Attentional bias towards cannabis cues in cannabis users: A systematic review and meta-analysis

Journal Pre-proof Attentional bias towards cannabis cues in cannabis users: a systematic review and meta-analysis Aisling O’Neill, Bianca Bachi, Sagnik Bhattacharyya

PII:

S0376-8716(19)30496-X

DOI:

https://doi.org/10.1016/j.drugalcdep.2019.107719

Reference:

DAD 107719

To appear in:

Drug and Alcohol Dependence

Received Date:

28 March 2019

Revised Date:

28 October 2019

Accepted Date:

31 October 2019

Please cite this article as: O’Neill A, Bachi B, Bhattacharyya S, Attentional bias towards cannabis cues in cannabis users: a systematic review and meta-analysis, Drug and Alcohol Dependence (2019), doi: https://doi.org/10.1016/j.drugalcdep.2019.107719

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Title: Attentional bias towards cannabis cues in cannabis users: a systematic review and meta-analysis

Authors: Aisling O'Neill1,2, Bianca Bachi1, Sagnik Bhattacharyya1*

Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK, SE5 8AF 2 Department of Psychiatry, RCSI, Dublin, Ireland

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*Corresponding author: Prof. Sagnik Bhattacharyya, M6.01.04, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, 16 De Crespigny Park, SE5 8AF, UK. Tel: +44 20 7848 0955, Fax: +44 20 7848 0976, Email: [email protected]

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Supplementary material can be found by accessing the online version of this paper at: https://dx.doi.org/and by entering doi: …

Highlights

Significant heterogeneity across methodology of attentional bias tasks

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Findings support increased attentional bias towards cannabis cues in cannabis users

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Short exposure studies suggest attentional bias may be due to automatic orientating

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Attentional bias may be a valid target for cannabis use disorder interventions

Abstract

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Introduction: Attentional bias, the automatic selective attentional orientation towards drug-

related stimuli is well demonstrated in substance users. However, attentional bias studies of cannabis users specifically have thus far been inconclusive. Thus, the aim of this systematic review and metaanalysis was to synthesize the currently available literature regarding cannabis related attentional bias in cannabis users.

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Methods: Literature search and selection was carried out, following the PRISMA guidelines, with all included studies investigating the relationship between cannabis use and attentional bias towards cannabis cues. Results: Fourteen manuscripts, reporting on 1271 participants (cannabis users n=1044; controls n=217), were considered for the systematic-review and majority were included in a metaanalysis. Studies reviewed used three types of attentional bias tasks: pictorial stimuli, word stimuli, and non-cannabis stimuli tasks. Greater attentional bias towards cannabis pictures (d = 0.42, P <

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0.0001) and words (d = 0.63, P = 0.03) as well as both types of stimuli overall (d = 0.53, P < 0.0001) was observed in cannabis users compared to controls, though there was evidence of significant heterogeneity for both word stimuli and overall meta-analysis. Bigger effect sizes were associated

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with shorter durations of exposure to cannabis stimuli suggesting mainly automatic orientating rather

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than controlled attention processing.

Conclusions: These findings suggest that cannabis users display greater attentional bias

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towards cannabis cues, likely an automatic process, than control groups. Future studies employing shorter exposure durations may validate attentional bias as a treatment target for the development of

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interventions in people with cannabis use disorder.

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Keywords: Cannabis; cannabis use disorder; cannabis cues; attentional bias; attentional processing

1. Introduction

Cannabis is the most widely used illicit drug around the world(United Nations Office on Drugs and Crime, 2016), with an estimated 13.3% of young European adults (15-34) reporting use within the previous year, and 1% of European adults reporting daily or near daily cannabis 2

use(European Monitoring Centre for Drugs and Drug Addiction, 2016). Despite the growing public acceptance of cannabis use in western society, cannabis use and cannabis use disorder have been associated with the onset and relapse of psychotic disorders(Alcorn et al., 2019; Field, 2005; Field et al., 2004; Vujanovic et al., 2016); depressive, and anxiety symptoms and disorders in users ranging in age from adolescence across adulthood (Leadbeater et al., 2019; Schoeler et al., 2018); as well as violent offending(Schoeler et al., 2016b), neurocognitive and social impairments, and medical comorbidities(Blest-Hopley et al., 2018, 2019; Hoch et al., 2015; Schoeler et al., 2016a).

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Experienced drug users have been found to display an automatic selective attentional orientation, which occurs outside of the user's awareness, towards stimuli related to their drug of use. This is known as an attentional bias(Field and Cox, 2008). The incentive-sensitization theory of

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addiction has been proposed as an explanation for this attentional bias(Robinson and Berridge, 2008). It suggests that repeated exposure to potentially addictive drugs results in a hyper-sensitization

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towards these drugs, and abnormal levels of incentive salience being assigned to the drugs, and drug

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related stimuli(Robinson and Berridge, 2008). This in turn results in an automatic bias of attentional processing towards stimuli related to the drug. With regard to cannabis specifically, this theory is

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reinforced by evidence that cannabis has the potential to produce rewarding and reinforcing effects by enhancing dopamine signalling in the mesolimbic and mesocortical reward pathways in the

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brain(Sabioni and Le Foll, 2018).

From this perspective, given the acquired association between cannabis related stimuli,

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subjective enjoyment, and reward-responses that lead to drug craving, seeking and taking, attentional bias appears to support the development and maintenance of addiction(Campbell et al., 2018). For this reason, attentional bias could be a potential target for treatment in cannabis use disorder, involving the reorientation of automatic attentional processes, as an alternative to the current psychosocial interventions emphasising cognitive control over substance use behaviours. Evidence supporting such interventions in the treatment setting have been mixed, as reviewed by Christiansen

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et al. 2015 and Field et al. 2014. However, administering the attentional bias modification training in the participants' home environment may provide a more robust reduction in craving and substance use . Furthermore, attentional bias has also been proposed as a predictor of relapse to substance use, particularly imminent drug use, rather than after a period of abstinence(Christiansen et al., 2015), adding to the clinical relevance of attentional bias investigations. Several attentional bias tasks have been developed, using either word or pictorial stimuli, and have been used to demonstrate attentional bias towards drug stimuli in studies of cocaine, tobacco,

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and MDMA, amongst others(Leeman et al., 2014; Robinson et al., 2016; Wilcockson et al., 2019). However, attentional bias studies of cannabis users have thus far been inconclusive. Therefore, given the importance of incentive salience in addictions, the role of automatic cognitive processes

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associated with drug seeking, taking, and relapse in cannabis use disorder and the potential treatment implications for targeting attentional bias processes, the aim of this systematic review and meta-

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analysis was to synthesize the evidence from available literature regarding the role of attentional bias

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in cannabis use.

