The effects of priming restrained versus disinhibited behaviour on alcohol-seeking in social drinkers

Drug and Alcohol Dependence 113 (2011) 55–61

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The effects of priming restrained versus disinhibited behaviour on alcohol-seeking in social drinkers Andrew Jones a , Ramona Guerrieri b , Gordon Fernie a , Jon Cole a , Andrew Goudie a , Matt Field a,∗ a b

School of Psychology, University of Liverpool, Liverpool, L69 7ZA, UK Department of Clinical Psychological Science, Maastricht University, Maastricht, The Netherlands

a r t i c l e

i n f o

Article history: Received 14 December 2009 Received in revised form 9 July 2010 Accepted 9 July 2010 Available online 17 August 2010 Keywords: Alcohol Impulsivity Response inhibition Stop-Signal task

a b s t r a c t Background: Deficient response inhibition (disinhibition) may play a causal role in alcohol abuse, with impaired inhibition occurring prior to, and acting as a risk factor for, subsequent alcohol problems. We experimentally primed either disinhibited or restrained behaviour while participants completed a StopSignal task, before examining the effects on alcohol-seeking behaviour. Methods: Fifty three social drinkers completed a Stop-Signal task following instructions that either emphasised rapid responding at the expense of successful inhibition (Disinhibition group) or vice versa (Restrained group). Subsequent ad lib alcohol-seeking was measured with a bogus taste test. Results: As predicted, participants in the Disinhibition group consumed more beer during the taste test compared to participants in the Restrained group. Furthermore, within the Restrained group only, correlations indicated that those participants who responded more cautiously during the Stop-Signal task subsequently consumed less beer. Conclusions: An experimental manipulation of response set during a response inhibition task, emphasising either restrained or disinhibited responding, has a causal influence on alcohol-seeking behaviour in social drinkers. © 2010 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Addiction and substance abuse are associated with elevated ‘impulsivity’. Impulsivity can be defined and assessed in a number of different ways, and indeed it does not appear to represent a unitary construct (Reynolds et al., 2006). It is often recognised as an umbrella term for constructs such as poor planning, sensitivity to immediate rather than delayed consequences of behaviour, and poor response inhibition (De Wit, 2009). A number of recent reviews have established that substance abusers tend to behave more impulsively on behavioural tasks designed to measure the constructs mentioned above, and their self-reported impulsivity is also higher, compared to non-user control groups (De Wit, 2009; Verdejo-Garcia et al., 2008). For example, behavioural tasks such as the Go/No-Go and Stop-Signal tasks are used to assess inhibitory control (or response inhibition), which is defined as the ability to inhibit a prepotent response. Results indicate that substance abusers tend to show deficient response inhibition on these tasks, compared to controls (e.g. Goudriaan et al., 2006, see VerdejoGarcia et al., 2008).

∗ Corresponding author. Tel.: +44 0151 7941137; fax: +44 0151 7942945. E-mail address: mfi[email protected] (M. Field). 0376-8716/$ – see front matter © 2010 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.drugalcdep.2010.07.006

Two important issues in relation to the association between impulsivity and substance abuse warrant consideration in the context of the present study. Firstly, associations between increased impulsivity and heavy drinking have been reported among nondependent ‘social’ drinkers, and as such increased impulsivity does not appear to be restricted to dependent populations. For example, adolescent heavy drinkers show increased rates of delay discounting compared to their light drinking counterparts (Field et al., 2007a), and among young adults, impaired response inhibition is associated with increased frequency of alcohol consumption (Colder and O’Connor, 2002; Nederkoorn et al., 2009). Secondly, although individual differences in impulsivity are considered fairly stable (perhaps akin to personality traits), it is apparent that levels of impulsivity can fluctuate within individuals. For example, selfreported trait impulsivity tends to decline during early adulthood, with a slow rate of decline being associated with the development of alcohol problems during this period (Littlefield et al., 2009). De Wit (2009) also raises the prospect of “abrupt environmental, physiological or emotional events (that) may cause transient ‘state’ changes in either self-control or inhibition that may result in re-initiation of drug use” (p. 28). We refer to this as ‘state’ impulsiveness. For example, response inhibition is impaired in research participants after administration of a priming dose of alcohol, compared to after administration of a placebo dose (e.g. Marczinski et al., 2005), and this disinhibited state produced by acute alcohol

