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Effects of Gambling-Related Cues on the Activation of Implicit and Explicit Gambling Outcome Expectancies in Regular Gamblers

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The current research examined whether the presentation of gambling-related cues facilitates the activation of gambling outcome expectancies using both reaction time (RT) and self-report modes of assessment. Gambling outcome expectancies were assessed by having regular casino or online gamblers (N = 58) complete an outcome expectancy RT task, as well as a self-report measure of gambling outcome expectancies, both before and after exposure to one of two randomly assigned cue conditions (i.e., casino or control video). Consistent with hypotheses, participants exposed to gambling-related cues (i.e., casino cue video condition) responded faster to positive outcome expectancy words preceded by gambling prime relative to non-gambling prime pictures on the post-cue RT task. Similarly, participants in the casino cue video condition self-reported significantly stronger positive gambling outcome expectancies than those in the control cue video condition following cue exposure. Activation of negative gambling outcome expectancies was not observed on either the RT task or self-report measure. The results indicate that exposure to gambling cues activates both implicit and explicit positive gambling outcome expectancies among regular gamblers.
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ORIGINAL PAPER
Effects of Gambling-Related Cues on the Activation
of Implicit and Explicit Gambling Outcome Expectancies
in Regular Gamblers
Melissa J. Stewart Sunghwan Yi Sherry H. Stewart
Springer Science+Business Media New York 2013
Abstract The current research examined whether the presentation of gambling-related
cues facilitates the activation of gambling outcome expectancies using both reaction time
(RT) and self-report modes of assessment. Gambling outcome expectancies were assessed
by having regular casino or online gamblers (N=58) complete an outcome expectancy
RT task, as well as a self-report measure of gambling outcome expectancies, both before
and after exposure to one of two randomly assigned cue conditions (i.e., casino or control
video). Consistent with hypotheses, participants exposed to gambling-related cues (i.e.,
casino cue video condition) responded faster to positive outcome expectancy words pre-
ceded by gambling prime relative to non-gambling prime pictures on the post-cue RT task.
Similarly, participants in the casino cue video condition self-reported significantly stronger
positive gambling outcome expectancies than those in the control cue video condition
following cue exposure. Activation of negative gambling outcome expectancies was not
observed on either the RT task or self-report measure. The results indicate that exposure to
gambling cues activates both implicit and explicit positive gambling outcome expectancies
among regular gamblers.
M. J. Stewart (&)S. H. Stewart
Department of Psychology, Dalhousie University, 1355 Oxford Street, Halifax, NS B3H 4J1,
Canada
e-mail: stewart.melissa@dal.ca
S. H. Stewart
e-mail: sherry.stewart@dal.ca
S. Yi
Department of Marketing & Consumer Studies, University of Guelph, Macdonald Institute Building,
50 Stone Road East, Guelph, ON N1G 2W1, Canada
e-mail: syi@uoguelph.ca
S. H. Stewart
Department of Psychiatry, QEII Health Sciences Centre, Dalhousie University, 5909 Veterans’
Memorial Lane, 8th floor, Abbie J. Lane Memorial Building, Halifax, NS B3H 2E2, Canada
S. H. Stewart
Department of Community Health & Epidemiology, Centre for Clinical Research, Dalhousie
University, 5790 University Avenue, Halifax, NS B3H 1V7, Canada
123
J Gambl Stud
DOI 10.1007/s10899-013-9383-8
Keywords Gambling outcome expectancies Gambling-related cues Implicit measures
Explicit measures Affective priming task
Introduction
Research on outcome expectancies has been extremely influential in the field of alcohol
addiction (see Goldman et al. 1999; Sayette 1999, for reviews). Researchers have dem-
onstrated that alcohol use behaviors are influenced by the outcomes that individuals expect
will occur from consuming alcohol (e.g., ‘‘If I drink, then’). Further, alcohol outcome
expectancies have been theorized as a key mediator of the relation between exposure to
alcohol-related cues (or drinking ‘triggers’) and alcohol use behavior (Goldman 2002;
Goldman and Rather 1993). Indeed, positive alcohol outcome expectancies have been
found to be strongly associated with more frequent and intense drinking (Goldman et al.
1999).
Given the theoretical significance of outcome expectancies in the alcohol field, as well
as the similarities between alcohol and gambling as addictions (e.g., Potenza 2006), out-
come expectancies also may play an important role in gambling. However, little research
has investigated the significance of outcome expectancies in relation to gambling. This is
troubling, as the few studies that have been conducted have shown that gambling outcome
expectancies are indeed associated with increased levels of gambling problems (Gillespie
et al. 2007b). Previous research on gambling outcome expectancies has primarily relied on
self-reports (Gillespie et al. 2007a,b; Shead and Hodgins 2009). For example, Gillespie
et al. (2007a) developed a self-report measure of gambling outcome expectancies, which
consists of three positive expectancy subscales (i.e., enjoyment/arousal, self-enhancement,
and money) and two negative expectancy subscales (i.e., over-involvement, and [negative]
emotional impact). Probable pathological gamblers scored higher than other gamblers on
their expectations of both the positive and negative outcomes of gambling (Gillespie et al.
2007b).
Although use of the self-report mode has been typical in assessing outcome expec-
tancies, its limitations have been increasingly recognized (e.g., Kramer and Goldman 2003;
Palfai and Ostafin 2003). Influenced by cognitive psychology, addiction researchers have
increasingly adopted the view that alcohol outcome expectancies are represented in the
associative memory network (e.g., Goldman et al. 1999; Stacy 1997). According to this
view, the strength of a given alcohol outcome expectancy is defined as the speed with
which the concept of drinking (or exposure to alcohol-related cues) facilitates the acti-
vation of the outcome expectancy in memory. For example, individuals who have a very
strong positive outcome expectancy of alcohol use should experience faster activation of
the positive outcome expectancy when exposed to beer or liquor bottles than those with
weak positive alcohol outcome expectancies.
In order to assess these individual differences in the strength of outcome expectancies,
addiction researchers have used implicit measures, such as reaction time (RT) tasks.
Compared to self-report measures of outcome expectancies, RT measures are less sus-
ceptible to social desirability bias, more efficient, and more difficult for participants to
consciously control (De Houwer 2006; Wiers et al. 2002). One such implicit RT measure is
the affective priming task (Fazio et al. 1986), which is widely used for examining the
automatic activation of attitudes from memory. Specifically, this procedure assesses the
extent to which the presentation of a prime (e.g., a picture) activates an associated
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123
evaluation (i.e., positive or negative) from memory. On each trial, the presentation of a
prime is followed by the display of either a positive or negative evaluative adjective (i.e.,
target). The participant’s task is to indicate the connotation of the target word as quickly as
possible (e.g., is the word ‘positive’ or ‘negative’?). Participants’ RT latency to this
judgment represents the outcome measure (Fazio et al. 1995). Alcohol outcome expec-
tancies assessed using implicit measures, such as the affective priming task, have been
found to be positively associated with alcohol consumption (see Goldman et al. 2006).
Applying the affective priming paradigm to the gambling domain, differences in the
strength of gambling outcome expectancies theoretically can be assessed by comparing the
speed with which exposure to the concept of gambling facilitates the automatic activation
of gambling outcome expectancies in memory. Specifically, individuals who have a strong
positive expectancy of gambling outcomes should experience faster activation of the
positive outcome expectancy upon exposure to gambling-related cues than those with a
weak positive outcome expectancy of gambling.
Despite the advantages of implicit measures discussed above, self-report measures of
outcome expectancies are not necessarily inferior to RT measures. In their reflective-
impulsive model, Strack and Deutsch (2004) purport that behavior is controlled by two
interacting systems: the reflective system and the impulsive system. In the reflective system,
behavior is the result of a conscious decisional process whereas in the impulsive system,
behavior is evoked through unconscious associations. Thus, both self-report and RT mea-
sures can be considered complementary in assessing outcome expectancies in that self-
report measures assess deliberative determinants of behavior, while RT measures assess
automatic determinants (see Wiers and Stacy 2006). Given that implicit and explicit
measures appear to tap into different facets of outcome expectancies in the alcohol field
(e.g., de Jong Wiers et al. 2007; Kramer and Goldman 2003), it may be similarly important
to make use of both modes of assessment when examining gambling outcome expectancies.
The purpose of this study was to investigate factors that facilitate the activation of
gambling outcome expectancies using both RT and self-report modes of assessment.