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2. Method

2.1. Literature search and selection procedures

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Following the PRISMA(Moher et al., 2015) guidelines, two investigators (B.B. and A.O'N)

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performed the final systematic search on 5 October 2018 using OVID in the PubMed and MEDLINE databases, with no restrictions on time of publication previous to this date. Search terms were grouped in three categories: (1) TYPE OF COGNITIVE BIAS: attention; bias; (2) ILLICIT SUBSTANCES: cannabi*; mari*; (3) TYPE OF TASK: probe task; Stroop task. The Boolean Operator ‘OR’ was adopted to separate within category terms, while ‘AND’ was used to combine the three categories. Authors were contacted for clarifications and unpublished data. The PRISMA flowchart presented in Fig. 1 shows the selection procedure followed to identify relevant studies, with numbers and reasons 4

for exclusion. Data extraction followed a systematic process consisting of compiling a database with the variables of interest retrieved from the included studies. Study selection and data extraction were performed by S.B., B.B., and A.O'N., and any disagreement was resolved by consensus.

2.2. Selection criteria and outcome measure Only published, peer-reviewed papers in English, reporting original studies were considered.

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The outcome of interest was attentional bias towards cannabis cues in cannabis users. As such, to be included in the review, the studies had to investigate the relationship between cannabis use and attentional bias towards cannabis cues. Additionally, as reaction times are the most consistently used measure of attentional bias, only these studies were included. If cannabis was not the only substance

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considered, studies were included only when they specified that cannabis was the most frequently

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used illicit substance, or when analysis was done for each substance separately, or when other

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substance use was controlled for.

2.3. Data extraction process and effect size calculation From each study, data on the following variables was extracted: participant socio-

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demographic information; frequency of drug use; cannabis use assessment tool; type of attentional task used; exposure time to the stimuli; mean attentional bias (AB) for each group, and standard

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deviation (SD) for each group. Where SD was not reported, standard error of the mean (SEM) was

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extracted and converted to SD before pooling the data. For the manuscripts where mean and/ or SD/ SEM data were not available, we extracted this information from the figures reported in those manuscripts(Alcorn et al., 2019; Field, 2005; Field et al., 2004; Vujanovic et al., 2016). For this purpose, we used the freely available tool WebPlotDigitizer (v4.2, 2019) (https://automeris.io/WebPlotDigitizer/) that has been used for data extraction purposes for a range of studies including meta-analyses(Hoogeboom et al., 2012; Mukherjee et al., 2013; Wright et al., 2014). For each of the studies where this process was carried out, we uploaded the jpeg version of 5

the relevant figure on to the web-based WebPlotDigitizer application and used the option available for 2D X-Y plots, which allows data to be extracted from the relevant axes (X or Y) indexed in the same unit as reported in the figure. Therefore, reaction-time data and SEM/SD measures were extracted in seconds or milliseconds as reported in the figures.

2.4. Meta-analyses

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All meta-analyses were carried out with R 3.5.0 (https://www.r-project.org/), using the random effects models of the metafor meta-analysis package(Viechtbauer, 2010). Initially, sub-group analyses were performed for the pictorial stimuli tasks, and the word stimuli tasks, and then a combined analysis involving all the pictorial and word stimuli studies was performed. Statistical

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analyses of the two non-cannabis stimuli studies were not performed, as the generalizability of a

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meta-analysis involving such low numbers would be limited. Furthermore, given the difference in stimuli content, it was also inappropriate to include the non-cannabis stimuli studies in the combined

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meta-analysis with the studies involving cannabis related stimuli. Study level effect-sizes were pooled, and an average value was computed for each subgroup analysis, and for the overall analysis,

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using random-effects models. Random-effects models were used as they assume the variability in effect sizes reflects both random error, and variance introduced due to the study samples being drawn

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from different populations (non-random error)(Borenstein, 2009). An alpha level of 0.05 was used for all statistical tests. Effect sizes were interpreted following established criteria, i.e. effect sizes of

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approximately 0.8 are considered large, 0.5 moderate, and 0.2 small (Cohen, 1988). The I2 heterogeneity statistic was used to test the homogeneity of the effect sizes of each study included in the meta-analyses, where 0% suggests no heterogeneity, 25% suggests low, 50% suggests moderate, and 75% suggests high heterogeneity (Higgins et al., 2003). Forest plots were created to illustrate this same heterogeneity (Fig. 2). Publication bias was assessed by visual inspection of the funnel plot (Fig. 3), regression test for funnel plot asymmetry, and by trim and fill analyses. Three of the studies

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involving pictorial stimuli tasks reported data for multiple stimulus or probe durations. Thus the data for each stimulus/probe exposure duration was considered and analysed as two separate studies in the meta-analyses, i.e. for Van Hemel-Ruiter et al. (2016), Vujanovic et al. (2016), and Field et al. (2006). Similarly, the Field et al. (2004) study included attentional bias data for both high cannabis users and a low cannabis users. As such, the high and low users were analysed as two separate studies in the meta-analyses. However, notably, data for the numbers of high and low users was not reported in the Field et al. (2004) study, thus the number of subjects and mean ages included in the meta-analysis

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reflect those of the total CU group number for both high and low users. Some studies could not be included in the meta-analysis of the word stimuli tasks, as relevant data was not available (e.g. data was reported for only one group of subjects with no comparator group). These studies included:

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Wanmaker et al. (2018); Metrik et al. (2016); and Cousijn et al. (2015).

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3. Results 3.1. Results of search

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A final list of 14 manuscripts, reporting on 1271 participants (cannabis users n=1044; controls n=217), were considered for the systematic-review. Studies reviewed used broadly three type of

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attentional bias tasks (for further details of the tasks see Supplementary Table 1)1. Five studies used pictorial stimuli task (cannabis users =119, controls=132), seven used word stimuli task (cannabis

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users =245, controls =70) and two used non-cannabis stimuli task (Wanmaker et al.: cannabis users

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=8, controls =15; van Hemel-Ruiter et al. did not report individual group sample sizes, total sample n=682).

