98 trials per block. Procedure The experiment was conducted inside the coffee shop on weekdays between 11:00 and 20:00 immediately following recruitment. After signing informed consent, participants completed questionnaires on demographic variables, history of cannabis and tobacco use, craving, satiation, and intention to use cannabis within the next 2 h. Participants then performed the classical Stroop test and the AAT. The AAT was performed on a laptop. The tasks were followed by a second assessment of craving, satiation, and intention to use. Participants finally completed questionnaires on substance use and related problems. Total session time was around 30 min, and reimbursement was 5 euro. Data preparation and statistical analysis Regarding the AAT, median scores were used to summarize participants performance on the AAT as medians are less sensitive to outliers than mean scores (Wiers et al. 2009; Palfai 2006). Reaction times below 200 ms, above 2,000 ms, and deviating more than 3 standard deviations from the individual mean were removed per participant. Error trials were also removed. For each participant, a cannabis bias score, a tobacco bias score, and a neutral bias score were calculated by subtracting the median approach RT from the median avoid RT, for the corresponding images (e.g., separately for the cannabis images, tobacco images,

4 170 Psychopharmacology (2013) 229: and all neutral images combined). This way, a positive score indicated faster approach compared to avoidance (approach bias). Reliability of the AAT was investigated by calculating Cronbach s alpha for each bias score, with individual bias scores per image. Internal reliability of the cannabis bias (12 items, Cronbach s α=0.42), tobacco bias (12 items, Cronbach s α=0.55), and neutral bias (24 items, Cronbach s α=0.53) was fairly poor (Cortina 1993). For the classical Stroop task, interference scores were used as an indication of response inhibition. These scores were calculated by subtracting the average time needed to complete the first two congruent subtasks from the time needed to complete the third incongruent subtask (Interference = Stroop III [(Stroop I + Stroop II) / 2] (Van der Elst et al. 2006). Errors in the Stroop tasks were most often self-corrected by participants. This requires extra time, and therefore, the scores on the Stroop tasks were indirectly corrected for errors. Mixed ANOVAs were used to examine differences between and within groups in the AAT biases. Post hoc one-sample t-tests were used to test whether the AAT biases deviated significantly from zero. 1 Regression analyses were performed to examine the influence of satiation and craving on the approach bias, as well as to investigate the potential moderating effects of response inhibition between the approach bias, subjective craving, and prior cannabis use. The speed of push and pull responses with the joystick vary within and between participants. Individual differences in approach and avoidance tendencies are reflected in the neutral bias score (push neutral pull neutral), but affect the cannabis bias and smoking bias as well. To control general biases in approach and avoid actiontendencies, regression analyses were conducted with neutral bias score as the covariate. Partial correlations were also conducted with neutral bias score as covariate based on the same rationale. Results Sample characteristics Three participants in the pre-cannabis group were excluded (all male) since they indicated to have no intention to use cannabis within 2 h after the test session. Of the 87 remaining participants, 77 % also smoked tobacco cigarettes. The pre- and postgroups did not differ significantly in gender, education level, Stroop interference score, cannabis use and problems, tobacco use and problems, and alcohol use and problems (see Table 1). However, participants in the post-cannabis group were significantly older than participants in the pre-cannabis group (t 85 =2.22, P=0.03). 