Drawing upon the affective priming paradigm (Fazio et al. 1986), the current study
assessed whether the presentation of gambling-related concepts (i.e., primes) leads to the
automatic activation of gambling outcome expectancies stored in regular gamblers’
memory networks. Specifically, it has been previously postulated in the alcohol field
(Goldman and Rather 1993; Goldman 2002) that situational cues related to alcohol use that
are repeatedly paired with positive affective outcomes of drinking are stored together in
memory. When individuals are later exposed to situational alcohol cues, these cues are said
to activate positive outcome expectancies in memory. In fact, Palfai and Ostafin (2003)
found that the RT to positive alcohol outcome expectancy terms was significantly faster
when hazardous drinkers consumed a priming dose of alcohol than when they consumed a
non-alcoholic placebo beverage.
In the present study, it was proposed that exposure to gambling-related cues (i.e., a
video of casino scenes) immediately prior to the assessment of gambling outcome
expectancies would activate positive gambling outcome expectancies in memory among
regular gamblers. Thus, it was predicted that compared to those who viewed a video
unrelated to gambling (i.e., control cue video condition), gamblers who viewed a gam-
bling-related video (i.e., casino cue video condition) would subsequently be significantly
faster in responding to positive outcome expectancy targets when they were preceded by
gambling picture primes relative to non-gambling picture primes. In relation to the explicit
(self-report) measure of gambling outcome expectancies, it was predicted that participants
in the casino cue video condition would self-report significantly higher positive gambling
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123
outcome expectancies following cue exposure than those in the control cue video condi-
tion. We expected these effects to be observed only at the post-cue test phase (i.e., after
viewing the video). The pre-cue test phase was included and analyzed as a pre-manipu-
lation baseline. Drawing upon the reflective-impulsive model of behavior (Strack and
Deutsch 2004), because the casino cue video was of a relatively long duration, allowing
ample opportunity for participants to process the gambling stimuli, it was expected that the
video manipulation would have similar effects on both our implicit and explicit measures
of positive gambling outcome expectancies.
With respect to negative outcome expectancies, in the alcohol literature some studies
have found a negative association between negative outcome expectancies and drinking
while others have found a positive association. These findings suggest that negative alcohol
expectancies may be protective against heavy drinking or a consequence of heavy con-
sumption, respectively (Jones and McMahon 1996; Stacy et al. 1990). Since the direction
of the hypothesized relation between negative outcome expectancies and gambling remains
unclear, we included implicit and explicit measures of negative gambling outcome
expectancies to explore the effects of gambling cue exposure on these measures.
Method
Participants
Participants consisted of 58 adult gamblers (38 males and 20 females) who ranged in age
from 19 to 61 years (M=29.97, SD =12.04). Participants were recruited through
advertisements posted on university bulletin boards, as well as in local newspapers and
classified websites. Thirty-six participants were recruited from the Halifax Regional
Municipality in Nova Scotia, while the remaining 22 participants were recruited from the
greater Guelph area in Ontario. Upon leaving their contact information, potential partici-
pants were contacted by telephone and screened to determine eligibility.
In order to be eligible to participate, individuals had to have gambled at a casino or
online
1
at least three times over the past two months. As RT measures require extremely
rapid responses to English words, only individuals whose native language was English
were eligible to participate. Individuals were excluded if they were currently attempting to
quit gambling or receiving treatment for problem gambling given ethical concerns that
exposure to gambling-related cues could theoretically trigger a return to problem gambling.
Participants were compensated $30 for their participation in the study.
Using the Problem Gambling Severity Index (PGSI) from the Canadian Problem
Gambling Index (CPGI; Ferris and Wynne 2001), participants consisted of 3 non-problem
gamblers (i.e., total score of 0), 8 low-risk gamblers (i.e., total score ranging from 1 to 2),
32 moderate-risk gamblers (i.e., total score ranging from 3 to 7), and 15 high-risk/problem
gamblers (i.e., total score of 8 or above). Total scores on the PGSI ranged from 0 to 25
(M=6.26; SD =5.20). Participants engaged in a range of gambling activities during the
three months prior to taking part in the study, including casino gambling (e.g., slots,
blackjack, poker, roulette), video lottery terminal gambling, sports betting (e.g., Proline,
hockey pools), online gambling, card games with friends, and raffle and lottery tickets.
1
The inclusion criterion that individuals had to have gambled at a casino or online was made to ensure that
the gambling-related primes in the RT task would apply to all gambler participants equally.
J Gambl Stud
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Materials
Problem Gambling Symptoms
The nine-item PGSI scale of the CPGI (Ferris and Wynne 2001) was used to assess the
presence and severity of gambling problems among participants. The PGSI contains five
items that assess problem gambling behavior (e.g., ‘‘Have you bet more than you could
really afford to lose?’’) and four items addressing the negative consequences of gambling
(e.g., ‘‘Has gambling caused you any health problems, including stress or anxiety?’’). For
each item, respondents indicated the frequency at which they have engaged in the behavior
or experienced the given consequence in the last 12 months using a four-point scale
ranging from 0 (never)to3(almost always). Previous research indicates that the PGSI has
good psychometric properties. Specifically, Ferris and Wynne (2001) found that the PGSI
demonstrated adequate reliability in terms of both internal consistency (a=.84) and test–
retest reliability (r=.78). The PGSI has also been found to demonstrate overall good
validity (i.e., construct, criterion, content validity) as a measure of problem gambling
(Ferris and Wynne 2001). Further, an independent study found that compared to other
gambling measures (e.g., SOGS), the PGSI demonstrated favourable psychometric prop-
erties in non-clinical populations in terms of construct validity, classification validity, and
item difficulty (McMillen and Wenzel 2006). In the present study, the PGSI demonstrated
good internal consistency (a=.89).
Self-Reported Gambling Outcome Expectancies
The 23-item Gambling Expectancy Questionnaire (GEQ; Gillespie et al. 2007a) was used
to assess self-reported gambling outcome expectancies at both pre and post-cue manipu-
lation. The GEQ consists of three positive expectancy subscales (enjoyment/arousal, self-
enhancement, and money) and two negative expectancy subscales (over-involvement and
emotional impact). Participants were asked to what extent they expected each item/out-
come (e.g., ‘‘I win money’’; ‘‘I feel excited’’; ‘‘I will feel guilty’’; ‘‘I will not be able to
stop’’) would occur when gambling on a seven-point scale ranging from 1 (no chance)to7
(certain to happen). In relation to its psychometric properties, Gillespie et al. (2007a)
reported that each of the subscales demonstrated adequate to good internal reliability. In
order to obtain an overall measure of participants’ positive gambling outcome expectan-
cies, the three positive subscales of the GEQ were combined in the present study. This
resulted in a 15-item scale assessing positive gambling outcome expectancies. The two
negative expectancy subscales of the GEQ were also combined to obtain an overall
measure of participants’ negative gambling outcome expectancies. This resulted in an
8-item scale assessing self-reported negative gambling outcome expectancies. The 15-item
positive gambling expectancy scale of the GEQ demonstrated adequate to good reliability
at both pre-cue (a=.78) and post-cue (a=.83) administration. Further, the 8-item
negative gambling expectancy scale demonstrated excellent reliability during both pre-cue
(a=.92) and post-cue administration (a=.95) in the present study.
Affective Outcome Expectancy RT Task
Adapted from the classic affective priming task (Fazio et al. 1995), this RT-based task was
used to assess the activation of affective gambling outcome expectancies. The task was
designed to measure how quickly individuals respond to positive and negative gambling
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outcome expectancy words (i.e., targets) immediately after being primed by gambling
versus control category (i.e., track and field) pictures.
2
The task was executed via
Empirisoft Inc.’s DirectRT experimental psychology software (Jarvis 2010). The target
word exemplars were selected based on a review of established self-report measures of
gambling outcome expectancies (e.g., GEQ; Gillespie et al. 2007a), as well as synonyms of
words from these measures. In total, there were 10 positive outcome expectancy words and
10 negative outcome expectancy words used as targets (see Table 1). In addition, 10
gambling-related and 10 non-gambling-related pictures were used as primes. The task
consisted of two phases: pre-cue test phase (i.e., baseline) and post-cue test phase. Each
phase began with one block of four practice trials, and two blocks with 20 test trials each
(total number of trials was 88 across the two phases). The stimuli for practice trials were
different than those presented during the test trials. Each phase was presented to partici-
pants as one continuous series. During each priming phase, each outcome expectancy
target word was presented four times: twice preceded by a gambling prime picture, and
twice preceded by a non-gambling prime picture. The order of primes and targets within
each block was counterbalanced across participants.