3.2. Study characteristics The characteristics of the 14 included studies are displayed in Tables 1-3, which have been

listed separately based on type of attentional bias task used (please see section below for more details).

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Supplementary material can be found by accessing the online version of this paper at: https://dx.doi.org/and by entering doi: …

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Females represented 17.7% of cannabis users, and 36.1% of controls ; mean age was 23.3 years for the cannabis users, and 26 years for the controls (age and gender of the cohorts was unavailable for the Cane et al. study). Control groups varied from never having used cannabis (Asmaro et al., 2014; Cane et al., 2009; Field et al., 2006; Vujanovic et al., 2016; Wilcockson and Sanal, 2016), to nondependant cannabis users(Field, 2005), to other drug users(Alcorn et al., 2019; van Hemel-Ruiter et al., 2016). Three manuscripts assessed attentional bias (AB) in young or adolescent samples(Cousijn et al., 2015; van Hemel-Ruiter et al., 2013; van Hemel-Ruiter et al., 2016). Concerning the frequency

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of drug use, four studies included weekly cannabis users(Alcorn et al., 2019; Asmaro et al., 2014; Field, 2005; Wilcockson and Sanal, 2016), four studies included daily cannabis users(Cousijn et al., 2015; Field et al., 2006; Metrik et al., 2016), participants in one study varied from past users to daily

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users(Cane et al., 2009), and five papers did not report frequency of drug use(Cousijn et al., 2015; van Hemel-Ruiter et al., 2013; van Hemel-Ruiter et al., 2016; Vujanovic et al., 2016; Wanmaker et

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al., 2018). Four manuscripts investigated AB in both cannabis users, and in other substances users

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(e.g. alcohol, tobacco, cocaine, amphetamine, GHB)(Alcorn et al., 2019; van Hemel-Ruiter et al., 2013; van Hemel-Ruiter et al., 2016; Wanmaker et al., 2018). However, in each study, a different version of the AB task was utilised, depending on the drug use of the participant. Participants who

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used multiple drugs were administered multiple tasks (except in the van Hemel-Ruiter et al. 2013

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study, for which no cannabis-stimuli were used).

3.3. Pictorial stimuli tasks

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Of the five manuscripts that used pictorial stimuli task, three employed a visual probe task in which, after the presentation of two side-by-side images (cannabis-related images, matched with neutral images), a probe appeared on the left or right side of the screen replacing either the cannabisrelated or neutral image (Alcorn et al., 2019; Field et al., 2006; van Hemel-Ruiter et al., 2016). Participants were then asked to press a button as fast as possible, indicating which side of the screen the probe appeared on (left or right). AB towards the drug cues was inferred when the reaction times

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(RTs) to the probes were shorter if the probe appeared behind the cannabis-related images, rather than behind the neutral images. In another study involving a pictorial stimuli task, after the appearance of either a cannabisrelated or neutral image on the screen, a face probe (neutral or smiling) was shown, and the participant had to record a response based on the type of probe(Vujanovic et al., 2016). In this case, an AB towards drug cues was inferred when the RTs to the face probe following the cannabis-related images

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was longer than RTs to the face probe following the neutral images. The final study involving a pictorial stimuli task employed a modified version of the eStroop Task(Asmaro et al., 2014). In this study, participants were presented with a small coloured square (blue, red, green, or yellow), superimposed over either a cannabis-related, negative valence, or neutral

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image, in the centre of the screen. Participants had to identify the colour of the square, ignoring the

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surrounding image. An AB towards drug cues was inferred if RTs to the square probe superimposed

3.4. Word Stimuli Tasks

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over cannabis-related images were longer than RTs to the square probe over neutral images.

Of the seven manuscripts which used word stimuli tasks, six employed the cannabis modified

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eStroop task(Cane et al., 2009; Cousijn et al., 2015; Cousijn et al., 2013; Field, 2005; Metrik et al., 2016; Wanmaker et al., 2018), presented as the paper version in the two studies by Cousijn et al. In

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five of these studies, cannabis-related words were matched for length and frequency with a neutral

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word that described a feature of the natural environment (e.g. marijuana vs shrubbery). In the sixth study, cannabis-related words were matched for length and frequency with general neutral words (e.g. mister, equitable, wool, forge). Participants were presented with this list of words printed in different colours, and they were instructed to name the colour in which the words were printed, while ignoring the semantic content of the words. An AB towards the drug cues was inferred if the participants were slower to name the colour of the drug-related words, compared to neutral words. The remaining word stimuli task employed a visual probe task, in which cannabis-related and neutral words were presented 9

at the top or bottom of the screen, followed by a dot probe, replacing either the cannabis-related or neutral words. Participants were required to indicate the location of the probe by pressing a response button(Field et al., 2004). As before, AB was inferred when RTs were faster when the probe appeared after the cannabis-related word.

3.5. Non-cannabis stimuli tasks Of the two studies which used non-cannabis stimuli task, one employed a visual probe task involving neutral and anxiety-related stimuli, followed by a dot probe, with the purpose of measuring

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whether cannabis users differ from controls in the way they process anxiety-related stimuli(Wilcockson and Sanal, 2016). The other study used a modified spatial orienting task to assess attentional engagement or disengagement towards reward and non-punishment cues in cannabis

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users(van Hemel-Ruiter et al., 2013).

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3.6. Duration of exposure to pictorial stimuli

A particular feature of the pictorial stimuli AB tasks was the duration of exposure to the

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stimuli. Exposure time varied from short (~200ms), intended to assess automatic orientating, to longer time-periods (500ms up to 2000ms), intended to assess controlled attention processing(Hindocha et

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al., 2018). The exposure times varied across the pictorial studies included here, and are reported in Tables 1-3.

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The duration of exposure in the word stimuli tasks was not a parameter of interest. As the

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probe disappeared when the participant responded, the duration of exposure was tied to the participant's reaction time.