1 Non-parametric tests were used for variables that were not normally distributed (Mann Whitney U test or Wilcoxon signed-rank test). Self-reported satiation and craving A mixed-design ANOVA, comparing the two groups across the two within-participant measures of satiation (start session and end session), was performed to examine differences in satiation (Table 2). There was a main effect of group, F 1,85 =17.94, P<0.001, η 2 =0.17. As expected, satiation was higher in the post-cannabis compared to the precannabis group. No main effect was found for time, F 1,85 = 1.00, P=0.320, indicating that satiation did not change during the 30-min test session. There was also no interaction effect between group and time, F 1,85 =1.60, P= A mixed-design ANOVA, comparing the two groups across the two measures of subjective craving (MCQ) was performed to examine differences in craving (Table 2). There was a main effect of group, F 1,85 =7.63, P=0.007, η 2 =0.08. Subjective craving was higher in the pre-cannabis group than in the post-cannabis group. No main effect was found for time, F 1,85 =0.065, P=0.800, indicating that craving did not change during the test session. There was also no interaction effect between group and time, F 1,85 =0.19, P= Main analyses 2 Group comparison AAT bias scores Three participants made more than 25 % errors on the AAT (all male) and were therefore excluded from analyses. The AAT biases were not fully normally distributed. 3 In response to this issue of non-normality, the variables were transformed into ranks, and to test for differences in the AAT biases between groups across the three biases (e.g., cannabis, tobacco, and neutral), a mixed-design ANOVA was conducted with these ranked data. 4 In contrast to our hypothesis, there was no main effect of bias type, F 2,82 =0.001, P=0.999, indicating that the bias scores did not significantly differ, irrespectively, of group. However, there was a main effect of group, F 1,82 =12.15, 2 All main analyses have been conducted a second time with only male participants, a third time with the three participants who made more than 25 % errors on the AAT included, and a fourth time with age as a covariate. Results and interpretations did not differ when only male participants were included. There was no longer group difference in neutral bias when the three participants who made more than 25 % errors on the AAT were included (t 85 = 1.906, P=0.06) and when age was entered as a covariate (t 81 = 1.83, P=0.071). 3 Cannabis, skewness 0.539, SE 0.263, Z=2.09, kurtosis 0.611, SE 0.520, Z=1.18; tobacco, skewness 0.019, SE 0.263, Z=0.07, kurtosis 0.520, SE 0.520, Z=1.00; neutral skewness 0.911, SE 0.263, Z= 3.46, kurtosis 1.45, SE 0.52, Z= This mixed-design ANOVA was performed a second time with whether participants also smoked tobacco cigarettes as a between-subjects factor and a third time with age as a covariate. Results and interpretations did not differ from the mixed-design ANOVA discussed.

5 Psychopharmacology (2013) 229: Table 1 Sample characteristics Pre-cannabis group Post-cannabis group P-value SD standard deviation, CUDIT-R Cannabis Use Disorder Identification Test Revised, AUDIT Alcohol Use Disorder Identification Test, FTND Fagerström Test of Nicotine Dependence *P<0.05; **P<0.01; ***P<0.001 % male (n) 92.9 (42) 88.9 (45) Age, mean (SD) (8.13) (10.18)* Also tobacco cigarettes smoking, n (%) 34 (81.0) 31 (68.9) Level of education (%low) (%average) (%high) 7.1; 66.7; ; 48.9; Daily tobacco use (number of cigarettes), mean (SD) 8.9 (6.43) 8.26 (5.52) Duration of tobacco use (years), mean (SD) 9.41 (8.64) (9.98) Weekly cannabis use (gram), mean (SD) 5.10 (4.83) 4.98 (3.05) Weekly cannabis use (days), mean (SD) 5.35 (1.52) 5.78 (1.85) Duration regular cannabis use (years), mean (SD) 7.54 (6.08) (9.17) Stroop interference, mean (SD) (14.55) (13.81) CUDIT-R score, mean (SD) (5.23) (6.43) AUDIT score, mean (SD) 8.60 (3.77) 7.91 (4.71) FTND score, mean (SD) 3.60 (2.28) 3.15 (2.36) Hours since last cannabis use, mean (SD) 20.5 (5.52) 0.20 (0.24)*** <0.001 P=0.001, η 2 =0.13. Post hoc independent t-tests (Bonferroni corrected for multiple comparisons) indicated that the groups differed significantly for each of the biases: cannabis, t 82 =2.64, P=0.01, d=0.58; tobacco, t 82 =3.02, P=0.003, d=0.66; neutral, t 82 =2.06, P=0.043, d=0.45. Subsequent one-sample t-tests (not corrected for multiple comparisons) showed that, in contrast to our hypothesis, the cannabis bias, tobacco bias, and neutral bias were significantly greater than zero in the postcannabis group (Fig. 1) (cannabis, mean=29.51, SD=89.21, t 43 =2.19, P=0.034, d=0.67; tobacco, mean=38.86, SD= 79.49, t 43 =3.24, P=0.002, d=0.99; neutral, mean=25.205, SD=65.68, t 43 =2.55, P=0.015, d=0.78). The biases in the pre-cannabis group did not deviate from zero. No interaction effect between group and biases was found, F 