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intoxication may lead to loss of control over alcohol consumption (see Field et al., 2010, for a review). Much of the research linking inhibitory control to substance abuse is cross-sectional, making it difficult to establish whether chronic drug use somehow impairs inhibitory control, if poor inhibitory control pre-dates substance involvement and possibly acts as a risk factor for the development of substance abuse problems, or if both processes are involved. The majority of relevant theoretical models raise the possibility that chronic use of psychoactive substances could lead to damage to the prefrontal cortex, leading to an inability to regulate behaviour, which may in turn impair the ability to regulate substance use (Goldstein and Volkow, 2002; Jentsch and Taylor, 1999; Lyvers, 2000). However, a number of recent clinical and preclinical findings suggest that individual differences in inhibitory control exist prior to involvement in substance use, and poor inhibitory control seems to be a risk factor for the development of substance abuse problems later in life. This has been demonstrated in animal models (Anker et al., 2009; Belin et al., 2008; Dalley et al., 2007; Economidou et al., 2009; Diergaarde et al., 2008), and also in longitudinal human studies in which adolescents are tracked over time (Giancola and Tarter, 1999; McGue et al., 2001; Tarter et al., 2003; Wong et al., 2006). More recently, researchers have examined whether individual differences in inhibitory control are associated with drug-seeking behaviour in the laboratory. For example, Weafer and Fillmore (2008) examined the association between alcohol-induced impairment of inhibitory control and ad lib alcohol-seeking in the laboratory. Previous work from this group (e.g. Marczinski et al., 2005) demonstrates that participants have impaired response inhibition after consumption of moderate doses of alcohol, and this may be related to loss of control over drinking during alcohol binges. Weafer and Fillmore (2008) demonstrated a significant positive correlation between the extent to which a moderate dose of alcohol impaired inhibitory control and subsequent ad lib consumption of alcohol when tested some days later in a sober state. This study was therefore the first to demonstrate a direct link between impaired inhibitory control and alcohol-seeking behaviour in the laboratory, and in this regard this is a very important study. However, the study does not establish that disinhibited ‘states’ in sober participants can play a causal role in alcohol-seeking behaviour, and it is this gap in the literature which we aimed to address in the present study. While it may be difficult (if not impossible) to experimentally manipulate inhibitory control among sober participants in the laboratory, a recent study does demonstrate that it is possible to prime a disinhibited (versus a restrained) state, or mental set, through the use of differential task instructions before participants complete an inhibitory control task. The standard Stop-Signal task (Logan et al., 1997) sets up a response conflict in that participants are required to respond rapidly to target (‘Go’) stimuli, while also successfully inhibiting their response whenever a ‘Stop’ signal is presented. (Guerrieri et al., 2009, study 2) presented instructions to participants that either stressed the importance of rapid responding to targets (at the expense of successful inhibition; ‘Impulsivity’ group), or stressed the importance of successful inhibition (at the expense of rapid responding; ‘Inhibition’ group) before those participants completed a Stop-Signal task. This manipulation of instructions led to differential performance on the Stop-Signal task, as predicted (participants in the Inhibition group were more successful at inhibiting responses on ‘Stop’ trials, and they were also slower to respond on ‘Go’ trials, compared to participants in the Impulsivity group). Importantly, results indicated that, among participants who were not currently on a calorie-restricted diet, the manipulation influenced food intake during a subsequent taste test: participants in the Impulsivity group consumed more food than participants in the Inhibition group. These results thus demonstrate that (a) a disinhibited (versus restrained) mental set can be tem-