3.7. Findings of the pictorial stimuli tasks Of the studies that used a pictorial stimuli task, three showed a significant effect of exposure

to cannabis stimuli on reaction times, highlighting an AB towards cannabis cues in cannabis users(Alcorn et al., 2019; van Hemel-Ruiter et al., 2016; Vujanovic et al., 2016). Length of exposure

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to the drug stimuli differed across each of these studies. Length of stimulus exposure also differed within the Van Hemel-Ruiter et al. study, with data reported for durations of 500ms and 1250ms. The cannabis users had greater AB towards cannabis stimuli than the controls at both durations, however the effect was found to be stronger at 500ms (Cohen's d=0.54) (Table 1)(van Hemel-Ruiter et al., 2016). In the Vujanovic et al. study, the authors also considered the effects of length of exposure to the probe, assessed at 125ms and 250ms. The study found that AB was significantly greater in the cannabis group compared to controls when the probes were displayed for 125ms, but not for the

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250ms exposure(Vujanovic et al., 2016). Effect size varied amongst these studies, with the biggest effect size observed in the Vujanovic et al. study, in relation to the 125ms length of exposure to the probe (d=1.03)(Vujanovic et al., 2016).

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A large effect size was also observed in the van Hemel Ruiter et al. study (d=0.64), when the cannabis stimuli were displayed for 500ms(van Hemel-Ruiter et al., 2016); while medium and small to medium

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effect sizes were observed by Alcorn et al. (d=0.59) and van Hemel Ruiter et al. (d=0.39), when the

Hemel-Ruiter et al., 2016).

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cannabis stimuli were displayed for 1000ms and 1250ms, respectively(Alcorn et al., 2019; van

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The two remaining pictorial stimuli tasks did not identify any significant differences in AB towards cannabis stimuli between the groups, with Field et al. assessing this at both 200ms and

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2000ms(Asmaro et al., 2014; Field et al., 2006).

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3.7.1 Pictorial stimuli tasks: Meta-analysis Eight studies were included in the meta-analysis assessing the effect of cannabis use on AB

in pictorial stimuli tasks, including study samples considered twice, based on trials with multiple stimuli durations (See Fig. 2 for a list of included studies). Results demonstrated a moderate significant effect of cannabis use on AB towards cannabis cues in cannabis users, compared to controls (Cohen's d = 0.42, P < 0.0001) (Table 4). Result of the Q test was non-significant (P = 0.17),

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and the heterogeneity statistic suggested that 0.01% of the total variability in effect sizes were attributable to differences between the studies (Fig. 2, Table 4) (Hedges and Olkin, 1985). Summary of pictorial stimuli tasks: Systematic review and meta-analysis suggest a significantly greater AB towards cannabis cues in cannabis users, compared to controls. This effect appeared to be stronger with shorter stimulus/probe durations. The meta-analysis demonstrated little heterogeneity amongst the studies.

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3.8. Findings of the word stimuli tasks Of the manuscripts which used cannabis-related word stimuli, one did not report the results relating to RTs and AB(Wanmaker et al., 2018). Of the additional six papers for which results were available, three identified a significant AB for cannabis words in the cannabis users, compared to

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controls(Cousijn et al., 2015; Cousijn et al., 2013; Metrik et al., 2016). In all these studies, a cannabis

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Stroop task was used. The three final studies involving word stimuli tasks did not identify any significant main effect of group on reaction times to cannabis cues(Cane et al., 2009; Field, 2005;

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Field et al., 2004). However, these studies highlighted a significant word type x group interaction (i.e. significant AB towards cannabis related words in dependent users, compared to non-dependent

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users)(Field, 2005); a significant probe condition x craving interaction (i.e. significant cannabis related AB for high-craving users, compared to low-craving users)(Field et al., 2004); and a

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significant interference effect within the cannabis users (i.e. significant AB towards cannabis words, compared to neutral words in the cannabis users)(Cane et al., 2009). Additionally, the study by Cane

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et al. found that the interference effect due to the cannabis cues persisted for at least three trials after the cannabis-related word was presented(Cane et al., 2009). 3.8.1 Word stimuli tasks: Meta-analysis Five studies were included in the meta-analysis assessing the effect of cannabis use on AB in word stimuli tasks, including study samples considered twice based on trials with multiple levels of cannabis use amongst the cannabis users (See Fig. 2 for a list of included studies). Results 12

demonstrated a moderate to large significant effect of cannabis use on AB towards cannabis cues in cannabis users, compared to controls (Cohen's d = 0.63, P = 0.03) (See Table 4). Result of the Q test was significant (P = 0.011), and the heterogeneity statistic suggested that 71.43% of the total variability in effect sizes were attributable to differences between the studies (Fig. 2, Table 4) (Hedges and Olkin, 1985). Summary of word stimuli tasks: Systematic review and meta-analysis suggest a significantly greater AB towards cannabis cues in cannabis users, compared to controls. However, the meta-

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analysis demonstrated substantial heterogeneity amongst the studies. The systematic review found significant differences in AB towards cannabis cues in high craving users compared to low craving

3.9. Findings of the non-cannabis stimuli tasks

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users, and between dependent users and non-dependent users.

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Finally, regarding the two studies in which non-cannabis stimuli tasks were used, Wilcockson et al. did not find any significant difference in RTs towards fearful stimuli between cannabis users

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and controls(Wilcockson and Sanal, 2016), while van Hemel-Ruiter et al. found a significant attentional bias toward non-punishment and reward in cannabis users(van Hemel-Ruiter et al., 2013).

Combined meta-analysis: pictorial and word stimuli tasks

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3.10.

Thirteen studies were included in the combined meta-analysis assessing the effect of cannabis use on

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AB in both pictorial and word stimuli tasks, including study samples considered twice (as described

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in the separate meta-analyses). Results demonstrated a significant effect of cannabis use on AB towards cannabis cues in cannabis users, compared to controls (Cohen's d = 0.53, P < 0.0001) (See Table 4). Result of the Q test was significant (P = 0.018), and the heterogeneity statistic suggested that 52.11% of the total variability in effect sizes were attributable to differences between the studies included in the meat-analysis (Hedges and Olkin, 1985). The regression test for funnel plot asymmetry found no significant asymmetry of the studies (t = 1.55, P = 0.15, Fig. 3), while the trim and fill analysis was also consistent, suggesting no missing studies, and thus no publication bias. 13

4. Discussion 4.1. Summary of findings To date, the trials that have investigated attentional bias towards cannabis cues in cannabis users have been heterogeneous in terms of the type of tasks, duration of exposure time to cannabis stimuli, and the controls included. Despite this, there is evidence for a general trend of greater attentional bias towards cannabis stimuli in cannabis users, compared to controls, across tasks. As already assessed by previous studies(Vujanovic et al., 2016), and supported by the results of the

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current pictorial stimuli tasks meta-analysis, the visual probe task appears to elicit a more accurate measure of attentional bias than word stimuli tasks, which use less vivid stimuli than pictorial stimuli. With this in mind, we have discussed the results of the included studies on the basis of the type of

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task used, below.

