Supplementary Materials

Note. Gambling and cannabis pictures used in the stop-signal task (SST). Gambling and

cannabis images used in the SST were taken by the first Author of this manuscript. Neutral

pictures were taken in an office located at the Department of Psychiatry of the Brugmann

University Hospital (Brussels, Belgium). Cannabis and gambling images were taken from

"https://freerangestock.com" and "https://www.pexels.com", which are website that give

access to quality copyright free photographs. All pictures were taken from a “first-person”

point of view and some involved other people interacting with cannabis (e.g., the hands of

another person holding some cannabis) or gambling (e.g., the hands of a croupier at a

blackjack table). Gambling and neutral pictures were matched on visual complexity with the

neutral pictures through a collaboration between the first author and a professional

photographer.

Figure S1. Some example of (A) cannabis and paired neutral pictures and (B) gambling and

paired neutral pictures.

Note. Instructions on how to compute the nth RT component from to the integration

method.

- n equal the number of RTs in the go trials RT. In the current SST, there were a total

of 175 go trials including cannabis pictures and 175 go trials including gambling

pictures. Thus, in the current study, the maximum value of n equals 175. The value of n

could vary according to the number of misses (i.e., no-go response during go trials)

and the number of outlier RT (i.e., go trials with response times more than 1.5 times

the interquartile range away from the 25th and 75th percentiles of the response time

distribution of each stop-signal probability level). For instance, if one participant

exhibit 15 misses and 3 outliers RT for the go trials on cannabis pictures, n for

cannabis pictures equal 157.

- Next, we computed the overall p[respond|signal] (i.e., the number of go response

during stop-signal trials, divided by the total number of stop-signal trials). In the

current SST, there were a total of 41 stop-signal trials for cannabis pictures and 41

stop-signal trials for gambling pictures. For instance, if one participant exhibited 20

go responses throughout stop-signal trials, the p[respond|signal] value would equal

0.49 (i.e., 20 divided by 41).

- Next, the number of go trials RT values (e.g., 157) was multiplied by the overall

p[respond|signal] (e..g, 0.49). In our example, this value equals 77.

- Next, all go trials RT values (e.g., 157) were rank ordered (in increasing order).

- In our example nth will correspond to the 77th value of the go-trial distribution.

- Finally, in order to obtain the SSRT, the nth value was subtracted from the mean

SSD (i.e., the average of the SSD values throughout the SST)

STUDY 1

METHODS

Participants and recruitment

Individuals with Cannabis Use Disorder (CUD; n = 67) were recruited from the Cannabis

Clinic of the Brugmann Hospital’s Psychiatric Institute (Université Libre de Bruxelles,

Brussels, Belgium). The Cannabis Clinic offers ambulatory CBT and family-based therapy to

individuals with problematic use of cannabis. Reasons for exclusion were the use of another

psychotropic drug that influences cognition, and overt cognitive dysfunction. This

information was obtained from a psychological examination interview made by board-

certified psychotherapist on the day of enrolment at the Cannabis Clinic. This interview also

contained qualitative information on patients’ motivation to quit cannabis. Specifically, all

participants recruited in this study reported either a motivation to modulate, control or abstain

from cannabis. CUD was assessed on the day of the study and based on items taken from the

Alcohol, Smoking and Substance Involvement Screening Test (ASSIST; WHO ASSIST

Working Group, 2002). Specifically, all CUD participants self-reported experiencing at least

three of the following five symptoms: a strong urge to use cannabis (on a daily or weekly

basis over the past three months), that their use of cannabis led to health, social, legal, or

financial problems (on a daily or weekly basis over the past three months), a failure to do

what was normally expected from them because of their use of cannabis (on a daily or weekly

basis over the past three months), that a friend or relative or anyone else expressed concern

about their use of cannabis (in the past three months), and that they tried and failed to cut

down or stop using cannabis (in the past three months).

Participants from the control group were recruited by word of mouth from the

community. Before being enrolled in the study, controls were first asked to complete a brief

pre-screening tool estimating drug and alcohol use. Control participants were excluded if they

reported using cannabis or other drugs of abuse within the past twelve months. Participants

were also excluded if they reported frequent alcohol use (e.g., an average of one drink or more

per day over the last year).

Affective status, alcohol consumption, and working memory

Affective state was rated with the Beck Depression Inventory (BDI; Beck et al., 1961),

Spielberger Trait Anxiety Inventory (STAI-T; Spielberger, 1993), and Positive and Negative

Affective Schedule (PANAS; Watson et al., 1988). The Alcohol Use Disorders Identification

Test (AUDIT; Saunders et al., 1993) was used to control for the level of alcohol consumption.