porarily induced through the use of differential instructions and (b) such manipulations can have a causal influence on motivated behaviour (food intake in this study). In the present study, we adapted the method used by Guerrieri et al. (2009) to explore the influence of priming a disinhibited versus restrained mental set on alcohol-seeking behaviour in a sample of social drinkers. Participants were randomly assigned to experimental groups that differed in the degree of emphasis that was placed on the importance of successful inhibition, before they completed a Stop-Signal task. All participants were instructed that the task required both rapid responding on Go trials and successful inhibition on Stop trials. However, participants in our Disinhibition group were informed that rapid responding was the most important task, whereas participants in our Restraint group were informed that successful inhibition was more important. After this manipulation, we provided participants with access to beer and a soft drink in the context of a bogus ‘taste test’ in order to assess their alcoholseeking behaviour. Our primary hypothesis was that participants in the Disinhibition group would consume more beer than participants in the Restraint group. We also assessed the effects of our experimental manipulation on subjective craving, and our secondary hypothesis was that the experimental manipulation would influence self-reports of the desire to drink alcohol. Finally, given recent evidence which suggests that the association between heavy drinking and executive dysfunction (including impaired response inhibition) is stronger in females than in males (Nederkoorn et al., 2009; Scaife and Duka, 2009; Townshend and Duka, 2005), we included gender as a factor in our data analyses in order to examine whether it would moderate the hypothesised effects of our experimental manipulation. 2. Method 2.1. Participants Fifty-three participants (24 males, 29 females) were recruited from the undergraduate student population at the University of Liverpool. Inclusion criteria included social drinking (regular consumption of at least one alcoholic drink per week) and liking for beer, with no history of self-reported alcohol abuse or alcohol problems, and being aged 18 years or older. All participants provided informed consent before taking part in the study, which was approved by the University of Liverpool Research Ethics Committee. 2.2. Materials and equipment The Stop-Signal task (Logan et al., 1997) was used to manipulate response inhibition. The task was programmed in Visual Basic 6.0 and presented on a laptop computer. Participants wore headphones in order to minimise background noise and to present auditory ‘Stop’ stimuli (see below). Each trial began with the presentation of a white fixation cross in the centre of a black screen for 500 ms. Immediately after the fixation cross was removed, one of two target stimuli were presented in the centre of the computer screen, the letter ‘X’ or the letter ‘O’, for 1000 ms. Participants were instructed to rapidly respond to target stimuli by pressing the correspondingly labelled key on the computer keyboard. On 75% of trials (‘Go’ trials), the target stimulus was uninterrupted, but on 25% of trials (‘Stop’ trials), a computer generated auditory tone was presented for 800 ms. Participants were instructed (but see Section 2.3, below) that they should inhibit their response to the target stimuli whenever they heard the auditory tone. As recommended (Band et al., 2003), the adjusting procedure was used (see Logan et al., 1997), in which the interval between onset of the visual Go stimulus and onset of the auditory Stop stimulus was adjusted based on participants’ task performance. On the first stop-signal trial, the Stop interval was fixed at 250 ms. If participants were able to successfully inhibit their response, the Stop interval was increased by 50 ms on the next Stop trial, thereby making inhibition more difficult. However, if participants were unable to successfully inhibit their response, the Stop interval was reduced by 50 ms, thereby making inhibition easier. Stop intervals were reset at 250 ms at the start of each block and adjusted over successive trials in order to obtain a stable estimate of ‘Stop Latency’, defined as the Stop interval at which participants could successfully inhibit approximately 50% of their responses. The minimum Stop interval was 0 ms, in which case the visual Go stimulus and auditory Stop stimulus were presented simultaneously. The task consisted of a total of 192 trials split into six blocks of 32 trials. There were 24 Go trials and 8 Stop trials in each block, and trials were presented in a new random order in each block. The first two blocks were practice blocks and were not considered for analysis (see Bitsakou et al., 2008). Participants