2,82 =0.434, P= To examine whether the between-group differences in the biases were due to greater variance in the post-cannabis group [cannabis intoxication may increase RTs (Lenné et al. 2010)], independent t-tests were performed, comparing the mean RTs of cannabis trials, tobacco trials, and neutral trials between groups (Table 3). None of these tests were significant. In addition, Box s test of equality of covariance matrices and Levene s test of equality of error variances contained no significant differences, which provided support for the assumption that the variances in bias scores did not differ between groups. Independent t-tests revealed that the overall approach bias in the post-cannabis group was due to a slower avoidance: Participants in the post-cannabis group had longer RTs for push trials compared to participants in the pre-cannabis group (Mann Whitney U, Z=2.44, P=0.015). Mean RTs for the pull trials did not differ between the post-cannabis and pre-cannabis groups (Mann Whitney U, Z=1.69, P=0.091; see Table 3). Relationship between cannabis bias, satiation, craving, and prior cannabis use To assess the relationship between the cannabis bias score and satiation, subjective craving, and prior cannabis use and Table 2 Satiation, craving, and intention to use cannabis Start session End session SD standard deviation, MCQ Marijuana Craving Questionnaire Satiation, mean (SD) Pre-cannabis group (26.86) (27.17) Post-cannabis group (26.39) (24.27) P-value <0.001 <0.001 Craving (MCQ), mean (SD) Pre-cannabis group (9.67) (12.93) Post-cannabis group (13.98) (14.76) P-value Intention to smoke (%yes) (%no) Pre-cannabis group 95.2; 0.0; ; 0.0; 4.8 (%maybe) Post-cannabis group 80.0; 15.6; ; 13.3; 8.9 P-value

6 172 Psychopharmacology (2013) 229: variable entered in step 1. Preliminary analyses indicated no violation of the assumption of normality, linearity, multicollinearity, and homoscedasticity (maximum Cook s distance=0.20, maximum standardized residual=2.68). Total variance explained by the final model was 37 % (F 6,75 =7.27, P<0.001; see Table 4 model b). The tobacco bias did not significantly predict the cannabis bias. However, craving was no longer a significant predictor of the cannabis bias (Table 4). 5 Moderating effect of executive functioning on relationship between cannabis bias and cannabis use Fig. 1 AAT bias scores with standard error bars for cannabis, tobacco, and neutral images in the pre-cannabis group and the post-cannabis group. A positive score reflected an approach bias (faster RT on pull trials than push trials). Participants in the post-cannabis group have an approach bias toward all image types, and these scores were significantly higher than those in the pre-cannabis group related problems (indexed with the CUDIT-R), hierarchical multiple regression was conducted. All variables were zerocentered. For the satiation and craving measure, the average of each measure was used (start-session and end-session correlation satiation, r=0.826, P<0.001; craving, r=0.821, P<0.001; correlation satiation craving, r= 0.23, P=0.034). In order to control general approach and avoidance tendencies and direct effects of cannabis, group and the neutral bias were entered in the first step. Additional variables in the second step were satiation, craving, and CUDIT-R scores. Preliminary analyses indicated no violation of the assumption of normality, linearity, multicollinearity, and homoscedasticity (maximum Cook s distance=0.17, maximum standardized residual=2.69). Total variance explained by the final model was 36 % (F 5,76 =8.70, P<0.001; see Table 4 model a). The control variables in step 1 explained 29 % of the variance in cannabis bias (F change 2,79 =16.25, P<0.001), with neutral bias being a significant predictor (P<0.001). In step 2, satiation, craving, and CUDIT-R explained an additional 7.3 % of the variance (F change 3,76 =2.89, P=0.041), with craving being a significant predictor (P=0.048). Participants with higher levels of craving had a weaker cannabis approach bias. Influence of tobacco bias on relationship between cannabis bias, satiation, craving, and prior cannabis use Most cannabis users also smoked tobacco. Cannabis-related images may have activated action tendencies toward tobacco in smoking cannabis users. To control the possible influences of tobacco on the cannabis bias, the same regression analysis was performed, with tobacco bias included