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Concerning the results of pictorial stimuli tasks, a general trend of greater attentional bias towards cannabis pictures in cannabis users, compared to controls, was observed. Two of the five

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studies included in this group found no significant main effect. However, one of these used a modified version of the eStroop task, originally developed as a word stimuli task, which may have affected the

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outcome. The pictorial meta-analysis supported this cannabis induced trend observed in our qualitative review, demonstrating a strong significant difference between the groups, with little to no

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evidence of true heterogeneity among the studies.

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Considering the duration of exposure to the drug cue in the pictorial stimuli tasks, though the number of studies is small, a trend can still be observed, with bigger effect sizes associated with shorter exposures to the drug stimuli (500ms)(van Hemel-Ruiter et al., 2016). Bigger effect sizes were also associated with shorter presentation time of the probe (125ms)(Vujanovic et al., 2016), during which task demands put time pressure on accurate discrimination of probes. These findings are somewhat consistent with the idea that an attentional bias towards cannabis cues in cannabis users is mainly due to automatic orientating rather than controlled attention processing, though attentional 14

bias was still present with longer durations of exposure to the cannabis cues. Short exposure durations (~200ms) are generally thought to measure relatively fast, automatic biases in the shifting of attention, while longer exposure durations are thought to measure biases in controlled disengagement of attention (500-2000ms)(Field and Cox, 2008). The reasoning behind this is that with shorter durations, participants can make only one (automatic) shift or orienting of attention towards one of the stimuli, while multiple shifts of attention between two stimuli may be possible with durations of 500ms(Field and Cox, 2008). This suggests that attentional biases observed at 500ms reflect delayed

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disengagement of attention, rather than automatic orientating (Field and Cox, 2008). However, researchers have generally used durations of 1000ms or longer to infer biases in the maintenance or disengagement of attention(Koster et al., 2005; Mogg et al., 1997; van Hemel-Ruiter et al., 2016).

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Given both of these points, the current findings still demonstrate a stronger attentional bias towards

increase in controlled attentional processing.

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cannabis cues at shorter stimulus durations that dissipates with longer exposures, likely linked to an

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Attentional bias towards cannabis-related words in cannabis users, compared to controls, was also observed in the word stimuli tasks. This was supported by the findings of the word stimuli task

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meta-analysis, which demonstrated a significant statistical effect of cannabis on attentional bias in cannabis users. However, this effect was also associated with substantial heterogeneity between the

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studies. Notably, three of these studies did not observe a significant main effect of group(Cane et al., 2009; Field, 2005; Field et al., 2004). However, in both the later Field et al. study, and the Cane et al.

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study, cannabis users were found to be significantly slower to name the colour of the cannabis-related words, compared to the neutral words(Cane et al., 2009; Field, 2005). The latter study also demonstrated a significant effect of the cannabis cues on the performance of the cannabis users in later trials(Cane et al., 2009). Moreover, in the earlier Field et al. study, the authors demonstrated cannabis-related attentional bias in terms of significantly faster reaction times to probes replacing cannabis-related words, compared to neutral words, in high-craving users, but no difference in lowcraving users(Field et al., 2004). 15

The findings of the overall meta-analysis, though also accompanied by substantial heterogeneity, still demonstrate an overall significant effect of cannabis use on attentional bias towards cannabis cues in cannabis users. The moderate to large effect sizes and varying heterogeneity across the meta-analyses also suggest that a larger number of studies may yield more conclusive results. However, as the results stand, the findings of the word stimuli and overall meta-analyses should be interpreted with caution. As shown by the two studies by Field and colleagues, attentional bias tasks may be used to

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detect other interesting aspects of cannabis use or addiction, apart from biased attention to cannabis stimuli. Indeed, van Hemel-Ruiter et al. demonstrated a significant attentional engagement towards non-punishment and reward stimuli in adolescent cannabis users(van Hemel-Ruiter et al., 2013), in

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accordance with the hypothesis that an enhanced attentional bias for appetitive cues may set adolescents at risk for developing excessive substance use. This evidence could have implications for

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future studies, in particular those relating to abnormal salience attribution in cannabis users, through

(e.g. food), in cannabis users.

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the investigation of differential attentional bias between cannabis stimuli and other pleasant stimuli

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The tasks and measures utilised in attentional bias studies also warrant further discussion. Specifically, both the Stroop and visual probe tasks have demonstrated poor internal validity, despite

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being the most commonly used tasks in studies of attentional bias (Christiansen et al., 2015; Field et al., 2014). A major criticism of the Stroop test in particular is its potential inability to distinguish

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between attentional bias and attentional avoidance(Field and Cox, 2008). Attentional avoidance may occur when participants are trying to avoid processing substance related stimuli. In turn, this may result in slower word naming of substance related stimuli during the Stroop test, obscuring the interpretation of the findings (Field and Cox, 2008). The visual probe task overcomes this issue, as it allows for differentiation between vigilance towards and avoidance of substance related stimuli (i.e. faster responses to probes that appear in the same location of the substance related stimuli indicate

16

that attention was already directed toward the substance related stimuli) (Jiang and Vartanian, 2018). This may be reflected in the consistency of the pictorial stimuli findings compared to those of the word stimuli tasks included in the current analysis. However, the visual probe task is also limited in that probe detection reaction times only provide information regarding allocation of attention, neglecting the shifting of attention between stimuli (Miller and Fillmore, 2010). These issues may not apply to direct measurements of attentional bias, such as the measurement of participant's eye movements where a longer gaze at a stimulus indicates attentional bias (Field et al., 2014).