A. Jones et al. / Drug and Alcohol Dependence 113 (2011) 55–61 were provided with feedback in the first block only. Trials were separated by an inter-trial interval of 1000 ms, during which the screen was blank. 2.3. Procedure All testing took place in laboratories within the School of Psychology at the University of Liverpool. All experimental sessions took place between 2 p.m. and 6 p.m. In an effort to reduce participants’ awareness of the aims of the study, participants were deceived about the true nature of the experiment. They were informed that they were taking part in an experiment investigating the relationship between reaction times and taste perception. Participants then provided informed consent and basic demographic information before completing a battery of questionnaires. The initial questionnaire battery included the Timeline Followback questionnaire, a one-week retrospective diary in which participants recorded their alcohol consumption over the past week (Sobell and Sobell, 1992), and The Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993), which is a screening questionnaire for the detection of drinking habits that are hazardous or harmful to health. The AUDIT provides a total score ranging from 0 to 40, with higher scores indicative of more severe alcohol use disorders. Participants also completed the ‘right now’ version of the Approach and Avoidance of Alcohol Questionnaire (AAAQ-now; McEvoy et al., 2004), which provides scores on three subscales: ‘inclined/indulgent’, ‘obsessed/compelled’ and ‘resolved/regulated’. Finally, participants completed the Barratt Impulsiveness Scale (BIS) version 11 (Patton et al., 1995), which provides scores on three subscales of trait impulsivity, termed attentional, motor, and non-planning impulsiveness. After completing the questionnaire battery, participants completed the Stop-Signal task, as described above. Participants were randomly allocated to ‘Disinhibition’ or ‘Restraint’ groups, and group allocation was balanced across gender. All participants were given the same general information about how to complete the task, including instructions to respond rapidly to ‘Go’ stimuli but to attempt to inhibit their responses whenever they heard an auditory tone (the ‘Stop’ stimulus). However, the wording of all subsequent instructions differed depending on which experimental group participants had been allocated to. These modified instructions were based on those used by (Guerrieri et al., 2009, study 2). Participants allocated to the Disinhibition group were instructed that rapid responding to ‘Go’ stimuli was the most important task; they should try to inhibit responding to ‘Stop’ stimuli if possible, but this was less important than rapid responding to the ‘Go’ stimuli. Conversely, participants in the Restraint group were told that successful inhibition of responses in response to ‘Stop’ signals was the most important task; they should try to respond quickly to ‘Go’ stimuli if possible, but this was less important than successful inhibition on Stop trials. Participants in both groups then completed an identical version of the Stop-Signal task, as described above. Immediately after completion of the Stop-Signal task (which took 10–15 min to complete), participants completed an additional AAAQ-now before completing the bogus taste test procedure. Participants were provided with 330 ml bottles of fruit juice drink (Orange and Passion Fruit flavour J2 O) and non-alcoholic beer (Beck’s alcohol free). We opted to use non-alcoholic beer instead of real beer so that our taste test procedure would yield a measure of alcohol-seeking that was uncontaminated by the alcohol ‘priming’ effect (see De Wit, 1996), which refers to the observation that the pharmacological effects of alcohol can motivate further alcohol-seeking. Therefore, any between-group differences in beer consumption could be specifically attributed to the instructions provided, and would not be contaminated by differential alcohol priming effects between the groups. Importantly, participants were informed that the beer was real (rather than non-alcoholic), and the brand used was chosen on the basis of pilot testing which revealed that participants could not discriminate it from alcohol-containing Beck’s beer on the basis of taste. To increase the effectiveness of the deception, the labels and caps were removed from both bottles before they were presented to participants. As in previous studies from our group in which the bogus taste test procedure has been used (e.g. Field et al., 2007b; Field and Eastwood, 2005) participants were instructed to rate the taste of each of the drinks on a number of dimensions, and to consume as much or as little of each drink as they wished in order to make their ratings. The four different rating continuums were pleasant-unpleasant, flat-gassy, bitter-sweet, and tastelessstrong tasting. Participants were given as much time as they required to complete the taste ratings, and to indicate to the experimenter when they had finished. Upon completion, the experimenter removed the two bottles from the participants’ view before later measuring the amount of each drink that had been consumed. Participants were thoroughly debriefed before leaving the laboratory; they received either course credit or financial compensation for their travel expenses and their time (£5). 2.4. Data reduction and analysis Data from the Stop-Signal task were analysed according to previously established criteria (see Band et al., 2003; Logan et al., 1997). To eliminate outliers on Go trials, reaction times were excluded if they were faster than 200 ms, slower than 2000 ms, and then if they were 3 SDs above the individual mean; ‘Go Reaction Time’ was then calculated on the remaining trials. Secondly, the total number of incorrect responses on Go trials, either pressing the incorrect key or not responding when required (‘Go Errors’), was calculated and expressed as a percentage of the total number of Go trials. Third, the average Stop Latency across Stop trials was calculated.