In the pre-cue test phase, each trial started with the presentation of either a gambling-
related or non-gambling-related (i.e., track and field) picture in the centre of the screen
which lasted for 200 ms. This was followed by a blank screen (100 ms), then by the
presentation of a target word (in the centre of the screen as well) that had either a
positive (e.g., excitement) or negative (e.g., tension) connotation. Participants were asked
to respond to words that had a negative connotation by clicking the ‘‘Z’’ key on the
keyboard, and to respond to words that have a positive connotation by clicking the ‘‘/’
key. The length of the inter-trial interval was 1,000 ms. The post-cue test phase was
identical to the pre-cue test phase. Participants were told that they needed to pay
attention to the pictures presented on the screen as their memory for the pictures may be
tested later. Participants were also informed that the first four trials of each test phase
were practice trials.
Table 1 Word exemplars used
in the gambling outcome expec-
tancy RT task
For the practice trials, positive
outcome expectancy words
consisted of: ‘amusement’ and
‘happiness’, whereas negative
outcome expectancy words
consisted of ‘boredom’ and
‘sorrow’
Positive outcome
expectancy words
Negative outcome
expectancy words
Fun Guilt
Relaxation Shame
Excitement Tension
Enjoyment Confusion
Esteem Frustration
Acceptance Anxiety
Winning Worry
Stimulation Dissatisfaction
Pleasure Anger
Satisfaction Displeasure
2
We selected track and field as the control category because it is an activity that is similar to gambling in
both size and complexity. Specifically, both gambling and track and field are broad categories that
encompass a variety of different activities. Track and field is also an activity that could theoretically be
associated with both positive outcomes (excitement, winning) and negative outcomes (frustration, tension).
J Gambl Stud
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Procedure
Upon arrival at the laboratory, participants provided informed consent and were randomly
assigned to the casino cue video condition or control cue video condition. Participants then
engaged in the first phase of the affective outcome expectancy RT task (pre-cue test
phase). Immediately after the pre-cue test phase, participants completed the GEQ
(Gillespie et al. 2007a) and a demographic questionnaire. Upon completing these ques-
tionnaires, participants were exposed to the cue manipulation to which they had been
randomly assigned (either the casino cue video or control cue video condition). In the
casino cue video condition (n=29), participants watched a 5-min video of typical casino
scenes with ambient noise reflecting the sounds that would be heard in a casino (e.g.,
sounds of slot machines paying out). In the control cue video condition (n=29), par-
ticipants watched a 5-min video of typical track and field scenes with ambient noise
reflecting the sounds that would be heard in a track and field audience (e.g., cheering and
clapping). After watching the video, participants engaged in the post-cue test phase of the
RT task. Participants then completed a second administration of the GEQ (Gillespie et al.
2007a) in order to determine whether any differences in outcome expectancies were
present upon being exposed to the gambling-related/control video. Participants were then
debriefed and compensated $30 for their time and effort.
Results
Preliminary Analyses
The data in both the pre-cue and post-cue manipulation phases contained a small pro-
portion of extremely slow and fast responses. Respectively, such responses typically
indicate momentary inattention and responses initiated prior to receiving the stimulus
(Greenwald et al. 1998). Not only are such responses considered problematic because they
lead to a distortion of means and inflation of variance, but also because they represent
phenomenon outside of interest. Following the recommended procedures to correct for
such responses (Greenwald et al. 1998), values below 300 ms were recoded to 300 ms and
those above 3,000 ms were recoded to 3,000 ms. Further, in order to reduce the charac-
teristic positive skewness of RT latencies and normalize the distribution, a log transfor-
mation was performed on the RT data prior to averaging mean RT scores (see Fazio 1990;
Greenwald et al. 1998). To aid in the interpretation of data, raw (untransformed) RTs are
displayed for descriptive purposes only. At each testing time (i.e., pre- and post-video
exposure), four composite RT scores were calculated for each participant. These repre-
sented the mean RTs for trials involving each of the four prime-target combinations (i.e.,
gambling prime-positive outcome expectancy target; gambling prime-negative outcome
expectancy target; non-gambling prime-positive outcome expectancy target; non-gambling
prime-negative outcome expectancy target).
In order to confirm random assignment and the equivalency of groups by experimental
condition, analyses were conducted to determine whether there were any systematic, pre-
existing differences between the casino cue video condition and the control cue video
condition on level of problem gambling severity, age, and gender. In relation to level of
problem gambling severity, an independent samples ttest revealed no significant difference
between the control cue video (M=6.07, SD =5.48) and casino cue video (M=6.44,
SD =5.00) conditions on level of problem gambling severity, t(56) =.28, p=.78.
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Further, an independent samples ttest revealed no significant age differences between
participants in the control cue video (M=30.66, SD =12.70) and casino cue video
(M=29.28, SD =11.52) conditions, t(56) =-.43, p=.67. Lastly, a Chi square test
revealed no significant gender differences between the control cue video (males: n=19;
females: n=10); and casino cue video (males: n=19; females: n=10) conditions, v
2
(1, N=58) =.00, p=1.00. As such, it was deemed that any differences observed
between groups may be attributed to the effect of the manipulation and not to pre-existing
or systematic differences between groups. Further, correlational analyses failed to reveal
any significant correlations (ps[.05) between the implicit and explicit measures of
gambling outcome expectancies in either the casino or control video cue conditions at pre-
and post-video cue manipulation (correlations ranged from -.08 to .33).
Affective Outcome Expectancy RT Task Performance
Prior to testing our hypotheses, we examined whether there were any cue manipulation con-
dition differences in RTs to positive or negative outcome expectancy targets when they were
preceded by gambling versus non-gambling primes during the pre-cue manipulation phase (i.e.,
baseline) of the RT task. To do so, two separate 2 92 mixed factorial ANOVAs were con-
ducted—one for the RT data for the positive outcome expectancy targets, and the other for the
RT data for the negative outcome expectancy targets. In both ANOVAs, the between subjects
factor was cue manipulation condition (casino cue video vs. control cue video) and the within
subjects factor was type of prime (gambling vs. non-gambling). The analyses confirmed that
during the pre-cue manipulation phase, there were no significant condition, prime, or interac-
tion effects on RTs to positive or negative outcome expectancy targets (i.e., all ps[.05). These
findings indicate that there was no tendency to associate gambling primes with positive (or
negative) outcomes in either of the two randomly assigned cue conditions (casino or control
video cue), prior to video cue exposure. Descriptive statistics for RTs to negative and positive
outcome expectancy targets when preceded by gambling and non-gambling primes for the
casino cue video and control cue video conditions during the pre-cue manipulation phase are
displayed in Table 2. This lack of effects at the pre-cue exposure phase meant that baseline RTs
did not need to be controlled in hypothesis testing.
We employed a priori planned comparisons in our hypotheses testing. Specifically,
following the analytic strategy of Birch et al. (2008), we analyzed RT data on the post-cue
manipulation affective priming task with relation to the initial hypotheses by decomposing
Table 2 Means and standard deviations of pre-cue RTs (in milliseconds) to positive and negative outcome
expectancy words upon presentation of gambling and non-gambling primes for the control cue video and
casino cue video conditions
Gambling primes Non-gambling primes
MSDM SD
Control cue video condition
Positive target words 807.82 237.81 795.86 194.79
Negative target words 840.56 264.90 866.45 301.84
Casino cue video condition
Positive target words 837.39 250.08 818.97 218.77
Negative target words 833.65 218.11 863.00 247.41
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the full 2 (cue manipulation: casino cue video vs. control cue video) 92 (primes: gam-
bling vs. non-gambling) 92 (targets: positive vs. negative outcome expectancy words)
table of means into a series of directional paired samples ttests. As recommended by
Tabachnick and Fidell (2007), conventional alpha levels were used to analyse the com-
parisons of primary interest first (Tabachnick and Fidell 2007). Specifically, these direc-
tional paired-samples ttests were used to compare RTs to categorize outcome expectancy
targets after exposure to gambling versus non-gambling primes at the post-cue manipu-
lation testing time for each cue manipulation condition and target type separately.
Descriptive statistics for RTs to negative and positive outcome expectancy targets when
preceded by gambling and non-gambling primes for the casino cue and control cue video
conditions during the post-cue manipulation phase are displayed in Table 3.
Consistent with hypotheses, participants in the casino cue video condition responded faster
to positive outcome expectancy words when they were preceded by gambling primes relative to
non-gambling primes, t(28) =-1.69, p=.05, representing a marginally significant differ-
ence in RTs (see Table 3). While participants in control cue video condition tended to respond
faster to positive outcome expectancy words whenthey were preceded by non-gambling primes
relative to gambling primes, a significant facilitation of positive gambling outcome expec-
tancies by non-gambling primes was not observed in the control cue condition, t(28) =1.04,
p=.16. This lack of significance can be attributed to greater variability (i.e., larger standard
deviation) in RT among participants in the control cue video condition relative to those in the
gambling cue video condition when responding to positive target words after being exposed to
both gambling primes and non-gambling primes (see Table 3).