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Nonetheless, currently, due to practicality and cost, eye tracking is less commonly used. A crucial criticism of most attentional bias studies is the lack of within subject data relating to the craving and motivational valence of the substance at the time of testing (Christiansen et al.,

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2015). Previous studies have found that attentional bias fluctuates in line with the subjective craving and motivational value the participant has assigned to the substance at that moment in time (Field et

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al., 2009; Rose et al., 2013). In addition to this within subject variability, craving induced by viewing

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a substance related word may also result in a slowing of cognitive processing during the Stroop test, further obscuring any selective attention to the words(Field and Cox, 2008). Thus, future studies

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should take fluctuations in subjective craving and the motivational state of the participants into consideration when investigating attentional bias in substance users.

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4.2. Strengths and limitations

Several limitations should be considered while assessing the evidence summarized here.

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These are mainly related to the limitations of the studies included. Firstly, there was significant methodological heterogeneity between the studies, which made it difficult to compare the results. Specifically, the studies used different attentional bias tasks, different stimuli exposure times, and in many cases the samples were not homogeneous in terms of frequency of drug use. Some studies also recruited participants with different substance misuse problems; while the "control" groups were also heterogeneous, varying from never having used cannabis, to non-dependent cannabis users, to users

17

of other drugs. These factors may have contributed to the substantial heterogeneity observed in the word stimuli and overall meta-analyses. Despite this, the systematic review and meta-analysis has multiple strengths. A systematic, thorough search strategy was used to identify all relevant available studies, and authors were contacted directly where data were not available. Furthermore, no publication bias was observed, adding strength to the findings.

4.3. Implications Though limited, these findings could have important clinical implications, as selective

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attention to drug cues in cannabis users may be considered to be a marker of perseverative thinking about drug use, drug seeking, initiation of compulsive drug use behaviours, and as a relapse risk factor(Vujanovic et al., 2016). To identify a specific population with biased attention to cannabis cues

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among cannabis users may also have important treatment implications. Indeed, cannabis dependent

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treatment seekers who show an automatic attention orientation towards drug stimuli may not benefit from the typical intervention options currently available for cannabis use disorder, as these primarily

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aim to reinforce cognitive control over drug-use trigger elements. For this reason, other interventions targeting the automatic attentional bias towards cannabis cues should be explored. For example, a

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recent study by Hindocha et al. demonstrated the efficacy of cannabidiol (a non-intoxicating extract of the cannabis plant) in reversing attentional bias towards tobacco cues in cigarette

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smokers(Hindocha et al., 2018). However, it is important to note that the current findings are based on laboratory experiments, and it is still unclear how they may translate to the wider population.

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Indeed, previous reviews suggest caution when recommending attentional bias modification as a treatment plan prior to thorough evaluation through large-scale clinical trials (Christiansen et al., 2015; Field et al., 2014).

4.4. Conclusions In conclusion, there is evidence to suggest that cannabis users display greater attentional bias towards cannabis cues, than control groups. This is highlighted by pictorial stimuli tasks, and to a 18

lesser extent by word stimuli tasks. Nevertheless, methodological inconsistency across studies preclude more definitive conclusions. Evidence that the effect-size for attentional bias to cannabis cues is greater for studies employing shorter exposure duration to cannabis stimuli suggest that future studies employing shorter exposure durations are warranted. This will not only add clarity to evidence regarding automatic selective attention orientation towards cannabis stimuli in cannabis users, but may also inform future studies of the potential role for attentional bias in clinical interventions for

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people with cannabis use disorder.

Author Disclosures

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S.B. has received support from the NIHR (NIHR Clinician Scientist Award; NIHR CS-11001), and the UK MRC (MR/J012149/1), and from the NIHR Mental Health Biomedical Research

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Centre at South London and Maudsley NHS Foundation Trust and King’s College London. All

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Contributors

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authors report no other biomedical financial interests or potential conflicts of interest.

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A.O'N. and B.B. contributed equally to the manuscript. B.B. contributed to the conception and design of the study, undertook the original systematic review, and produced an early draft of the

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manuscript; A.O'N. updated the systematic review, analysed and interpreted the data, and revised the manuscript; S.B. conceived and designed the study, supervised the conduct of the manuscript, contributed to study selection, analysis and interpretation, and drafting and revision of the manuscript and gave final approval of the version to be published.

19

Conflicts of interest No conflicts declared.

5. Acknowledgements B.B. and A.O'N. contributed equally to the manuscript. B.B. contributed to the conception and design of the study, undertook the original systematic review, and produced an early draft of the

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manuscript; A.O'N. updated the systematic review, analysed and interpreted the data, and revised the manuscript; S.B. conceived and designed the study, contributed to study selection, analysis and interpretation, and drafting and revision of the manuscript and gave final approval of the version to

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be published.

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S.B. has received support from the NIHR (NIHR Clinician Scientist Award; NIHR CS-11001), and the UK MRC (MR/J012149/1), and from the NIHR Mental Health Biomedical Research

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Centre at South London and Maudsley NHS Foundation Trust and King’s College London. All other authors report no biomedical financial interests or potential conflicts of interest.

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The authors declare no conflicts of interest.

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Robinson, T.E., Berridge, K.C., 2008. Review. The incentive sensitization theory of addiction: some current issues. Philos Trans R Soc Lond B Biol Sci 363(1507), 3137-3146. Rose, A.K., Brown, K., Field, M., Hogarth, L., 2013. The contributions of value-based decisionmaking and attentional bias to alcohol-seeking following devaluation. Addiction 108(7), 1241-1249. Sabioni, P., Le Foll, B., 2018. Psychosocial and pharmacological interventions for the treatment of cannabis use disorder. F1000Res 7, 173. Schoeler, T., Kambeitz, J., Behlke, I., Murray, R., Bhattacharyya, S., 2016a. The effects of cannabis

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van Hemel-Ruiter, M.E., Wiers, R.W., Brook, F.G., de Jong, P.J., 2016. Attentional bias and executive control in treatment-seeking substance-dependent adolescents: A cross-sectional and follow-up study. Drug Alcohol Depend 159, 133-141. Viechtbauer, W., 2010. Conducting Meta-Analyses in R with the metafor Package. 2010 36(3), 48. Vujanovic, A.A., Wardle, M.C., Liu, S., Dias, N.R., Lane, S.D., 2016. Attentional bias in adults with cannabis use disorders. J Addict Dis 35(2), 144-153. 24

Wanmaker, S., Leijdesdorff, S.M.J., Geraerts, E., van de Wetering, B.J.M., Renkema, P.J., Franken, I.H.A., 2018. The efficacy of a working memory training in substance use patients: A randomized double-blind placebo-controlled clinical trial. J Clin Exp Neuropsychol 40(5), 473-486. Wilcockson, T.D.W., Pothos, E.M., Parrott, A.C., 2019. Substance usage intention does not affect attentional bias: implications from Ecstasy/MDMA users and alcohol drinkers. Addict Behav 88, 175181. Wilcockson, T.D.W., Sanal, N.E.M., 2016. Heavy cannabis use and attentional avoidance of anxiety-

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Figures

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Fig. 1: Literature search and selection of the studies, adapted from the PRISMA flow-chart(Moher et al., 2015).