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Fourth, the number of Inhibition Errors was calculated as the percentage of Stop trials on which participants failed to inhibit their response. Finally, the Stop-Signal Reaction Time (SSRT) was calculated based on previously described methodology (see Bitsakou et al., 2008). SSRT provides an estimate of the time required to stop an initiated Go response (see Band et al., 2003), and longer SSRTs represent impaired response inhibition. Alcohol-seeking behaviour was inferred from the taste test procedure based on the methods used in previous reports (e.g. Field et al., 2007b; Field and Eastwood, 2005). We calculated the volume of beer consumed, expressed as a percentage of total fluid consumption (beer and fruit juice drink combined). Data were analysed as follows: firstly, in order to test for pre-existing differences between participants allocated to ‘Disinhibited’ versus ‘Restrained’ groups, we conducted Independent samples t-tests to compare groups in terms of age, self-reported weekly alcohol consumption, scores on the AUDIT, scores on the ‘inclined/indulgent’, ‘obsessed/compelled’ and ‘resolved/regulated’ subscales of the AAAQ, and scores on the ‘motor impulsivity’, ‘attentional impulsivity’, and ‘cognitive impulsivity’ subscales of the BIS-11. To test the effectiveness of the experimental manipulation, Univariate Analyses of Variance (ANOVA) were used to analyse Go Reaction time, Go Errors, Stop Latency, Inhibition Errors and SSRT, with Experimental Group and Participant Gender as the between-subject factors. In order to test our primary experimental hypotheses, consumption data from the bogus taste test procedure was analysed using a Univariate ANOVA, with beer consumption as a percentage of total fluid consumption as the dependent variable, and Experimental Group and Participant Gender as the between-subject factors. In order to examine whether the experimental manipulation influenced the total volume of fluid consumed (beer and soft drink combined), this variable was analysed in the same way in a separate analysis. Self-reported ‘pleasantness’ ratings for the two drink types were analysed using a mixed design ANOVA, with Drink Type (beer/softdrink) as the within-subject factor, and Experimental Group and Participant Gender as between-subject factors. Subjective craving data from the AAAQ subscales were analysed using mixed design ANOVA, with within-subjects factor of Time (before experimental manipulation, after experimental manipulation) and AAAQ subscale (inclined-indulgent, obsessed-compelled, and resolved-regulated), and betweensubject factors of Experimental Group and Participant Gender. Finally, to explore the associations between Stop task performance and alcohol-seeking during the taste test, Stop task indices were correlated with beer consumption using Pearson correlations. These correlations were performed separately for Disinhibition and Restraint groups.

3. Results 3.1. Participant characteristics Descriptive statistics for participants assigned to Disinhibition and Restraint groups are shown in Table 1. Independent samples t-tests were used to examine whether groups differed on any of these variables; some variables (age, weekly alcohol consumption, and the AAAQ subscales) were log transformed before analysis to improve their distributions. There were no statistically significant differences between groups on any of these variables, although there was a trend (t(51) = 1.77, p = 0.08) for participants in the Disinhibition group to have a higher level of weekly alcohol consumption compared to participants in the Restraint group. Given this non-significant group difference, the primary analyses described below were repeated with weekly alcohol consumption entered as a covariate; however inclusion of the covariate did not alter any of the primary results. The results presented below did not include weekly alcohol consumption as a covariate. 3.2. Performance on the Stop-Signal task Descriptive statistics are shown in Table 2. The ‘Go Errors’ variable was log transformed before analysis to reduce skewness. Each variable was then analysed using a separate univariate ANOVA, with Experimental Group (2: Disinhibition/Restraint) and Participant Gender (2: Male/Female) as between-subject factors. Effect sizes (Cohen’s d, Thalheimer and Cook, 2002) are also reported. There were significant main effects of Experimental Group on Go Reaction Time (F(1, 53) = 12.04, p < .01, d = .97), Stop Latency (F(1, 53) = 17.51, p < .01, d = 1.17), and Inhibition Errors (F(1, 53) = 15.98, p < .01, d = 1.12). However, groups did not differ on Go Errors (F(1, 53) = 0.10, p = .76, d = .09) or Stop-Signal Reaction Time (F(1,

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Table 1 Participant characteristics based on questionnaires administered at the beginning of the experiment, shown separately for participants allocated to Disinhibited and Restrained groups. Values are mean ± SD.

Age (years) Gender ratio (M:F) Alcohol units/week AUDIT BIS—attention BIS—motor BIS—non-planning AAAQ—inclined/indulgent AAAQ—obsessed/compelled AAAQ—resolved/regulated

Disinhibited group

Restrained group

t (df = 51)

p value

19.89 ± 13: 14 30.11 ± 12.63 ± 10.56 ± 13.43 ± 14.22 ± 5.27 ± 1.04 ± 1.41 ±

20.23 ± 11: 15 22.46 ± 13.85 ± 10.38 ± 13.79 ± 14.63 ± 4.77 ± 1.00 ± 1.37 ±

2.83

0.41

0.68

9.37 6.73 1.30 2.19 1.40 1.38 1.06 1.72

1.77 0.80 0.51 0.66 1.09 1.32 0.48 0.85

0.08 0.43 0.62 0.51 0.28 0.19 0.64 0.40

1.89 15.34 4.11 1.16 1.78 1.34 1.23 0.83 1.15

Notes: Alcohol units/week: self-reported weekly alcohol consumption, in UK units (1 UK unit = 10 ml or 8 g of pure alcohol); AUDIT: score on the Alcohol Use Disorders Identification Test, higher values indicate increased likelihood of alcohol problems; BIS: scores on the three subscales of the Barratt Impulsivity Scales, higher values indicate increased impulsivity; AAAQ: scores on the three subscales of the Approach and Avoidance of Alcohol Questionnaire, higher values indicate higher craving.