as an extra control To test the hypothesis that executive functioning acted as a moderator between approach bias and cannabis use and related problems, we performed multiple regression analyses on CUDIT-R scores. After correction for the neutral bias, we entered the cannabis bias, Stroop interference (an indication of executive functioning), and the interaction between the Stroop score and cannabis bias. These regression analyses were performed a second time with weekly cannabis use (gram) as the dependent variable. None of the predictors were significant in these analyses. In contrast to the hypotheses, executive functioning did not act as a moderator between approach bias and cannabis use and related problems. Exploratory correlations within groups For each group, the association between the cannabis bias, smoking bias, craving, satiation, and measures of tobacco use, cannabis use, and alcohol use and problems were explored separately (partial correlations corrected for neutral bias). In the pre-cannabis group, the cannabis bias correlated significantly with the AUDIT (R=0.39, P=0.011). In the post-cannabis group, the cannabis bias correlated significantly (negatively) with craving (R= 0.41, P=0.007). Discussion This study investigated the influence of satiation, subjective craving, and cannabis use on cannabis approach and avoidance action tendencies in heavy cannabis users inside Amsterdam coffee shops. Heavy cannabis users with the intention to use cannabis (pre-cannabis group) were compared to heavy cannabis users under the influence of cannabis (post-cannabis 5 The regression analyses as shown in Table 4 have been performed a second time with age and years of regular cannabis use additionally entered in the first step. The MCQ-SF now also significantly predicted the cannabis bias when the smoking bias was included in the model (beta= 0.227, p=0.034, R 2 change step 2b=0.074, p=0.041). Neither age nor duration of regular use predicted the cannabis bias.

7 Psychopharmacology (2013) 229: Table 3 AAT reaction time per group for each image type and response type Mean reaction time with standard error *P<0.05; **P<0.001 push pull comparison; # P<0.05 group comparison Push trials Pull trials Overall AAT cannabis Pre-cannabis group (27.26) (27.88) (26.98) Post-cannabis group (31.25) (28.59)* (29.31) AAT tobacco Pre-cannabis group (21.29) (22.35) (21.22) Post-cannabis group (29.82) (26.79)** (27.69) AAT neutral Pre-cannabis group (21.99) (24.88) (23.02) Post-cannabis group (27.40) (25.06)** (25.89) AAT overall Pre-cannabis group (22.74) (24.01) (22.99) Post-cannabis group (22.80)*,# (26.10) (27.19) group). While in accordance with our hypotheses, the postcannabis group reported higher satiation than the pre-cannabis group; unexpectedly, the post-cannabis group showed a stronger instead of a weaker approach bias for cannabis as well as for the other image categories (smoking and neutral) compared with the pre-cannabis group. Moreover, the cannabis approach bias was negatively associated with craving. We did not observe a significant relationship between the cannabis approach bias and satiation, prior cannabis use and related problems, and response inhibition. Experimental laboratory studies support an important role for the approach bias in the development and maintenance of addictive behaviors (Stacy and Wiers 2010; Watson et al. in press). However, the motivational mechanisms underlying the expression of approach bias are unclear. If the approach bias is a goal-directed response, its expression is expected to be modulated by the incentive value of cannabis use (Watson et al. in press). The incentive value of cannabis is thought to (gradually) increase over the course of cannabis use, whereas satiety may temporarily devalue the rewarding effects of cannabis use. We did not observe a relationship between the cannabis bias and satiation, and prior cannabis use and related problems. Moreover, stronger craving was related to a lower cannabis approach bias. Cannabis intoxication appears to be associated with a general approach bias. These findings suggest that strong intentions and motivations to use do not increase the approach bias, contradicting the goal-directed account of the approach bias. The post-cannabis group was slower on overall avoidance tendencies, while response inhibition (classical Stroop task) did not differ between the pre-cannabis and postcannabis