Working memory was assessed using the Digit span task (forward and backward; Wechsler,

1949, 1991), which is a widely used neuropsychological test that quickly evaluates working

memory capacity by determining the maximum length of numbers that participants can

serially recall in the same (forward condition) or the reverse (backward condition) order.

RESULTS

Difference between mean failed stop-signal RT and mean go signal RT. Mixed-model

ANOVA were used with stimulus type (neutral vs. cannabis) and response type (failed stop-

signal RT vs. go signal RT pooled across all stop-signal probability levels) as within-subjects

factors; groups (controls vs. CUD) as between-subjects factor; and RT as dependent measure.

These analyses revealed a main effect of response type, F(1,84) = 352.11, p < .001, η² = .81,

indicating that RT was lower for failed stop-signal (M = 846, SD = 111) than for go_signal

RT (M = 940, SD = 123). There was a main effect of stimulus type, F(1,84) = 22.20, p < .001,

η² = .21, indicating that RT was lower for neutral (M = 880, SD = 112) than cannabis stimuli

(M = 907, SD = 124). There was a response type × group interaction, F(1,84) = 6.02, p = .016,

η² = .07, indicating that RT difference between failed stop-signal (controls: M = 837, SD =

123; CUD: M = 855, SD = 130) and go_signal RT (controls: M = 943, SD = 110; CUD: M =

936, SD = 112) was higher in the control group than in the CUD group. No other significant

effect was observed (all p > .16).

Proportion of missed responses according to the level of stop-signal probability. Mixed-

model ANOVA were used with level of stop-signal probability > 0% (yellow, orange, red)

and stimulus type (neutral vs. cannabis) as within-subjects factors; groups (controls vs. CUD)

as between-subjects factor; and proportion of missed responses as dependent measure (see

Table S1 for descriptive statistics). These analyses revealed a main effect of stop-signal

probability, F(2,84) = 26.47, p < .001, η² = .24, indicating that the proportion of miss

increased with the level of stop-signal probability. There was a main effect of stimulus type,

F(1,84) = 5.20, p = .025, η² = .06, indicating that the proportion of miss was higher for

cannabis than for neutral pictures. There was a main effect of group, F(1,84) = 4.29, p = .041,

η² = .05, indicating that the proportion of miss was higher in CUD than in controls. There was

no significant interaction effect (all p > .24).

Stimuli categorization accuracy. Mixed-model ANOVA were used with level of stop-signal

probability (green, yellow, orange, red) and stimulus type (neutral vs. cannabis) as within-

subjects factors; groups (controls vs. CUD) as between-subjects factor; and proportion of

correct stimuli categorization as dependent measure (see Table S1 for descriptive statistics).

These analyses revealed a main effect of level of stop-signal probability, F(3,84) = 19.16, p =

.008, η² = .19. Pairwise comparisons revealed that the proportion of correct stimuli

categorization was higher in the green context as compared to the yellow, orange and red

contexts (all p < .001). No other significant result was observed (all p > .07).

Probability of responding on stop-signal trials according the level of stop-signal probability.

Mixed-model ANOVA were used with level of stop-signal probability > 0% (yellow, orange,

red) and stimulus type (neutral vs. cannabis) as within-subjects factors; groups (controls vs.

CUD) as between-subjects factor; and probability of responding on stop-signal trials as

dependent measure (see Table S2 for descriptive statistics). These analyses revealed a main

effect of level of stop-signal probability, F(2,84) = 60.93, p < .001, η² = .42, indicating that

probability of responding on stop-signal trials decrease in function of stop-signal probability.

No other significant result was observed (all p > .08).

Cannabis cue ratings. After having performed the SST task, participants were asked to rate

each one the 18 cannabis SST pictures (7 points Likert scale, ranging from “not at all” to “a

lot”) on (i) familiarity, (ii) pleasantness, (iii) and on whether the picture make them feel the

desire to smoke cannabis.

First, reliability analyses revealed than there was a high consistency between cannabis

pictures used in the task when rated by participants on “familiarity” (control group: Cronbach’

s α = .75; CUD group; Cronbach’ s α = .96), “pleasantness” (control: Cronbach’ s α = .98;

CUD; Cronbach’ s α = .97) and “desire to smoke” (control: Cronbach’ s α = .97; CUD;

Cronbach’ s α = .99). Moreover, in the CUD group (n = 56), the three ratings dimensions

correlate (Pearson) significantly with each others (all p < .001). This allowed us to compute,

within the CUD group, a combined score of cannabis cue ratings (i.e., mean between

“familiarity”, “pleasantness”, and “desire to smoke” ratings) for further analyses. In the

control group (n = 30), there was only a significant correlation between the “pleasantness”

and “desire to smoke” dimensions, r(30) = 0.91, p < .001.