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Fig. 2: List of studies and results (including standardised mean differences, confidence intervals, heterogeneity statistics, and forest plots) of the pictorial stimuli, word stimuli, and overall meta-analyses. Multiple stimulus/probe durations from the same studies were analysed as separate studies, and are differentiated from each other by the suffix 1 or 2, as follows: Van Hemel et al. 2016.1 = 500ms (stimulus duration); Van Hemel et al. 2016.2 = 1250 (stimulus duration); Vujanovic et al. 2016.1 = 125ms (probe duration); Vujanovic et al. 2016.2 = 250ms (probe duration); Field et al. 2006.1 = 2000ms (stimulus duration); Field et al. 2006.2 = 200ms (stimulus duration). High and low users in the Field et al. (2004) study, also analysed as separate samples, are indicated by the suffixes H (high) and L (low). Note that data regarding numbers of high and low users in the Field et al. (2004) study were not available, thus the total sample number and mean ages of the combined high and low users are reported in the columns referring to the cannabis users in the high and low samples.

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Fig. 3: Funnel plot showing little asymmetry amongst the individual studies. Regression test found no significant asymmetry of the individual studies in the funnel plot (t = 1.55, P = 0.15). Trim and fill analysis suggested no missing studies.

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Table 1: Demographics and characteristics of the 5 pictorial stimuli tasks O D C A ther drug uration of stimuli x. Of CU use of CU C S T elf-report; obacco: 000ms expired mean breath cigarettes/da samples; 16 y (SD)=4.15 CU N met (7.06) DSM-IV A criteria for lcohol: mean dependence alcoholic drinks/week (SD)=4.28 (4.14) N A N ddiction /R 00ms; centre 1250ms diagnosis of CUD

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Des cription

1

1 9.67 (5.45)

2 2.62 (7.74)

1 2.33 (2.96)

2 0.27 (1.91)

3

2 3.08

2

0

5

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3

A Can smaro et al. nabis AB in 3 (2014) CUD, compared to NCU. Modified eStro op task. AB was inferred

1 5

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1

C U, but not cocaine users, exhibited cannabis AB. t( 16)=2.44, p=0.03, d=0.59

5

C U greater cannabis AB than NCU at exposure times of 500ms and 1250ms, though NCU also had significant cannabis AB at 500ms. M ean AB of CU:

AB 500ms: 26.13, d=0.64 AB 1250ms: 11.36, d=0.39 M ean AB of NCU: AB 500ms: 10.23 AB 1250ms: 1.29 2 N A N C C /R ll CU met icotine: n= ue U had DSM-IV 8/12; stimulus: 15 greater criteria for Alcohol: 00ms cannabis CUD; Urin n=12/12; AB than e sample; Cocaine: P NCU at expired air n=3/12; robe: 125ms; 125ms sample Opiates: 250ms (t(92)=3.00, n=4/12; p=0.001, d= Amphetamin 1.03), but e: n=2/12; not at Sedatives: 250ms. n=2/12; Hallucinoge ns: n=2/12. 0 C S O 5 N U: daily elf-report; ther drug use 00ms o significant users cannabis (including differences severity N of tobacco and for cannabis CU: non- dependence nicotine) AB between users scale were the groups. exclusion R criteria Ts to drug

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Can nabis AB in 2 CU, compared to NCU. Face probe task. AB was inferred from longer RTs in cannabis related conditi ons.

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V ujanovic et al. (2016)

R esults

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Sampl Sample Gender e Size mean age (SD) (% Female) U C N C N C N freq. udy U CU U CU U CU A Can 1 1 2 4 3 3 lcorn et al. nabis AB in 7 7 4.45 3.5 5.29 5.29 U: (2019) CU, compared (cocaine (5.43) (8.65) daily/nea to cocaine dependen r daily users. Visual t) use probe task. AB inferred from CU: <5 shorter RTs to days in probe prev. replacing month cannabis images. va Can 5 6 1 1 3 3 n Hemelnabis AB in 4 1 9.69 9 (2.37) 2 1 /R Ruiter et al. adolescents (2.83) (2016) with alcohol, cannabis, amphetamine or GHB dependency. Visual probe task. AB inferred from shorter RTs to probe replacing cannabis images. St

from longer RTs in cannabis related conditions.

Fi eld et al. (2006)

Can nabis AB in 3 CU, compared to NCU. Visual probe task. AB inferred from shorter RTs to probe replacing cannabis images.

2 6

2 3.04 (4.23)

2 1.3 (5.04)

2 9.13

3 7.69

5 C S U: at elf-report /A least once/wee k N CU: not current users, and never past regular users.

N 00ms; 2000ms

stimuli were faster than to neutral stimuli, but this was unaffected by group. 2 N o significant differences between the groups for cannabis AB.

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CU=cannabis users, NCU=non-cannabis users, CUD=cannabis use disorder, freq.= frequency, Ax=assessment, AB=attentional bias, RTs=reaction times, prev.=previous, SD=standard deviation, d = Cohen's d measure of effect size, N/R=not reported, N/A=not applicable.