groups. Moreover, average RTs and variance in RTs did not differ between groups. These results suggest that cannabis intoxication inhibits avoidance responses in general and that the general approach bias in the postcannabis groups is unlikely due to a general effect of cannabis on cognition. The literature on acute effects of cannabis in heavy cannabis users is sparse, reporting mixed findings about how it affects response inhibition and psychomotor responses (for review, see Crean et al. 2011). Discrepancies between studies may be caused by differences in intoxication level, with a higher THC dose leading to stronger impairments. Also, tolerance may decrease acute effects of cannabis intoxication on cognitive functioning in general. Interestingly, cannabis intoxication in heavy cannabis users potentially selectively improves sustained attention (Hart et al. 2001) and divided attention (Haney et al. 1999). It may therefore be that cannabis intoxication specifically Table 4 Hierarchical multiple regression analyses for variables predicting AAT cannabis bias Final model a R 2 =0.36***, adjusted R 2 =0.32. Final model b R 2 =0.37***, adjusted R 2 =0.32. SE standard error, MCQ-SF Marijuana Craving Questionnaire Short Form, CUDIT-R Cannabis Use Disorder Identification Test Revised *P<0.05; **P<0.01; ***P<0.001 Model a Model b B SE B β B SE B β Step 1a change R 2 : 0.291*** Step 1b change R 2 : 0.308*** Pre-/post-cannabis group AAT neutral bias *** *** AAT tobacco bias Step 2a change R 2 : 0.073* Step 2b change R 2 : 0.06 Pre-/post-cannabis group AAT neutral bias *** *** AAT tobacco bias Satiation MCQ-SF * CUDIT-R

8 174 Psychopharmacology (2013) 229: decreases the ability to disengage attention and thereby avoidance, causing the general slowing of avoidance in the post-cannabis group without affecting response inhibition. Importantly, these inferences are speculative, and we cannot exclude potential confounding (sub)acute effects of cannabis on cognitive functioning with a between-subject design. In line with our expectations, satiation was higher and craving was lower in the post-cannabis group, whereas the approach bias was stronger in the post-group regardless of satiation. These findings contradict earlier experimental laboratory studies investigating the effect of alcohol consumption on the alcohol approach bias in heavy drinkers (Christiansen et al. 2012). Findings from these studies suggest that anticipation of alcohol increases an alcohol approach bias but that the pharmacological effects may not contribute to this. Therefore, inhibition of avoidance in the post-cannabis group may likely be a specific pharmacological effect of cannabis, rather than a phenomenon general to heavy substance use. Alternatively, it could be that the level of cannabis intoxication in the current sample was relatively low, acting as a prime to use even more (average satiation in the post-cannabis group was 67 %, and 79 % indicated to still have the intention to use more cannabis after the test session). This view is in line with attentional bias studies in heavy alcohol users, which showed an increase of the bias at low doses but a decrease at moderate and high doses (Schoenmakers et al. 2008; Schoenmakers and Wiers 2010; Dukaand Townshend2004). However, given the observed increase in satiation and decrease in craving in the post-cannabis group, we consider this alternative hypothesis unlikely. Since experimental laboratory studies support an important role of cognitive biases in the development and maintenance of addictive behaviors, it is important to know if these processes generalize to a real-life setting in which individuals have the opportunity to use. A real-life setting may also contain contextual factors (social elements and environmental cues) that could increase the biases and craving (McKay and Schare 1999). In contrast to our laboratory AAT study in heavy cannabis users (Cousijn et al. 2011), we did not observe a cannabis approach bias in the pre-cannabis group in the coffee shop. Thereby, this study cannot validate the role of the approach bias in real life. However, the lack of an approach bias for cannabis in the pre-cannabis group may be explained by the differences in sample characteristics between studies. The heavy cannabis users included in the present study were long-term users with slightly higher levels of use-related problems. It could be that the approach bias only plays a role