53) = 2.33, p = .13, d = .43). There were no significant main effects of Gender, or Experimental Group × Gender interactions, for any of the variables (Fs < 1.27, ps > .20). To summarize this overall pattern of results, compared to participants in the Restraint group, participants in the Disinhibition group were faster to respond to Go stimuli, yet they made more Inhibition Errors, which resulted in their average Stop Latency being reduced by the tracking algorithm in the computer program. However, there were no group differences in Go Errors or Stop-Signal Reaction Time (SSRT). The latter finding is perhaps unsurprising when one considers that SSRT is calculated by subtracting mean Stop Latency from mean Go Reaction Time on each block: given that both Stop Latency and Go Reaction Time were reduced among participants in the Disinhibition group compared to the Restraint group, these effects appeared to cancel each other out thereby leading to no significant group difference in SSRT. 3.3. Alcohol-seeking: beer and soft-drink consumption during the taste test Beer consumption (as a percentage of total fluid consumed) was analysed using a Univariate ANOVA, with between-subject factors of Experimental Group and Participant Gender. The main effect of Experimental Group was highly statistically significant (F(1, 53) = 8.68, p < .01, d = .83), as participants in the Disinhibition group consumed significantly more beer than participants in the Restraint group. The Experimental Group × Participant Gender interaction approached statistical significance (F(1, 53) = 3.18, p = .08). As shown in Figure 1, there was a trend for the difference between Disinhibition and Restraint groups to be larger among Female participants compared to Male participants, although this trend fell short of statistical significance. The total volume of fluid consumed (beer and soft-drink combined) was analysed in the same way; this revealed only a significant main effect of Gender (F(1, 53) = 6.85, p < .05; Males consumed more overall). The main effect of Experimental Group, and the Experimental Group × Gender interaction, were not statistically significant (F(1, 53) = 0.30, p > .1 and F(1, 53) = 1.07, p > .1, respectively). Table 2 Dependent measures from the Stop-Signal Reaction Time task, shown separately for participants in the Disinhibited group and the Restrained group. Values are mean ± SD. Disinhibited group Go reaction time (ms) Go errors (%) Inhibition errors (%) Stop latency (ms) SSRT (ms)

580.05 1.78 52.55 216.73 362.67

± ± ± ± ±

126.92 1.72 17.01 82.24 67.83

Restrained group 698.23 2.38 35.22 307.46 390.27

± ± ± ± ±

114.95 3.34 12.98 68.78 68.32

Fig. 1. Beer consumption (as a percentage of total fluid consumed) among male and female participants in the Disinhibited and Restrained groups. Values are mean ± SEM.

Pleasantness ratings for the two drinks were analysed using a Mixed Design ANOVA, with a within-subject factor of drink type (beer/soft-drink) and between-subject factors of Experimental Group and Participant Gender. The main effect of drink type was statistically significant (F(1, 49) = 10.78, p < .01), reflecting higher pleasantness ratings for the soft drink compared to beer. There were no other significant main effects or interactions (Fs < 2.92, ps > 0.9). Pleasantness ratings for soft-drink and beer, respectively were 72.85 (SD = 16.81) vs. 61.96 (SD = 16.84) in the Disinhibition Group, and 66.65 (SD = 23.27) vs. 52.35 (SD = 24.16) in the Restraint Group. To briefly summarize results from the taste test: the Disinhibition Group consumed more beer (as a percentage of total fluid) than the Restraint Group. However, groups did not differ on the total volume of fluid consumed, which suggests that the experimental manipulation did not lead to a nonspecific increase in consumptive behaviour. Furthermore, there were no significant effects of the experimental manipulation on self-reported pleasantness of the drinks, which suggests that the effects of the experimental manipulation on alcohol-seeking occurred independently of participants’ liking of the drinks. 3.4. Subjective alcohol craving: scores on the AAAQ Scores on the three AAAQ subscales were log transformed before analysis to reduce skewness. To examine the influence of the experimental manipulation on subjective craving, data were analysed using a 3 × 2 × 2 × 2 mixed design ANOVA, with within-