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Table 2: Demographics and characteristics of the 7 word stimuli tasks Sampl Sample gender O D A e Size mean age (SD) (% Female) ther drug uration of C R x. Of use of CU stimuli esults C N C N C NU freq. udy CU U CU U CU U CU W Wo 1 N 3 2 N N A A A N C anmaker et al. rking memory 9 /A 6.6 7.7 /A /A verage days ll CU lcohol: /A annabis AB (2018) training that patients met 50.6% data in CU intervention used DSM-IV C placebo study, cannabis in criteria ocaine: group is not compared to previous 30 for 27.1% provided. placebo, days: 22.4 CUD intended to reduce attentional bias measured via the modified eStro op task. AB inferred from slower RTs to colour-name drug related words. M AB 9 N 2 N 3 N A S N N C etrik et al. towards 3 /A 1.4 (4.4) /A 4.4 /A t least two elficotine (less /A annabis AB (2016) cannabis words days per report than 21 was in CU, week for the tobacco cigar observed, compared to previous ettes per compared to neutral words, month, and day) neutral following at least stimuli: t exposure to lit weekly for (92) = 4.15, cannabis cigare the previous p < 0.001. tte. 6 months. Cannabis Modified eStro exposure op task. AB related inferred from increases in slower RTs to intensity of colour-name demand drug related were words. associated with greater AB to cannabis vs neutral words. C Inv 5 N 1 N 2 N A A Il N C ousijn et al. estigation of 7 /A 9.6 (2) /A 4.6 /A verage days ll CU licit drug use /A U displayed (2015) the that patients met in previous 6 AB for relationships used DSM-IV months: cannabis between AB, cannabis in criteria 18.8% words (t56 approach bias, previous 30 for ( = 2.94, p = craving, days: 19.3 CUD Other 0.005, d = cognitive (10.7) substance 0.38). control, and use disorder Cannabis cannabis use in was criteria related adolescents for motivational with CUD. exclusion) measures, Modified AB, and Stroop task. craving AB inferred were not from slower significantly RTs to colourcorrelated. name drug related words. C Can 2 2 2 2 3 3 C S A N C ousijn et al. nabis AB in 7 6 4 (2.8) 5.3 (2.6) 0 8 U: every elflcohol mean /A U had (2013) CU, compared week, for report (SD) of greater to NCU. previous 2 AUDIT cannabis Modified Stroo years. At score = 8.4 AB than p task. AB least 200 (4.8) NCU: t(51) inferred from lifetime N = 2.54, p = slower RTs to occurrences. icotine 0.01, colour-name No history dependence d=0.70. drug related of treatment mean (SD) D words. seeking. of FTND ependent Des cription

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St

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2 1 3 (non- 2.07 dependen (1.75) t users)

S /D

N /D

N /D

N /D

N C U: Daily - elf23.5%; report Weekly 29.4%; once a month 17.6%; every 6 months 17.6%; once a year 5.9%; no longer smoke 5.9%

2 1.77 (2.13)

2 0

2 3.08

2 D S ependent elf/A CU: 37 report, cannabis cannabis joints/month severity of Ndepende onnce dependent scale CU: 4.5 cannabis joints/month

N /A

N

/A

na ur Jo Fi eld et al. (2004)

Can nabis AB in 7 CU, compared to NCU.

1 6

1 2 2 2.4 (5.4) 0.9 (7.3) 0.6

7 5

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CU displayed stronger cannabis AB than nondependent CU: t(25) = 2.3, p = 0.03). N S ignificant AB for cannabis words over neutral words observed in CU (F(1,16) = 19.03, p < 0.0001, and not in NCU.

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1 /D

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Ca nnabis AB in 5 dependent CU, compared to non-dependent CU. Modified eStro op task. AB inferred from slower RTs to colour-name drug related words.

1 5

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Fi eld et al. (2005)

Sho rt and longer- 7 term cannabis AB in CU, compared to NCU. Modified Stroop task.

score = 2.6 (2.2)

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Ca ne et al. (2009)

N CU: no use in previous month, and fewer than 50 lifetime occurrences.

7 C U median elfnumber of report cannabis

S N icotine: 59% 00ms

N N o significant differences between dependent and nondependent CU on RTs to cannabis cues. D ependent CU displayed significant AB towards cannabis words, compared to the control words (t14=6.75, P <0.001), while nondependent CU did not. S troop interference was significantly positively correlated with number of joints smoked per month (r=0.55, P<0.005) and mean craving scores on the S-MCQ (r=0.54, P<0.005) 5 N o significant difference between

joints smoked per month: 16 N CU: no current users, no history of cannabis us e other than experimenta l use.

groups on RTs to cannabis vs control words. H igh craving CU displayed greater cannabis AB, compared to control stimuli t(8) = 2.64, P < 0.05; while no difference was observed in low craving CU, and NCU.

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Visual probe task. AB inferred from shorter RTs to probe replacing cannabis images.

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CU=cannabis users, NCU=non-cannabis users, CUD=cannabis use disorder, freq.= frequency, Ax=assessment, AB=attentional bias, RTs=reaction times, prev.=previous, SD=standard deviation, d = Cohen's d measure of effect size, N/R=not reported, N/A=not applicable, AUDIT=alcohol use disorders identification test, FTND=fagerstrom test for nicotine.

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Table 3: Demographics and characteristics of the 2 non-cannabis stimuli tasks Sample Sample Size mean age (SD) C N C U CU U CU 8 1 2 5 3.13 4.13 (4.16) (3.94)

N 6.14 (0.6)

1 /A

N .48

7 /A

U freq. N

C x. Of CU

O D A ther drug uration of use of CU stimuli esults

3 C U: daily elfuse report N CU: no current/hi story of cannabis use

S A

N /R

N Ni cotine: mean 50ms; (SD) cigarett 500ms es/day over previous month = 2.22 (4.71)

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Wil A cockson et al. B towards (2016) anxietyrelated stimuli in CU, compared to NCU. Visual probe task. AB inferred from shorter RTs to probe replacing anxietyrelated images. van In N Hemel-Ruiter vestigation of /R A et al. (2013) the role of T rewardotal related sample: attentional n=682 biases on (adolesce adolescent nts w. substance alcohol, use. Spatial tobacco, orienting or task. AB cannabis inferred from use shorter RTs to reward stim uli.

gender (% Female) N C U CU 2 2 3.13 3.3 (4.16)

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Al cohol: mean (SD) servings of alcohol/week over previous month = 6.00 (7.24)

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A ge, attentional engagemen t toward nonpunishment (short delay), and attentional engagemen t toward reward (long delay) all predicted unique variance of substance use (p < .001)

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CU=cannabis users, NCU=non-cannabis users, CUD=cannabis use disorder, freq.= frequency, Ax=assessment, AB=attentional bias, RTs=reaction times, prev.=previous, SD=standard deviation, N/R=not reported, N/A=not applicable.

34

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o AB difference between CU and NCU.

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