in earlier stages of cannabis use, predicting onset and repeated cannabis use rather than chronic cannabis use and dependence. This appears to disagree with the incentive sensitization theory of addiction (Robinson and Berridge 1993) and seems more in line with theories where incentive sensitization is mainly important during early escalation of drug use and less with subsequent compulsive drug use (Di Chiara 2000; Everitt and Robbins 2005). Alternatively, the lack of an approach bias may be explained by the relatively poor reliability of the AAT and the high level of distraction inside the coffee shop. To test this hypothesis, associations between approach bias and cannabis use need to be assessed in dependent, heavy, and sporadic cannabis users compared to non-using controls inside the coffee shop. Subjective craving is also thought to be an important motivational process contributing to continued substance use and relapse (Franken 2003). A bidirectional positive relationship could be expected between craving and other cognitive biases: Craving may direct attention toward substances of abuse and activate approach action tendencies, which, in turn, may lead to a further increase of craving (Field et al. 2009). In contrast to our hypothesis but in line with our previous study (Cousijn et al. 2011), the approach bias was negatively associated with craving (MCQ-SF; Heisman et al. 2009). These findings are difficult to explain; however, a potential explanation may be that the MCQ craving scores (at least in part) reflect the inability to control the urge to use cannabis rather than the urge itself, as is suggested by our recent neuroimaging study (Cousijn et al. 2012). Important to note, the relationship between cannabis approach bias and craving was not significant when we controlled the tobacco approach bias. This suggests that craving as measured with the MCQ may be related to a general smoking approach bias rather than a cannabis-specific approach bias. This relationship between cannabis intoxication and a general inhibition of avoidance may have clinical implications. Individuals who are trying to abstain from substance use other than cannabis or tobacco could be advised to give up cannabis as well since the general approach tendencies resulting (or lack of avoidance) from cannabis intoxication may increase chances to relapse. This may, in part, be caused by cross sensitization. For example, it has been shown that an alcohol prime increases the attention bias for cocaine (Montgomery et al. 2010). These inferences are speculative, and further research is needed to verify if cannabis intoxication increases the approach bias for other substances of abuse than cannabis and tobacco. Finally, some limitations must be considered. First, we applied a between- rather than a within-subjects design which may have confounded the findings. This quasiexperimental study should be repeated in a more controlled fashion with a counterbalanced within-subject design; however, such an approach is more difficult to implement as it requires a two-time participation of the heavy cannabis users. Second, this study was conducted at a single location (the coffee shop). To make inferences about the influence of a real-life setting on approach bias and response inhibition, one should compare the coffee shop setting to a laboratory

9 Psychopharmacology (2013) 229: setting. Third, we did not include an objective THC measure, which makes it impossible to control (sub)acute effects of THC intoxication. Moreover, we only included a self-report of satiation, not a self-report of prior cannabis use. Unfortunately, prior cannabis use may have affected the results. The reliability of self-reports is questionable, especially in individuals with a substance use disorder (Goldstein et al. 2009). In addition, THC levels in cannabis cigarettes greatly vary between participants depending on type and amount of cannabis inside the cigarette. Including an absolute measure of cannabis intoxication and/or controlling the THC intake is therefore recommended in future studies. Fourth, the age difference between the pre-cannabis and post-cannabis groups could be confounded. Including age as a covariate did not change the overall results; however, age can be expected to correlate positively with duration of heavy use and cumulative lifetime cannabis use. Unfortunately, we did not assess cumulative lifetime cannabis