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subject factors of AAAQ Subscale (3: inclined-indulgent/obsessedcompelled/resolved-regulated), Time (2: beginning of study/after experimental manipulation), and between-subject factors of Experimental Group (2) and Participant Gender (2). There were significant main effects of Participant Gender (F(1, 49) = 4.52, p < .05; craving was higher in males), AAAQ Subscale (F(2, 48) = 184.52, p < .01; scores on the inclined-indulgent subscale were higher than scores on the other two subscales) and Time (F(1, 49) = 6.06, p < .05; craving scores were lower after the experimental manipulation compared to at the beginning of the study). None of the other main effects or interactions were statistically significant, and importantly there were no significant main effects or interactions involving Experimental Group (Fs < 1.74, ps > .1). Therefore, the experimental manipulation did not influence subjective alcohol craving. Data are not shown. 3.5. Associations between Stop task performance and alcohol-seeking behaviour In the Disinhibition group, there were no significant correlations between Stop task indices and beer consumption during the taste test (as a percentage of total fluid consumed) (rs < −.24, ps > .1). However, in the Restraint group, beer consumption was significantly correlated with several Stop task indices: Go Reaction Time (r = −.40, p < .01), Stop Latency (r = −.42, p < .01) and Inhibition Errors (r = .43, p < .01). However, beer consumption was not related to Go Errors (r = .14, p > .1) or Stop-Signal Reaction Time (r = −.25, p > .1). This pattern of correlations demonstrates that, among participants in the Restraint group, those who responded more slowly on Go trials, and who made fewer Inhibition errors on Stop trials, tended to drink less beer during the subsequent taste test procedure. 4. Discussion Results from this study indicate that priming a disinhibited versus a restrained state, or mental set, through differential task instructions has a causal influence on alcohol-seeking behaviour among social drinkers. Participants who were instructed to respond in a disinhibited way on a Stop-Signal task subsequently consumed more beer than participants who had been instructed to respond with restraint. This study is therefore the first to demonstrate that experimental priming of a disinhibited mental set among sober participants can contribute to subsequent alcohol-seeking behaviour. Importantly, among participants in the Restraint group, in whom the importance of successful inhibition was emphasised, we found that those participants who responded more cautiously during the Stop-Signal task subsequently consumed less alcohol. Finally, our experimental manipulation did not influence self-reported alcohol craving, despite the aforementioned effects on alcohol-seeking behaviour. The primary finding was that providing instructions to participants to either respond quickly (Disinhibition group) or respond with restraint (Restraint group) during the Stop-Signal task appeared to influence their ad lib beer consumption during a subsequent bogus taste test: participants in the Disinhibition group consumed more beer than participants in the Restraint group. Our experimental manipulation was taken directly from (Guerrieri et al., 2009, study 2), who found that participants in their Disinhibition group consumed more calories than participants in their Restraint group. Taken together, these results suggest that temporary disinhibited states may play an important causal role in a variety of motivated behaviours, as well as providing tentative support for theoretical models which posit a key causal role for poor inhibitory control in disorders of motivation such as substance use

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disorders (e.g. (Goldstein and Volkow, 2002; Jentsch and Taylor, 1999) and eating disorders (Dawe and Loxton, 2004). We feel that our results complement and extend data reported in a recent study described by (Weafer and Fillmore, 2008) who demonstrated that a moderate dose of alcohol led to impairments in inhibitory control (relative to placebo), and that individual differences in the extent of this impairment were correlated with subsequent ad lib beer consumption during a bogus taste test. The results reported by (Weafer and Fillmore, 2008) provide a mechanism to explain why individuals may lose control over their alcohol consumption during alcohol binges, because low doses of alcohol consumed during the initial stages of a binge appear to impair inhibitory control, which in turn leads to increased alcohol-seeking behaviour. However, our results suggest that a similar mechanism may be in operation even among sober participants. That is, induction of a mental set in which rapid, disinhibited responding was emphasised led to increases in alcohol-seeking behaviour. We can speculate further on the precise nature of any causal relationship between a temporary disinhibited ‘state’, and alcohol-seeking behaviour. For example, it is possible that our Disinhibition group experienced a disinhibited state, which led to increased alcohol-seeking, or that participants in our Restraint group experienced a cautious and restrained state, which led to reduced alcohol-seeking; a further possibility is that both processes are in operation. As we did not include a no-manipulation control condition in our study, it is difficult to draw any firm conclusions. However, correlational analyses did suggest an intriguing hypothesis. Among participants in the Disinhibited group, indices of Stop task performance were not correlated with beer consumption during the taste test. However, among participants in the Restraint group, multiple Stop task indices were correlated with beer consumption: participants who responded more slowly (and presumably more cautiously) on Go trials, and who were more successful at inhibiting their responses on Stop trials, tended to consume less beer. This suggests that emphasising the importance of successful inhibition during the task leads to a reduction in alcoholseeking behaviour, and those participants who are most successful at engaging inhibitory control under this instructional set are likely to consume less alcohol. These preliminary data indicate that the influence of individual differences in inhibitory control on alcohol consumption is dependent on context. If the context emphasises the need to exercise restraint, then inhibitory control becomes a relevant predictor of alcohol consumption. However, if the context emphasises disinhibited responding, individual differences in inhibitory control are inconsequential. Although our experimental manipulation influenced alcoholseeking behaviour in the taste test, we did not see parallel changes in self-reported alcohol craving. In fact, self-reported alcohol craving in both groups was significantly lower after participants completed the Stop-Signal task (compared to before the task) We are unable to speculate about why craving decreased across the whole sample, but these results do suggest that our experimental manipulation was able to influence alcohol-seeking behaviour without selectively changing subjective craving. This is consistent with theoretical models which posit that craving and drug-seeking behaviour can operate independently, and drug-seeking behaviour can be manipulated in the absence of changes in craving (Tiffany, 1990; Wiers et al., 2007). Future research should identify if experimental manipulations of impulsivity have a greater impact on subsequent alcohol- or other substance-seeking behaviour in individuals with a history of heavy drinking or abuse of other substances. In theory (e.g. Lyvers, 2000) such individuals might be suffering from compromised executive function or inhibitory control as a consequence of extensive substance use, and an experimental method to ameliorate this disinhibition might be expected to have a particularly