Second, mixed-model ANOVA were used with dimension (familiarity vs. pleasantness

vs. desire to smoke) as within-subjects factor; groups (control vs. CUD) as between-subjects

factor; and rating scores as dependent measure. These analyses revealed a main effect of

dimension, F(2,82) = 61.21, p < .001, η² = .43, indicating that rating score of “familiarity” (M

= 4.62, SD = 1.42) were higher than scores of “pleasantness” (M = 3.37, SD = 1.91; p < .001)

and “desire to smoke” (M = 3.28, SD = 2.04; p < .001). There was a main effect of group,

F(1,82) = 87.36, p < .001, η² = .52, indicating that rating scores were higher in the CUD group

(M = 4.45, SD = 1.63), as compared to the control group (M = 1.93, SD = 0.50). There was a

dimension × group interaction, F(2,87) = 25.07, p < .001, η² = .23, indicating that, difference

of ratings between the “familiarity” and both the “pleasantness” and the “desire to smoke”

was higher in the control group than in the CUD group (see Figure S2).

Third, as exploratory analyses, we examined the association between cannabis cue

ratings and proactive/reactive inhibition toward cannabis cues. Thee correlation analyses

(Pearson) were undertaken within the CUD group (n = 49). Only significant correlations were

selected that met a α-level of .05 after Bonferroni-Holms corrections (Holm, 1979). We

observed no significant correlation between the indices of reactive inhibition for cannabis

cues (i.e., log transform SSRT) and rating scores of “familiarity”, “pleasantness”, and “desire

to smoke” ratings (all p > 0.09). There was also no significant correlation when using the

combined score of cannabis cue ratings (p = 0.08). The association failed to reach significance

when were correlates separately (all p > .063). No significant correlation (Pearson) was

observed between indices of proactive control for cannabis cues (i.e., the “orange_go minus

yellow_go” and the “red_go minus orange_go” RT ratios) and cannabis cue rating (with either

combined or separate scores of “familiarity”, “pleasantness”, and “desire to smoke” ratings,

all p > .31).

STUDY 2

Participants and recruitment

Necessary sample size was computed a priori (using G*Power 3.1.9.2; Faul et al., 2007) for

repeated measures ANOVA (effect size f = 0.20; α err prob = 0.05; Power (1- β err prob) =

0.95; six measurements; three groups; correlation among repeated measures = 0.70 (based on

Study 1); nonsphericity correction ε = 1). This analysis indicated that at least 21 participants

were required in each group (60 subjects in total) to detect a within-between interaction effect

with a small effect size. However, based on the proportion of subjects excluded in Study 1

due to unacceptable stop-signal task performance (17 % of participants from the CUD group),

we recruited 25 participants for each group (see Table 2 for demographics). All participants

gave written informed consent to the experimental procedure, which was approved by the

CHU-Brugmann University Hospital Institutional Review Board. Participants received 50

euros for their participation.

Problem gamblers (n = 50) were recruited from the “Voluntary Self-Exclusion” (VSE)

program of the Belgian Gaming Commission. More specifically, VSE gamblers were

recruited on the Internet through advertisements displayed on “The Protection of the Player”

webpage of the Belgian Gaming Commission

(https://www.gamingcommission.be/opencms/opencms/jhksweb_fr/protection/index.htm).

The Protection of the Player webpage allows quit-motivated individuals to apply for an access

ban to enter casinos (real and online), gaming arcades (real and online) and betting office (real

and online). A screening interview was conducted by phone with each gambler participant in

order to confirm that individuals (i) were frequent gamblers who wanted to deny their access

to real or virtual gaming establishments, and (ii) had no history of therapeutic treatment

focused on gambling behavior, alcohol or others substance abuses. Alcohol use (alcohol

drinks per day), medical and psychiatric history were also assessed by means of a locally

developed screening tool (see also Brevers, et al., 2012a, 2012b; Stevens et al., 2015). We

excluded any subject from the gambler groups who (a) reported gambling in casino settings

less than once a week or less than four times a month during the past 18 months, (b) was older

than 65 years (to avoid potential confounds from slow motor functioning due to aging), or (c)

had experienced a substance abuse–related disorder during the year before enrolment in the

study. In addition, those subjects included were judged to be healthy on the basis of their

medical history. Substance use and medical history were examined using items taken from the

Addiction Severity Index Short Form (Hendriks et al., 1989).