use, which may have differed between groups given the age difference between groups. Fifth, we did not measure history of any other drug than alcohol, cannabis, and tobacco, which may have confounded our findings. Finally, only the CUDIT-R was used as a measure of cannabisuse-related problems. Cognitive biases like the approach bias may be higher in dependent versus non-dependent cannabis users. There was no significant relationship between the CUDIT-R and the cannabis approach bias, but a clinical diagnosis of cannabis dependence was not assessed. Including a clinical assessment of cannabis use disorder is therefore recommended in future studies. In conclusion, cannabis intoxication in a real-life setting inhibited general avoidance. Expression of the cannabis approach bias appeared not to be modulated by satiation or response inhibition. Thereby, this field study increased our understanding and, at the same time, raised new issues regarding the motivational mechanisms underlying an approach bias for cannabis and other psychoactive substances. Acknowledgments This research was supported by the National Science Foundation (NWO) Vici grant awarded to R.W. Wiers. The authors report no conflict of interest. References Adamson SJ, Kay-Lambkin FJ, Baker AL, Lewin TJ, Thornton L, Kelly BJ et al (2010) An improved brief measure of cannabis misuse: the Cannabis Use Disorders Identification Test Revised (CUDIT-R). Drug Alcohol Depend 110: Bechara A (2005) Decision making, impulsive control and loss of willpower to resist drugs: a neurocognitive perspective. Nat Neurosci 8: Bradley BP, Mogg K, Wright T, Field M (2003) Attentional bias in drug dependence: vigilance for cigarette-related cues in smokers. Psychol Addict Behav 17:66 72 Bradley BP, Field M, Healy H, Mogg K (2008) Do the affective properties of smoking-related cues influence attentional and approach biases in cigarette smokers? J Psychoparmacol 22: Christiansen P, Rose AK, Cole JC, Field M (2012) A comparison of the anticipated and pharmacological effects of alcohol on cognitive bias executive function, craving, and ad-lib drinking. J Psychopharmacol. doi: / Cortina JM (1993) What is coefficient alpha? An examination of theory and applications. J Appl Psychol 78: Cousijn J, Goudriaan AE, Wiers RW (2011) Reaching out towards cannabis: approach-bias in heavy cannabis users predicts changes in cannabis use. Addiction 106: Cousijn J, Goudriaan AE, Ridderinkhof KR, Veltman DJ, Van den Brink W, Wiers RW (2012) Approach-bias predicts the development of cannabis problem severity: results from a prospective fmri study. PLoS One 7:e42394 Crean RD, Crane NA, Mason BJ (2011) An evidence based review of acute and long-term effects of cannabis use on executive cognitive functions. J Addict Med 5:1 8 Di Chiara G (2000) Role of dopamine in the behavioural actions of nicotine related to addiction. Eur J Pharmacol 393: Duka T, Townshend JM (2004) The priming effect of alcohol pre-load on attentional bias to alcohol-related stimuli. Psychopharmacol 176: Everitt BJ, Robbins TW (2005) Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nat Neurosci 8: Field M, Cox WM (2008) Attentional bias in addictive behaviors: a review of its development, causes, and consequences. Drug Alcohol Depend 97:1 20 Field M, Mogg K, Bradley BP (2005) Alcohol increases cognitive biases for smoking cues in smokers. Psychopharmacol 180:63 72 Field M, Kiernan A, Eastwood B, Child R (2008) Rapid approach responses to alcohol cues in heavy drinkers. J Behav Ther Exp Psychiatry 39: Field M, Munafo MR, Franken IHA (2009) A meta-analytic investigation of the relationship between attentional bias and subjective craving in substance abuse. Psychol Bull 135: Flint A, Raben A, Blundell JE, Astrup A (2000) Reproducibility, power and validity of visual analogue scales in assessment of appetite sensations in single test meal studies. Int J Obes Relat Metab Disord 24:38 48 Franken IHA (2003) Drug craving and addiction: integrating psychological and neuropsychopharmacological approaches. Prog Neuropsychopharmacol Biol Psychiatry 27: Geiselman PJ, Anderson AM, Dowdy ML, West DB, Redmann SM, Smith SR (1998) Reliability and validity of a macronutrient selfselection paradigm and a food preference questionnaire. Physiol Behav 63: Goldstein RZ, Craig AD, Bechara A, Garavan H, Childress AR, Paulus MP, Volkow ND (2009) The neurocircuitry of impaired insight in drug addiction. Trends Cogn Sci 13: Grenard JL, Ames SL, Wiers RW, Thush C, Sussman S, Stacy AW (2008) Working memory capacity moderates the predictive effects of drug-related associations on substance use. Psychol Addict Behav 22: Haney M, Ward AS, Comer SD, Foltin RW, Fischman MW (1999) Abstinence symptoms following smoked marijuana in humans. Psychopharmacol (Berl) 141: Hart CL, van Gorp W, Haney M, Foltin RW, Fischman MW (2001) Effects of acute smoked marijuana on complex cognitive performance. Neuropsychopharmacology 25: Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO (1991) The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict 86:

10 176 Psychopharmacology (2013) 229: Heisman SJ, Evans RJ, Singleton EG, Levin KH, Copersion ML, Gorelick DA (2009) Reliability and validity of a short form of the Marijuana Craving Questionnaire. Drug Alcohol Depend 102:35 50 Houben K, Wiers RW (2009) Response inhibition moderates the relationship between implicit associations and drinking behavior. Alcohol Clin Exp Res 33: Houben K, Wiers RW, Jansen A (2011) Getting a grip on drinking behavior: training working memory to reduce alcohol abuse. Psychol Sci 22:1 8 Jones BT, Jones BC, Smith H, Copley N (2003) A flicker paradigm for inducing change blindness reveals alcohol and cannabis information processing biases in social users. Addiction 98: Krompinger JW, Simons RF (2011) Cognitive inefficiency in depressive undergraduates: Stroop processing and ERP s. Biol Psychol 86: Lenné MG, Dietze PM, Triggs TJ, Walsmley S, Murphy B, Redman JR (2010) The effects of cannabis and alcohol on simulated arterial driving: influences of driving experience and task demand. Accid Anal Prev 42: McKay D, Schare ML (1999) The effects of alcohol and alcohol expectancies on subjective reports and physiological reactivity: a meta-analysis. Addict Behav 24: Montgomery C, Field M, Atkinson AM, Cole JC, Goudie AJ, Sumnall HR (2010) Effects of alcohol preload on attentional bias towards cocaine-related cues. Psychopharmacol 210: Ostafin BS, Palfai TP (2006) Compelled to consume: the Implicit Association Test and automatic alcohol motivation. Psychol Addict Behav 20: Palfai TP (2006) Activating action tendencies: the influence of action priming on alcohol consumption among male hazardous drinkers. J Stud Alcohol 67: Palfai TP, Ostafin BD (2003) Alcohol-related motivational tendencies in hazardous drinkers: assessing implicit response tendencies using the modified IAT. Behav Res Ther 41: Peeters M, Wiers RW, Monshouwer K, Janssen T, Vollebergh WAM (2012) Automatic processes in at-risk adolescent: the role of alcohol-approach tendencies and response inhibition in drinking behavior. Addiction 107: Robinson TE, Berridge KC (1993) The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Rev 18: Saunders JB, Aasland OG, Babor TF, de la Fuente JR, 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: Schoenmakers TM, Wiers RW (2010) Craving and attentional bias respond differently to alcohol priming: a field study in the pub. Eur Addict Res 16:9 16 Schoenmakers TM, Wiers RW, Field M (2008) Effects of a low dose of alcohol on cognitive biases and craving in heavy drinkers. Psychopharmacol 197: Stacy AW, Wiers RW (2010) Implicit cognition and addiction: a tool for explaining paradoxal behaviour. Ann Rev Clin Psychol 6: Thush C, Wiers RW, Ames SL, Grenard JL, Sussman S, Stacy AW (2008) Interactions between implicit and explicit cognition and working memory capacity in the prediction of alcohol use in at-risk adolescents. Drug Alcohol Depend 94: Van der Elst W, Van Boxtel MPJ, Van Breukelen GJP, Jolles J (2006) The Stroop color word test: influence of age, sex and education; and normative data for a large sample across the adult age range. Assessment 13:62 79 Watson P, De Wit S, Hommel B, Wiers RW (in press) Motivational mechanisms and outcome expectancies underlying the approach bias towards addictive substances. Front Cogn Wiers RW, Rinck M, Dictus M, van den Wildenberg E (2009) Relatively strong automatic appetitive action-tendencies in male carriers of the OPRM1G-allele. Genes Brain Behav 8: Wiers RW, Rinck M, Kordts R, Houben K, Strack F (2010a) Retraining automatic action-tendencies to approach alcohol in hazardous drinkers. Addiction 105: Wiers RW, Ames SL, Hofmann W, Krank M, Stacy AW (2010b) Impulsivity, impulsive and reflective processes and the development of alcohol use and misuse in adolescence and young adults. Front Psychol 2010:1 12