When examining RTs to negative outcome expectancy targets among participants in the
casino cue video condition, no significant differences were found between participants’
RTs to targets preceded by gambling primes versus non-gambling primes, t(28) =-.46,
p=.33 (see Table 3). Similarly, there were no significant differences in the RTs to
negative outcome expectancy targets among participants in the control cue video condition
when they were exposed to gambling primes versus non-gambling primes, t(28) =-.33,
p=.37 (see Table 3).
Cue Condition Differences in Self-Reported Gambling Outcome Expectancies
Prior to assessing whether exposure to gambling-related cues led to an increase in self-
reported gambling outcome expectancies, we examined whether any group differences
Table 3 Means and standard deviations of post-cue RTs (in milliseconds) to positive and negative outcome
expectancy words upon presentation of gambling and non-gambling primes for the control cue video and
casino cue video conditions
Gambling primes Non-gambling primes
MSDM SD
Control cue video condition
Positive target words 750.28 262.45 712.74 177.81
Negative target words 788.64 258.26 801.77 281.38
Casino cue video condition
Positive target words 721.19* 160.79 739.34* 166.19
Negative target words 740.34 157.61 753.23 190.37
*Indicates a marginally significant difference between means (p=.05)
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existed in self-reported positive and negative outcome expectancies [as measured by the
GEQ (Gillespie et al. 2007a)] before viewing the cue manipulation videos (i.e., at baseline).
In order to test this, two separate independent-samples ttests were conducted. In both ttests,
the independent variable was cue manipulation condition (casino cue video vs. control cue
video). In the first ttest, the dependent variable was self-reported positive gambling out-
come expectancies on the GEQ, whereas the dependent variable in the second ttest was self-
reported negative gambling outcome expectancies on the GEQ. The analyses revealed that
there were no significant differences in either self-reported positive (casino cue video:
M=4.46, SD =.61; control cue video: M=4.27, SD =.68) or negative (casino cue
video: M=2.96, SD =1.35; control cue video: M=2.72, SD =1.05) gambling outcome
expectancies prior to the cue manipulation (i.e., both ps[.05). These findings indicate
random assignment to cue manipulation conditions was effective in equating the two
conditions on their baseline (pre-cue exposure) positive and negative gambling outcome
expectancies. This lack of condition effects at the pre-cue exposure (baseline) phase meant
that baseline GEQ scores did not need to be controlled in hypothesis testing.
We then examined our a priori planned comparison regarding cue condition differences
in self-reported positive gambling outcome expectancies following exposure to the cue
manipulation by performing directional independent samples ttests. Consistent with our
hypothesis, participants in the casino cue video condition (M=4.26, SD =.60) reported
significantly higher scores on the self-report measure of positive gambling outcome
expectancies than those in the control cue video condition (M=3.87, SD =.85),
t(56) =1.98, p=.03. However, this effect was not found for negative gambling outcome
expectancies. Specifically, an independent samples ttest revealed that participants in the
casino cue video (M=2.66, SD =1.47) and control cue video condition (M=2.45,
SD =1.29) did not significantly differ in their self-reported negative gambling outcome
expectancies after exposure to the cue manipulation, t(56) =.58, p=.56.
Discussion
Although outcome expectancies have been found to play an important role in addictive
behaviors (e.g., Goldman et al. 1999; Sayette 1999), a paucity of research has been con-
ducted on the relation between outcome expectancies and gambling. To address this gap in
the literature, the current research investigated the possibility that exposure to gambling-
related cues facilitates the activation of gambling outcome expectancies using implicit (i.e.,
RT), as well as explicit (i.e., self-report) modes of assessment. It was hypothesized that
compared to those who viewed a video unrelated to gambling (i.e., control cue video
condition), gamblers who viewed a gambling-related video (i.e., casino cue video condi-
tion) would subsequently be significantly faster in responding to positive outcome
expectancy targets when they are preceded by gambling picture primes relative to non-
gambling picture primes. In relation to the explicit (self-report) measure of gambling
outcome expectancies, it was predicted that participants in the casino cue video condition
would self-report significantly higher positive gambling outcome expectancies following
cue exposure than those in the control cue video condition.
Cue Condition Differences in the RT Measure of Outcome Expectancies
Consistent with our predictions, participants who were exposed to a video of typical
gambling-related scenes (i.e., casino cue video condition) responded faster to positive
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123
outcome expectancy words when they were preceded by gambling primes relative to non-
gambling primes (p=.05). This facilitation of positive outcome expectancies by gambling
primes was not observed in the control cue video condition, as participants who viewed a
video of typical track and field scenes did not significantly differ in RTs to positive
outcome expectancy words when they were preceded by gambling primes relative to non-
gambling primes. While participants in this latter condition, who were exposed to a track-
and-field control video, tended to respond faster to positive outcome expectancy words
when they were preceded by track-and-field control primes relative to gambling primes,
this difference was not statistically significant (p[.05). This pattern of findings rules out
the possibility that participants just responded faster to positive outcome expectancy words
following primes that were ‘‘similar’’ to the previously-viewed videos (i.e., following
gambling primes in the gambling video condition; following track-and-field primes in the
track-and-field video condition) than following primes that were ‘‘different’’ than the
previously-viewed videos (i.e., following track-and-field primes in the gambling video
condition; following gambling primes in the track-and-field video condition).
Instead, these results suggest that exposure to gambling-related cues immediately prior
to the assessment of gambling outcome expectancies activates positive outcome expec-
tancies in memory among regular gamblers. Further, these findings provide additional
support to previous research (Goldman 2002; Goldman and Rather 1993) on the role of
outcome expectancies in alcohol use, which proposes that situational cues related to
alcohol use that are repeatedly paired with positive affective outcomes are stored together
with these outcomes in memory. When later exposed to alcohol cues, these cues sub-
stantially facilitate the degree to which the concept of alcohol activates positive outcome
expectancies. In addition to providing further empirical support for this proposal, the
current study extends this line of reasoning by showing evidence of its applicability to
gambling. It is also important to highlight that engaging in gambling activities was not
necessary to obtain these findings. Specifically, the activation of positive gambling out-
come expectancies was found to occur when participants were simply exposed to typical
gambling scenes, an experience that appears quite similar to watching others gamble or
viewing gambling-related advertisements.
In addition, these findings partially coincide with Palfai and Ostafin’s (2003) research
assessing the activation of alcohol outcome expectancies using a similar RT task. Spe-
cifically, Palfai and Ostafin (2003) found that compared to administration of a non-alco-
holic beverage, administration of a low dose of alcohol was associated with a faster RT to
positive alcohol outcome expectancies among hazardous drinkers. Unexpectedly, this did
not differ depending upon whether participants in their study received alcohol or non-
alcohol related primes prior to the presentation of outcome expectancy targets. In contrast,
the current research found that participants in the casino cue video condition exhibited
evidence of faster responses to positive outcome expectancy targets when primed by
gambling pictures than when primed by non-gambling pictures.
One possible explanation for this discrepancy in findings may relate to the stimuli used
as primes. Specifically, primes in the current research consisted of gambling and non-
gambling pictures, whereas in Palfai and Ostafin’s (2003) research, alcohol and non-
alcohol related words were used as primes. Given that pictures have been found to be
remembered better than words (e.g., Grady et al. 1998; Seifert 1997) and the primes in both
studies were presented only briefly, it is possible that participants in the current study were
better able to store the gambling and non-gambling primes in memory. As a result of
a potential increased memory of primes in the current research, the presentation of gam-
bling primes relative to non-gambling primes among those previously exposed to
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gambling-related cues on the video led to a faster activation of positive outcome expec-
tancy concepts in memory. Applying this to the alcohol domain, results of the current
research suggest that the activation of positive outcome expectancies among regular
drinkers may only require exposure to drinking scenes rather than actual alcohol use.
A further potential explanation for the discrepancy in findings may relate to the different
analytic strategies employed in the present study and Palfai and Ostafin’s (2003) research.
Specifically, the current research analyzed the RT data on the post-cue manipulation
affective priming task with relation to our initial hypotheses by conducting specific
planned comparisons, whereas Palfai and Ostafin’s (2003) did not decompose the full
tables of means (i.e., video cue manipulation, primes, and targets) when analyzing the RT
task performance in their research and instead used an omnibus ANOVA.