Participants from the control group (n = 25) were recruited by word of mouth from the

community. Before being enrolled in the study, controls were first asked to complete a brief

pre-screening tool estimating substance use. Non-gambler control participants were excluded

if they reported to consume cannabis or other drugs of abuse within the past twelve months.

Participants were also excluded if they reported excessive alcohol consumption (e.g., an

average of three drinks or more per day over the last year) or gambled occasionally (i.e., more

than 1 time in the last 3 months).

Control measurements

Affective status was rated with the BDI (Beck et al., 1967), STAI-T (Spielberger,

1993), and PANAS (Watson et al., 1988). The AUDIT (Saunders et al., 1993) was used to

control for the level of alcohol drink consumption. Problem Gambling over the last 12 months

was assessed with the Problem Gambling Severity Index (PGSI; Ferris and Wynne, 2001). On

the PGSI, score of 1 or 2 = low level of problem gambling; score of 3 to 7 = moderate level of

problem gambling; and score of 8 or more = high level of problem gambling. Gambling

frequency (“How many days per week do you gamble?”), and motivation for stopping or

diminishing gambling (“Are you ready to make some effort in order to stop or moderate

gambling?” [YES-NO answer]; “On a 10-point scale, how much do you want to change your

gambling behaviors?”) were also recorded. The Attentional Control Scale (ASC; Derryberry

and Reed, 2002) was administered to assess attentional control. Working memory was

assessed using the operation span task (Ospan; Turner and Engle, 1989), in which participants

were requested to solve mathematical operations while simultaneously remembering a set of

unrelated words.

Illustration of one gambling script.

It is 7pm on this day of summer… You are joining your brother at [Name of the Bar]…

You are thinking about what he told you yesterday: there are new poker slot machines at

[Casino Name]… You have planned your evening in order to enjoy playing on these new slot

machines with you brother and some friends… You are now at the bar with [Name of the

Brother]…. You are discussing the potential gambling strategies that you are about to play at

[Casino Name]… You are already feeling the excitement. You are feeling some tension in your

feet… You are now going out of the [Name of the Bar]. On your way to the casino, you are

pressing your brother to walk faster…. You are now arriving in front of the [Casino Name].

You are thinking about the pleasure that you will have while gambling. [Name of friends] are

already gambling inside the casino…. You are now about to enter the [Casino Name]. You

are feeling the excitation increasing in all your body…. You hands are shaking. While

entering the [Casino Name], you are perceiving the sounds and lights of the machines. You

already know which machine you are about to play…. You are now sitting in front of the slot

machine and you are telling yourself: “Finally! I’m going to enjoy this so much!”.

RESULTS

Difference between mean failed stop-signal RT and mean go signal RT. Mixed-model

ANOVA were used with stimulus type (neutral vs. gambling) and response type (failed stop-

signal RT vs. go signal RT pooled across all stop-signal probability levels) as within-subjects

factors; groups (controls vs. Relax_G vs. Gambl_G) as between-subjects factor; and RT as

dependent measure. These analyses revealed a main effect of response type, F(1,68) = 761.92,

p < .001, η² = .92, indicating that RT was lower for failed stop-signal (M = 845, SD = 95) than

for go_signal RT (M = 946, SD = 89). There was a main effect of stimulus type, F(1,68) =

90.99, p < .001, η² = .59, indicating that RT was lower for neutral (M = 872, SD = 94) than

gambling stimuli (M = 919, SD = 97). There was a stimulus type × response type interaction,

F(1,68) = 11.43, p = .001, η² = .15, indicating that RT difference between failed stop-signal

(neutral: M = 816, SD = 100; gambling: M = 874, SD = 97) and go_signal RT (neutral: M =

928, SD = 94; gambling: M = 964, SD = 88) was higher for neutral than for gambling stimuli.

No other significant effect was observed (all p > .06).

Proportion of missed responses according to the level of stop-signal probability. Mixed-

model ANOVA were used with level of stop-signal probability > 0% (yellow, orange, red)

and stimulus type (neutral vs. gambling) as within-subjects factors; groups (controls vs.

Relax_G vs. Gambl_G) as between-subjects factor; and proportion of missed responses as

dependent measure (see Table S2 for descriptive statistics). These analyses revealed a main

effect of stop-signal probability, F(2,67) = 12.15, p < .001, η² = .16, indicating that the

proportion of miss increased with the level of stop-signal probability. There was a main effect

of stimulus type, F(1,68) = 13.08, p = .001, η² = .17, indicating that the proportion of miss

was higher for gambling than for neutral pictures. No other significant effect was observed

(all p > .11).