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large and beneficial impact on subsequent substance use in these individuals. Furthermore it is important to confirm that the manipulation of task instructions does not lead to differential levels of negative mood (e.g. stress, anxiety, frustration) which might influence alcohol-seeking behaviour. A previous study (Guerrieri et al., 2009) suggests that, while the differential instructions may lead to some differential effects on some aspects of negative mood, these effects are not associated with the effects of the experimental manipulation on motivated behaviour. (Guerrieri et al., 2009), reported that their ‘Restraint’ group had higher scores on a self-report measure of anger-hostility, but lower scores on a self-report measure of impulsivity, compared to their ‘Disinhibition’ group. Importantly, self-reported impulsivity was associated with food intake after the experimental manipulation, but self-reported anger-hostility was not. Similarly, in our own study, individual differences in response inhibition were associated with alcohol-seeking after the experimental manipulation, although we did not measure self-reported negative mood. Future studies should incorporate measures of selfreported negative mood after the experimental manipulation, in order to examine if mood states are differentially affected by task instructions, and if they covary with the primary outcome variable (alcohol-seeking). Finally, future studies on this topic may benefit from the use of alternative experimental designs to the one used here. For example, it may be advantageous to measure performance on the Stop-Signal task after standard instructions before participants complete the task again having received differential instructions such as those described here. Such a method would permit the investigation of whether the differential instructions lead to improvement (or worsening) of task performance, although one disadvantage of this method is that it may create experimental demand effects as participants may become aware of the experimental hypotheses. To summarise, in this experimental study we demonstrated that by manipulating response inhibition through task instructions provided to participants, we were able to influence their subsequent alcohol-seeking behaviour. Participants primed to respond in a disinhibited fashion during a Stop-Signal task consumed more beer than those participants who were primed to respond with restraint. This study adds to a growing body of literature which links components of impulsivity to substance use problems, including alcohol problems, and provides one of the first demonstrations that this link may well be a causal one. The data also highlight that the influence of individual differences in impulsivity on alcohol consumption are context dependent, as individual differences only seem to be important in contexts in which successful inhibition is emphasised. Future research is required to clarify the mechanisms involved and any factors, such as participant gender, which may moderate these effects. Role of funding source Funded by a research grant from the Medical Research Council, reference GO601070, awarded to Matt Field, Jon Cole, and Andrew Goudie. The MRC had no role in the design of the study, the collection, analysis, and interpretation of data, the writing of the report, or the decision to submit the paper for publication. Contributors All authors were involved in the design of the study. Andrew Jones collected the data. Andrew Jones and Matt Field undertook the statistical analysis and prepared the first draft of the manuscript. All other authors contributed to and have approved the final manuscript.

Conflict of interest There are no conflicts of interest

Acknowledgement Supported by a research grant from the Medical Research Council, reference GO601070, awarded to Matt Field, Jon Cole, and Andrew Goudie.

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