Although exposure to gambling-related cues appeared to activate positive gambling
outcome expectancies in memory among regular gamblers in our study, this was not the
case for negative gambling outcome expectancies. Specifically, when examining RTs to
negative outcome expectancy targets among participants in the casino cue video condition,
no significant differences were found between participants’ RTs to targets that were pre-
ceded by gambling primes versus non-gambling primes. Similarly, there were no signifi-
cant differences in the RTs to negative outcome expectancy targets among participants in
the control cue video condition after they were exposed to gambling primes versus non-
gambling primes. These findings are consistent with previous research in the alcohol field,
which suggests that negative outcome expectancies reflect outcomes less proximal to
alcohol use than positive outcome expectancies (Jones et al. 2001).
Cue Condition Differences in Self-Reported Gambling Outcome Expectancies
When examining participants’ self-reported positive gambling outcome expectancies fol-
lowing exposure to the cue manipulation, it was found that participants in the casino cue
video condition scored significantly higher on the self-report measure of positive gambling
outcome expectancies than those in the control cue video condition. However, this was not
the case for negative gambling outcome expectancies, as participants in the casino cue and
control cue video conditions did not significantly differ in their self-reported negative
gambling outcome expectancies after exposure to the video cue manipulation.
In line with our predictions, these findings suggest that the presentation of gambling-
related cues leads to an increase in the expected positive outcomes that gamblers report
will occur from gambling. These findings are consistent with the results of the RT task and
as such, provide converging evidence of the impact of gambling-related cues on the
facilitation of positive gambling outcome expectancies. That is, using both implicit and
explicit modes of assessment, the current research found that the presentation of gambling-
related cues leads to an activation of positive gambling outcome expectancies in memory.
Further, these results build upon previous research (e.g., Gillespie et al. 2007b) using this
self-report measure to assess gambling outcome expectancies by suggesting that exposure
to gambling-related cues is associated with an increase in self-reported positive gambling
outcome expectancies. In addition, consistent with some findings from the alcohol outcome
expectancy literature (e.g., de Jong et al. 2007; Jajodia and Earleywine 2003; Kramer and
Goldman 2003), the implicit and explicit measures of positive gambling outcome expec-
tancies were not significantly correlated, suggesting that these two modes of assessment
may be assessing distinct aspects of gambling cognitions. In order to determine whether
this is the case, it is important that future research investigate whether implicit measures of
gambling outcome expectancies assess a unique facet of the gambling cognition domain
J Gambl Stud
123
that cannot be accessed through explicit measurement. Further, it is important that future
research also examine situational variables that may enhance the predictive validity of
implicit measures of gambling outcome expectancies on gambling behavior.
It is also important to note the potential impact of the length of the gambling-cue
exposure on the activation of implicit and explicit positive gambling outcome expectan-
cies. Although we found that the 5-min video of gambling scenes activated positive out-
come expectancies, as measured by both self-report and the RT task, different results may
have been found had the video been of a shorter duration. Specifically, a relatively brief
presentation of gambling-related cues may not allow individuals the time to engage in the
conscious, deliberative processing of gambling outcome expectancies that is captured by
self-report modes of assessment. As such, had the gambling cue been shorter (e.g., 30 s),
an activation of implicit but not explicit positive outcome expectancies may have been
observed among participants in the current study. Further research is necessary to inves-
tigate whether shorter exposure to gambling-related cues (e.g., gambling advertisements)
activates implicit but not explicit gambling outcome expectancies.
Limitations
Some limitations of the present study should be addressed. First, the hypothesized effect on
the implicit task (i.e., faster RTs to positive expectancy words following gambling vs.
control primes in the gambling video condition) reached marginal significance (p=.05)
rather than the conventional alpha level criterion of p\.05. Thus, it is important that this
effect be replicated in future research to determine its reliability. Second, although findings
from the current research provide support to the prediction that, relative to non-gambling-
related cues, gambling-related cues would lead to an increased activation of positive
gambling outcome expectancies among gamblers, it did not include a control group of
individuals who do not gamble. As such, it is not known whether gamblers respond to this
RT task differently than non-gamblers. In order to address this limitation, future research
should use the affective priming paradigm (Fazio et al. 1986) to determine whether
gamblers display a stronger activation of positive gambling outcome expectancies fol-
lowing exposure to gambling-related cues than non-gamblers.
A further caveat of the current research involves the failure to assess differences in the
activation of gambling outcome expectancies based on level of problem gambling severity.
Specifically, given the current sample size, we were unable to assess whether the presentation
of gambling-related cues leads to a greater activation of positive gambling outcome expec-
tancies among problem gamblers relative to at-risk or low-risk gamblers. As such, it would be
important for future research to examine whether the findings of the current research differ
depending upon level of problem gambling severity. In addition, while the current research
found that exposure to gambling-related cues led to the activation of both implicit and explicit
positive gambling outcome expectancies, we did not determine whether such modes of
assessing gambling outcome expectancies are capable of predicting actual gambling
behavior. Further, we did not examine whether implicit and explicit measures of gambling
outcome expectancies contribute unique, as well as shared variance in the prediction of
different forms of gambling behavior, such as the amount of time and money spent gambling.
As such, it is important that future research make use of both modes of assessment to
determine whether they are independent predictors of gambling behavior, as has been pre-
viously shown in the alcohol outcome expectancy literature (e.g., Wiers et al. 2002).
Lastly, a limitation inherent to the RT task used in the present study should be
acknowledged. Specifically, the current research assessed associations between gambling
J Gambl Stud
123
and outcome expectancies relative to associations with another control activity (i.e., track
and field). As such, we cannot discern whether similar results would persist if an activity
other than track and field had been used as a control. In order to address this limitation, it is
important that future research examine the associations between gambling-related cues and
the facilitation of outcome expectancies relative to associations with different activities.
Implications
Despite the limitations noted, findings from the current research have a number of important
practical as well as clinical implications. Firstly, our results appear to provide an argument
against the legalization of casino advertisements, both online and offline. Specifically, as
results revealed that exposure to gambling-related cues led to an increased activation of
positive gambling outcome expectancies, it may be the case that gambling advertisements
facilitate the activation of positive gambling outcome expectancies among gamblers. Fur-
thermore, chronic activation of positive gambling outcome expectancies may pose a risk for
problematic gambling behavior among members of communities located in close proximity
to gambling venues, as well as employees of such establishments. In terms of clinical
implications, results of the current research point to the potential utility of focusing on
altering not only explicit cognitions but also implicit cognitions as interventions for problem
gambling. For example, expectancy challenges, interventions that which aim to reduce
individuals’ expectancies about the rewarding properties of a substance, have been used to
successfully reduce positive explicit outcome expectancies in the alcohol area (Darkes and
Goldman 1993; Darkes et al. 1998). More recently, cognitive retraining methods have been
developed that alter implicit associations with alcohol from positive to negative (Houben
et al. 2010). Importantly, both methods have been associated with reduced drinking
(Houben et al. 2010; Wiers et al. 2005). Given their encouraging results in the alcohol field,
such interventions may prove effective in reducing both implicit and explicit positive
outcome expectancies among gamblers. Moreover, the present findings suggest that these
interventions might be most optimally employed following gambling cue exposure.
Conclusion
Despite the importance of outcome expectancies in addictive behaviors (e.g., Goldman
et al. 1999; Sayette 1999), research examining outcome expectancies in the gambling field
is in its nascent stage. In order to address this gap in the literature as well as facilitate
further research on gambling outcome expectancies, the present study assessed whether
exposure to gambling-related cues activates gambling outcome expectancies using both
implicit (i.e., RT) and explicit (i.e., self-report) assessment modes. As predicted, findings
from the current research indicate that exposure to gambling-related cues selectively
activates both implicit and explicit positive, but not negative, gambling outcome expec-
tancies among regular gamblers. Findings stemming from this preliminary investigation of
the role of gambling-related cues on the implicit and explicit activation of positive gam-
bling outcome expectancies highlight the relevance of outcome expectancies in the field of
gambling research as well as point to the need for further research that examines the role of
gambling outcome expectancies on gambling behavior. Further, results of the current
research indicate that this novel RT task is a useful instrument, in addition to self-report
measures, in terms of its ability to measure the activation of gambling outcome expec-
tancies among regular gamblers.
J Gambl Stud
123
Acknowledgments We would like to acknowledge and thank Pamela Collins and Scott Connors for their
research assistance with this study. This research was supported by a Research Grant from the Ontario
Problem Gambling Research Centre (#44449) to Sunghwan Yi and Sherry H. Stewart.