Stimuli categorization accuracy. Mixed-model ANOVA were used with level of stop-signal

probability (green, yellow, orange, red) and stimulus type (neutral vs. gambling) as within-

subjects factors; groups (controls vs. Relax_G vs. Gambl_G) as between-subjects factor; and

proportion of correct stimuli categorization as dependent measure (see Table S2 for

descriptive statistics). These analyses revealed a main effect of level of stop-signal

probability, F(3,66) = 17.35, p < .001, η² = .21. Pairwise comparisons revealed that the

proportion of correct stimuli categorization was higher in the green context as compared to

the yellow, orange and red contexts (all p < .003), and in the yellow context as compared to

the orange (p = .039) and red contexts (p = .003). No other significant result was observed (all

p > .11).

Probability of responding on stop-signal trials according the level of stop-signal probability.

Mixed-model ANOVA were used with level of stop-signal probability > 0% (yellow, orange,

red) and stimulus type (neutral vs. Relax_G vs. Gambl_G) as within-subjects factors; groups

(controls vs. gamblers) as between-subjects factor; and probability of responding on stop-

signal trials as dependent measure (see Table S2 for descriptive statistics). These analyses

revealed a main effect of level of stop-signal probability, F(2,67) = 21.42, p < .001, η² = .25,

indicating that probability of responding on stop-signal trials decrease in function of stop-

signal probability. These analyses revealed a main effect of stimulus type, F(1,68) = 12.16, p

= .001, η² = .16, indicating that probability of responding on stop-signal trials decrease was

higher for neutral (M = .52, SD = .17) than for gambling (M = .49, SD = .14) stimuli.

Gambling cue ratings. After having performed the SST task, participants were asked to rate

each one the 18 gambling SST pictures (7 points Likert scale, ranging from “not at all” to “a

lot”) on (i) familiarity, (ii) pleasantness, (iii) and on whether the picture make them feel the

desire to gamble.

First, reliability analyses revealed than there was a high consistency between cannabis

pictures used in the task when rated by participants on “familiarity” (control group: Cronbach’

s α = .96; Relax_G group: Cronbach’ s α = .93; Gambl_G group: Cronbach’ s α = .94),

“pleasantness” (control group: Cronbach’ s α = .96; Relax_G group: Cronbach’ s α = .90;

Gambl_G group: Cronbach’ s α = .90) and “desire to gamble” (control group: Cronbach’ s α

= .96; Relax_G group: Cronbach’ s α = .93; Gambl_G group: Cronbach’ s α = .93).

Moreover, within the control Relax_G and Gambl_G groups, the three ratings dimensions

correlate (Pearson) significantly with each others (all p < .001).

Second, mixed-model ANOVA were used with dimension (familiarity vs. pleasantness

vs. desire to gamble) as within-subjects factor; groups (control vs. Relax_G vs., Gambl_G) as

between-subjects factor; and rating scores as dependent measure. These analyses revealed a

main effect of dimension, F(2,67) = 15.19, p < .001, η² = .19, indicating that rating score of

“familiarity” (M = 4.28, SD = 2.01) were higher than scores of “desire to gamble” (M = 3.80,

SD = 1.74; p < .001), with “pleasantness” (M = 3.95, SD = 1.63; p < .001) as an intermediate

score. There was a main effect of group, F(2,67) = 39.43, p < .001, η² = .55, indicating that

rating scores were higher in the Relax_G (M = 4.94, SD = 1.23) and Gambl_G groups (M =

4.98, SD = 1.27), as compared to the control group (M = 2.30, SD = 1.19). There was a

dimension × group interaction, F(2,67) = 6.88, p = .001, η² = .18, indicating that, difference of

ratings between the “familiarity” and both the “pleasantness” and the “desire to smoke” was

higher in the Relax_G vs., Gambl_G groups than in the control group (see Figure S2).

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Table S1. Overview of the stop-signal task performance in Study 1. Stimuli Categorization

accuracy [p(correct)], go response RT (in ms), probability of a missed go response [p(miss)]

and probability of responding on stop-signal trials [p(respond|signal)] as a function of

stimulus type and level of stop-signal probability (0 = green, 17 = yellow, 25 = orange,

33 = red) and group. M = mean; sd = standard deviation.

Table S2. Overview of the stop-signal task performance in Study 2.

Figure S2. (a) Scores of cannabis pictures rating for the CUD and the control groups. All errors bars indicate 95% confidence intervals. (b) Scores of gambling pictures rating for the Gambl_G, Relax_G and the control groups. All errors bars indicate 95% confidence intervals.

(a)

(b)