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... Positive OEs have a larger impact on hazardous alcohol use than do negative OEs and have a causal role in predicting drinking (i.e., Zamboanga, Horton, Leitkowski, & Wang, 2006). Such findings regarding the importance of OEs in the substance abuse field have led researchers to suggest that OEs may also be important in understanding gambling behaviour (Stewart, Yi, & Stewart, 2014), given the similarities between substance abuse and problem gambling as addictive behaviours (American Psychiatric Association, 2013;Potenza, 2006). ...
... An advantage to the use of the G-BOAT is that it is time efficient, cost-effective, and easily administered and it scored in comparison to some existing implicit RT measures of gambling OEs (Stacy et al., 1994). Use of various implicit measures in conjunction with explicit tasks allows for assessment of both reflective and impulsive systems with respect to gambling behaviour (Stewart et al., 2014), a focus of Study 2. ...
... Comparable implicit affective tasks have been applied in the gambling literature, whereby positive or neutral, or negative or neutral, attribute words were intermixed with gambling pictures and participants were required to rapidly identify the type of word (Brevers et al., 2013). In the current study, we used a version of Fazio et al.'s task, modified by Stewart et al. (2014), to measure RT latencies to respond to gambling-relevant outcomes preceded by gambling and non-gambling pictures. Although both negative and positive OEs were presented, the current study's analyses focused on RTs to categorize positive OEs, as these were the type of OEs examined in the G-BOAT. ...
Article
Outcome expectancies (OEs), or beliefs about the consequences of engaging in a particular behaviour, are important predictors of addictive behaviours. In Study 1 of the present work, we assessed whether memory associations between gambling and positive outcomes are related to excessive and problem gambling. The Gambling Behaviour Outcome Association Task (G-BOAT) was administered to a sample of 96 community-recruited gamblers. On the G-BOAT, participants responded to a list of positive outcome phrases with the first two behaviours that came to mind. Those with more problematic gambling (as measured on the Problem Gambling Severity Index) and greater gambling involvement (as measured by time and money spent gambling on the Gambling Timeline Followback) responded to positive outcome phrases on the G-BOAT with more gambling-related responses. In Study 2, we administered G-BOAT to a community-recruited sample of 61 gamblers, who also completed a computerized reaction time measure of implicit gambling OEs, an explicit self-report measure of gambling OEs, and a measure of gambling frequency. Consistent with Strack and Deutch’s (2004) reflective-impulsive model, memory associations on the G-BOAT and positive OE scores on the explicit Gambling Expectancy Questionnaire each predicted unique variance in frequency of gambling behaviour. These studies are among the first to demonstrate the important role of memory associations in excessive and problem gambling.Les résultats escomptés (RE), c’est-à-dire la croyance dans les conséquences d’un comportement donné, constituent une importante variable explicative des comportements liés à la dépendance. L’étude 1 a évalué si des associations mémorielles entre le jeu et des résultats positifs sont reliées aux problèmes de jeu compulsif. La tâche d’association de résultats découlant de comportements liés au jeu (Gambling Behaviour Outcome Association Task [G-BOAT]) a été administrée à un échantillon de 96 joueurs recrutés au sein de la collectivité. Dans le cadre de la G-BOAT, une liste de locutions exprimant un résultat positif était présentée aux participants et ceux-ci devaient répondre en indiquant pour chacune des locutions les deux premiers comportements qui leur venaient à l’esprit. Ceux qui présentaient un problème de jeu plus grave (selon l’indice de jeu problématique) et qui s’adonnaient davantage au jeu (selon le suivi du temps passé à jouer et de l’argent dépensé effectué à l’aide de l’outil Gambling Timeline Followback) ont donné des réponses liées au jeu plus fréquemment que les autres. Dans le cadre de l’étude 2, la G-BOAT a été administrée à un échantillon de 61 joueurs recrutés au sein de la collectivité. Ceux-ci ont en outre fait l’objet d’une mesure informatisée du temps de réponse (TR) pour les RE liés au jeu implicites, d’une autoévaluation des RE liés au jeu explicites et d’une mesure de la fréquence des comportements liés au jeu. Conformément au modèle de réflexion et impulsion de Strack et Deutch (2004), les associations mémorielles obtenues dans le cadre de la G-BOAT et les résultats relatifs aux RE positifs obtenus dans le cadre du questionnaire sur les attentes quant au jeu ont dans les deux cas permis de prévoir une variance unique concernant la fréquence des comportements liés au jeu. Ces études fournissent ainsi un premier ensemble de données probantes relativement à l’importance des associations mémorielles dans l’apparition des problèmes de jeu compulsif.
... measured indirectly with reaction-time tasks) and explicit (i.e. measured with self-report questionnaires) memory associations between gambling and positive outcomes showed that positive implicit memory associations were associated with greater gambling involvement and more gambling-related problems, and uniquely predicted gambling behaviour above and beyond explicit outcome expectancies [20][21][22]. ...
... 95% CI mean difference = (À27. 16,20.58), t (25) = À0.28, ...
... The first result suggests that, for moderate-to high-risk gamblers, gambling cues are not only attention-grabbing [13][14][15][16][17][18][19] and triggers of positive memory associations [20][21][22], but also elicit automatically a motor response of approach towards them. Thus far, approach tendencies assessed with the AAT have been found for different addictions [26,[28][29][30][31][32][33][34], suggesting common dysregulated cognitive motivational processes [5,6] and biased information processing of substance-related cues associated with the expected reward [7]. ...
Article
Background and aims: Similar to substance addictions, reward-related cognitive motivational processes, such as selective attention and positive memory biases, have been found in disordered gambling. Despite findings that individuals with substance use problems are biased to approach substance-related cues automatically, no study has yet focused on automatic approach tendencies for motivationally salient gambling cues in problem gamblers. We tested if moderate- to high-risk gamblers show a gambling approach bias and whether this bias was related prospectively to gambling behaviour and problems. Design: Cross-sectional assessment study evaluating the concurrent and longitudinal correlates of gambling approach bias in moderate- to high-risk gamblers compared with non-problem gamblers. Setting: Online study throughout the Netherlands. Participants: Twenty-six non-treatment-seeking moderate- to high-risk gamblers and 26 non-problem gamblers community-recruited via the internet. Measurements: Two online assessment sessions 6 months apart, including self-report measures of gambling problems and behaviour (frequency, duration and expenditure) and the gambling approach avoidance task, with stimuli tailored to individual gambling habits. Findings: Relative to non-problem gamblers, moderate- to high-risk gamblers revealed a stronger approach bias towards gambling-related stimuli than neutral stimuli (P = 0.03). Gambling approach bias was correlated positively with past-month gambling expenditure at baseline (P = 0.03) and with monthly frequency of gambling at follow-up (P = 0.02). In multiple hierarchical regressions, baseline gambling approach bias predicted monthly frequency positively (P = 0.03) and total duration of gambling episodes (P = 0.01) 6 months later, but not gambling problems or expenditure. Conclusions: In the Netherlands, relative to non-problem gamblers, moderate- to high-risk gamblers appear to have a stronger tendency to approach rather than to avoid gambling-related pictures compared with neutral ones. This gambling approach bias is associated concurrently with past-month gambling expenditure and duration of gambling and has been found to predict persistence in gambling behaviour over time.
... Previous research has found that exposure to gambling-relevant cues activated gamblers' outcome expectancies, as indicated on both explicit (self-report) and implicit (RT) tasks. M. J. Stewart, Yi, and Stewart (2014) found that exposure to gambling cues, in the form of watching a five-minute casino video, activated both implicit and explicit positive gambling outcome expectancies among regular gamblers immediately after watching the video. Gamblers who watched the casino video self-reported significantly stronger positive gambling outcome expectancies on Gillespie et al. 's (2007a) Gambling Expectancy Questionnaire (GEQ) than gamblers in a control video condition and also showed activation of positive outcome expectancies on the Affective Priming Task, whereas those in the control video condition did not. ...
... A confederate played on one of the EGMs to provide sounds and visuals reminiscent of those of a casino or EGM venue. This cue manipulation was used as it represented an arguably more ecologically valid cueing scenario that regular gamblers might encounter in everyday life relative to the videos used in the original M. J. Stewart et al. (2014) study. ...
... Recently there has been a movement toward improving the external-validity of labbased research findings, and previous reports have highlighted the merits of testing in environments that closely resemble real-world settings (Spooner & Pachana, 2006). This is particularly salient in the present study because replicating the findings of M. J. Stewart Stewart et al. (2014) it could have significant clinical implications. Cue exposure therapy is often used in addiction treatment to extinguish the conditioned craving responses that are elicited by cues from the environment (e.g. ...
Article
There is a consensus in the addictions literature that exposure to addiction-relevant cues can precipitate a desire to engage, or actual engagement, in the addictive behaviour. Previous work has shown that exposure to gambling-relevant cues activates gamblers’ positive gambling outcome expectancies (i.e. their beliefs about the positive results of gambling). The current study examined the effects of a new, arguably more ecologically valid cue manipulation (i.e. exposure to a gambling lab environment vs. sterile lab environment) on 61 regular gamblers’ explicit and implicit gambling outcome expectancies. The authors first tested the internal consistency of their implicit reaction time measure of gambling outcome expectancies, the Affective Priming Task. Split-half reliabilities were satisfactory to high (.72 to .88), highlighting an advantage of this task over other characteristically unreliable implicit cognitive measures. Unexpectedly, no predicted between-lab condition differences emerged on most measures of interest, suggesting that peripheral environmental cues that are not the focus of deliberate attentional allocation may not activate positive outcome expectancies. However, there was some evidence that implicit negative gambling outcome expectancies were activated in the gambling lab environment. This latter finding holds clinical relevance as it suggests that presenting peripheral gambling-related cues while treating problem gamblers may facilitate processing of the negative consequences of gambling.
... Positive OEs have a larger impact on hazardous alcohol use than do negative OEs and have a causal role in predicting drinking (i.e., Zamboanga, Horton, Leitkowski, & Wang, 2006). Such findings regarding the importance of OEs in the substance abuse field have led researchers to suggest that OEs may also be important in understanding gambling behaviour (Stewart, Yi, & Stewart, 2014), given the similarities between substance abuse and problem gambling as addictive behaviours (American Psychiatric Association, 2013;Potenza, 2006). ...
... An advantage to the use of the G-BOAT is that it is time efficient, cost-effective, and easily administered and it scored in comparison to some existing implicit RT measures of gambling OEs (Stacy et al., 1994). Use of various implicit measures in conjunction with explicit tasks allows for assessment of both reflective and impulsive systems with respect to gambling behaviour (Stewart et al., 2014), a focus of Study 2. ...
... Comparable implicit affective tasks have been applied in the gambling literature, whereby positive or neutral, or negative or neutral, attribute words were intermixed with gambling pictures and participants were required to rapidly identify the type of word (Brevers et al., 2013). In the current study, we used a version of Fazio et al.'s task, modified by Stewart et al. (2014), to measure RT latencies to respond to gambling-relevant outcomes preceded by gambling and non-gambling pictures. Although both negative and positive OEs were presented, the current study's analyses focused on RTs to categorize positive OEs, as these were the type of OEs examined in the G-BOAT. ...
Article
Outcome expectancies (OEs), or beliefs about the consequences of engaging in a particular behaviour, are important predictors of addictive behaviours. In Study 1 of the present work, we assessed whether memory associations between gambling and positive outcomes are related to excessive and problem gambling. The Gambling Behaviour Outcome Association Task (G-BOAT) was administered to a sample of 96 community-recruited gamblers. On the G-BOAT, participants responded to a list of positive outcome phrases with the first two behaviours that came to mind. Those with more problematic gambling (as measured on the Problem Gambling Severity Index) and greater gambling involvement (as measured by time and money spent gambling on the Gambling Timeline Followback) responded to positive outcome phrases on the G-BOAT with more gambling-related responses. In Study 2, we administered G-BOAT to a community-recruited sample of 61 gamblers, who also completed a computerized reaction time measure of implicit gambling OEs, an explicit self-report measure of gambling OEs, and a measure of gambling frequency. Consistent with Strack and Deutch’s (2004) reflective-impulsive model, memory associations on the G-BOAT and positive OE scores on the explicit Gambling Expectancy Questionnaire each predicted unique variance in frequency of gambling behaviour. These studies are among the first to demonstrate the important role of memory associations in excessive and problem gambling. © 2016, Centre for Addiction and Mental Health. All rights reserved.
... In order to facilitate research in this area, Stewart and colleagues recently conducted a set of studies (Stewart, Yi, Ellery, & Stewart, under review;Stewart, Yi, & Stewart, 2014) that examined the impact of gambling cue exposure on the activation of implicit and explicit gambling outcome expectancies. Consistent with previous research in the alcohol field (e.g. ...
... Participants Participants (N ¼ 58; 38 males and 20 females) for this investigation consisted of gamblers who were part of a larger study investigating the effect of gambling cue exposure on implicit and explicit gambling outcome expectancies (Stewart et al., 2014). Participants ranged in age from 19 to 61 years (M ¼ 29.97, SD ¼ 12.04). ...
... The GEQ served as the direct measure of positive gambling outcome expectancies in the present study. Similar to previous research in this area (Stewart et al., 2014), the 3 positive subscales of the GEQ were combined in the present study in order to obtain a 15-item global measure of participants' positive gambling outcome expectancies. Participants were asked to what extent they expect each item/positive outcome (e.g. ...
Article
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Outcome expectancies are the positive or negative effects that individuals anticipate may occur from engaging in a given behaviour. Although explicit outcome expectancies have been found to play an important role in gambling, research has yet to assess the role of implicit outcome expectancies in gambling. In two studies, we investigated whether implicit and explicit positive gambling outcome expectancies were independent predictors of gambling behaviour (i.e. amount of time spent and money risked gambling; Study 1) and problem gambling severity (Study 2). In both studies, implicit positive gambling outcome expectancies were assessed by having regular gamblers (N ¼ 58 in Study 1; N ¼ 96 in Study 2) complete a gambling outcome expectancy reaction time (RT) task. A self-report measure of positive gambling outcome expectancies was used to assess participants’ explicit positive gambling outcome expectancies. Both the RT task and self-report measure of positive gambling outcome expectancies significantly contributed unique as well as shared variance in the prediction of self-reported gambling behaviour (Study 1) and problem gambling severity (Study 2). Findings from the current research point to the importance of using both direct and indirect assessment modes when examining the role of outcome expectancies in gambling.
... For example, implicit positive attitudes have been found to predict escalation of alcohol consumption [120], to correlate with nicotine dependence and predict relapse in smoking [121] and to predict food-choice [122]. In gambling, implicit positive attitudes seem to be associated with greater gambling involvement and more gambling-related problems, and uniquely predict gambling behavior above and beyond explicit outcome expectancies [123][124][125][126] and to be a hallmark of problem gamblers [127]. ...
... Given emerging work on the utility of implicit association type tasks in the gambling research area (e.g., [123,125]), these early results highlight the importance of using culturally-appropriate stimuli in implicit cognition studies. An appropriate selection of stimuli representing common gambling activities in the local participants' context would also allow for a more refined matching of relevant stimuli to the individual gambling preferences. ...
... For example, implicit positive attitudes have been found to predict escalation of alcohol consumption [120], to correlate with nicotine dependence and predict relapse in smoking [121] and to predict food-choice [122]. In gambling, implicit positive attitudes seem to be associated with greater gambling involvement and more gambling-related problems, and uniquely predict gambling behavior above and beyond explicit outcome expectancies [123][124][125][126] and to be a hallmark of problem gamblers [127]. ...
... Given emerging work on the utility of implicit association type tasks in the gambling research area (e.g., [123,125]), these early results highlight the importance of using culturally-appropriate stimuli in implicit cognition studies. An appropriate selection of stimuli representing common gambling activities in the local participants' context would also allow for a more refined matching of relevant stimuli to the individual gambling preferences. ...
Chapter
Excessive gambling behavior is a complex psychopathological phenomenon, characterized by the interaction of multiple etiological factors and by a very heterogeneous symptomatological expression. To date, there are no existing evidence-based “best practice” treatment standards for gambling disorder. Healthcare providers and clinicians are further challenged by the difficulty in reaching out to individuals suffering from gambling problems. Despite a surge of empirical studies on various therapeutic approaches addressing disordered gambling, there is an urgent need for the development of suitable and cost-effective helping tools. This chapter presents a narrative overview of recent advances in the development of and research on innovative treatment approaches and treatment modalities for gambling problems, ranging from training interventions based on addiction models, such as Cognitive Bias Modification and general cognitive training programs; neuromodulation techniques, and employment of modern digital technology to promote large-scale support services and overcome treatment barriers, to personalization of existing interventions to individual and culture-based characteristics and preferences, and integration of multiple methods. Each section of this chapter presents existing preliminary evidence for such novel treatment approaches in the domain of disordered gambling and, when not available, results in the broader field of addictive behaviors. Altogether, these novel venues of research on gambling interventions share the goal of enhancing therapeutic effects and overcoming barriers and limitations to existing treatment programs by meeting the heterogeneous needs and demands of this peculiar clinical population.
... The quantities E and S + are parameters and V is an auxiliary variable. In our dynamical system approach we have the possibility of including a stochastic input representing cues which may trigger an addiction [13][14][15][16]. ...
... The type of addiction determines the variable A: for the intake of food and drugs the amount over one week is used. For addictive activities such as gambling [16], the frequency or duration over one week can be taken. We assume that A is a linear function of the addiction vulnerability V: ...
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This study deals with addictive acts that exhibit a stable pattern not intervening with the normal routine of daily life. Nevertheless, in the long term such behaviour may result in health damage. Alcohol consumption is an example of such addictive habit. The aim is to describe the process of addiction as a dynamical system in the way this is done in the natural and technological sciences. The dynamics of the addictive behaviour is described by a mathematical model consisting of two coupled difference equations. They determine the change in time of two state variables, craving and self-control. The model equations contain terms that represent external forces such as societal rules, peer influences and cues. The latter are formulated as events that are Poisson distributed in time. With the model it is shown how a person can get addicted when changing lifestyle. Although craving is the dominant variable in the process of addiction, the moment of getting dependent is clearly marked by a switch in a variable that fits the definition of addiction vulnerability in the literature. Furthermore, the way chance affects a therapeutic addiction intervention is analysed by carrying out a Monte Carlo simulation. Essential in the dynamical model is a nonlinear component which determines the configuration of the two stable states of the system: being dependent or not dependent. Under identical external conditions both may be stable (hysteresis). With the dynamical systems approach possible switches between the two states are explored (repeated relapses).
... In detail, they correspond to such a thought: "After engaging in one behaviour, I expect X (Kuntsche et al. 2010). In the gambling field, OEs refer to the anticipated positive/negative outcomes that occur from one's gambling behaviour (Stewart et al. 2005;Stewart et al. 2014). Research shows that positive OEs (e.g., Emond et al. 2010;Michalczuk et al. 2011;Teeters et al. 2015) and negative OEs (e.g., St-Pierre et al. 2014;Wickwire et al. 2010;Wohl et al. 2006) have a role in predicting problem gambling in young people. ...
Article
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The Gambling Expectancy Questionnaire (GEQ; Gillespie et al. 2007a) is a 23-item scale assessing three positive outcome expectancies (Enjoyment/Arousal, Money, Self-Enhancement) and two negative outcome expectancies (Over-Involvement, Emotional Impact) related to gambling. It is the most used instrument to assess gambling outcome expectancies in adolescents and it has good psychometric properties. To allow a greater and more useful application of the scale, the present study aimed to modify the GEQ to make it usable with all adolescents, regardless of their gambling behaviour and to verify its psychometric properties. To that aim, the items were modified and the response scale was reduced from a seven-point to a five-point Likert scale. To verify the adequacy of the modified scale, two studies were conducted among Italian adolescents. In the first study (n = 501, 75% males, Mage = 16.74, SD = .88), after having removed four items and relocating another through explorative factor analysis, the original five-factor structure of the scale was confirmed by applying a confirmatory factor analysis. Reliability and validity evidence were also provided. The second study (n = 1894, 61% males, Mage = 15.68, SD = .71) attested its invariance across gambling behaviour status and gender. The modified version of the GEQ (GEQ – MOD) can be profitably used for research and preventive purposes with youth.
Article
The current study examined the relationship between gambling motives and gambling in various social contexts using both retrospective and real-time assessment of gambling social context. Ninety-five young adults (79 males, 16 females; aged 19–24 years) who reported gambling at least 4 times in the past month participated. Scores on the Gambling Motives Questionnaire (GMQ; Stewart & Zack, 2008) were used as a measure of gambling motives (Enhancement, Social, Coping). Data on the social context of gambling (alone, with family, with friends, with strangers) were derived retrospectively from the Gambling Timeline Follow-Back (G-TLFB; Weinstock, Whelan, & Meyers, 2004) as well as in real time using experience sampling (ES) methods (Conner Christensen, Feldman Barrett, Bliss-Moreau, Lebo, & Kaschub, 2003). For both the G-TLFB and ES data, we conducted a series of multivariate regression analyses with the block of gambling motives predicting gambling behaviour in each social context. Across the two assessment methods, coping gambling motives positively predicted gambling alone, whereas social gambling motives negatively predicted gambling alone and positively predicted gambling with friends. These findings suggest that individuals who gamble for particular motives are more likely to do so in specific social contexts.
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Substantial correlational evidence supports a causal (mediational) interpretation of alcohol expectancy operation, but definitive support requires a true experimental test. Thus, moderately to heavily drinking male college students were randomly assigned to 1 of 3 conditions in a pre–post design: Expectancy challenge (designed to manipulate expectancy levels), “traditional” information, and assessment-only control. Expectancy challenge produced significant drinking decreases, compared with the other 2 groups. Decreases in measured expectancies paralleled drinking decreases in the challenge condition. Significant increases in alcohol knowledge in the traditional program were not associated with decreased drinking. These experimental findings support a causal (mediational) interpretation of expectancy operation. The implications for a cognitive (memory) model of expectancies and for prevention and intervention programs for problem drinking and alcoholism are discussed.
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The primary goal of the present article is to compare expectancy models with competing attitude models of alcohol use. First, several methodological issues in expectancy research were addressed, to more adequately compare the theoretical models. Study 1 examined the effect of possible self-report biases on associations among expectancy constructs and alcohol use. In Studies 2 and 3, the basic distinction between general factors of positive and negative alcohol expectancies was investigated in both cross-sectional and prospective models. Alternative predictions that were based on competing expectancy and attitude theories were evaluated primarily in Study 3. Results from these studies supported the validity of the expectancy constructs and the proposed distinctions among expectancy and attitude constructs—in terms of strong discriminant validity, absence of self-report bias, and differential prediction of alcohol use. Furthermore, the findings favored certain expectancy models over alternative attitude models of alcohol use, reaffirming the usefulness of the expectancy framework.
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We hypothesized that attitudes characterized by a strong association between the attitude object and an evaluation of that object are capable of being activated from memory automatically upon mere presentation of the attitude object. We used a priming procedure to examine the extent to which the mere presentation of an attitude object would facilitate the latency with which subjects could indicate whether a subsequently presented target adjective had a positive or a negative connotation. Across three experiments, facilitation was observed on trials involving evaluatively congruent primes (attitude objects) and targets, provided that the attitude object possessed a strong evaluative association. In Experiments 1 and 2, preexperimentally strong and weak associations were identified via a measurement procedure. In Experiment 3, the strength of the object-evaluation association was manipulated. The results indicated that attitudes can be automatically activated and that the strength of the objectevaluation association determines the likelihood of such automatic activation. The implications of these findings for a variety of issues regarding attitudes—including their functional value, stability, effects on later behavior, and measurement—are discussed.
Chapter
I argue that implicit measures are measurement outcomes that have certain functional properties. The expression "indirect measure," however, refers to an objective property of the measurement procedure, being that the researcher does not assess the attitude on the basis of a self-assessment by the participant but on the basis of another behavior. With regard to the question of why one should use implicit measures, research suggests that they do not allow one to register stable structures in memory. It is also doubtful that they provide an index of implicit attitudes. But to the extent that implicit measures reflect the automatic impact of attitudes and cognitions, they could provide a unique insight into the effects of automatic processing on real-life behavior.
Book
For the first time, research on implicit cognitive processes relevant for the understanding of addictive behaviors and their prevention or treatment is brought together in one volume! The Handbook of Implicit Cognition and Addiction features the work of an internationally renowned group of contributing North American and European authors who draw together developments in basic research on implicit cognition with recent developments in addiction research. Editors Reinout W. Wiers and Alan W. Stacy examine recent findings from a variety of disciplines including basic memory and experimental psychology, experimental psychopathology, emotion, and neurosciences.
Chapter
Explanations of goal-directed behavior increasingly have highlighted the role of anticipatory processes, especially anticipation of reward. Because many researchers in both neurobiological and psychological domains often use the term "expectancy" to refer to these processes, we review the expectancy construct as a device for unifying explanation at these different levels of analysis. Appreciation of this role is essential for advancing expectancy assessment. To this end, we show how expectancies can be assessed using implicit (indirect) tasks. These studies have indicated that the content and organization of implicitly measured expectancies differ as a function of an individual's exposure to alcohol information, customary drinking level, and context, and that expectancies can directly influence drinking.