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Predicting gambling behaviour and problems from implicit and explicit positive gambling outcome expectancies in regular gamblers

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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.
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International Gambling Studies
ISSN: 1445-9795 (Print) 1479-4276 (Online) Journal homepage: http://www.tandfonline.com/loi/rigs20
Predicting gambling behaviour and problems from
implicit and explicit positive gambling outcome
expectancies in regular gamblers
Melissa J. Stewart, Sherry H. Stewart, Sunghwan Yi & Michael Ellery
To cite this article: Melissa J. Stewart, Sherry H. Stewart, Sunghwan Yi & Michael Ellery (2015)
Predicting gambling behaviour and problems from implicit and explicit positive gambling
outcome expectancies in regular gamblers, International Gambling Studies, 15:1, 124-140, DOI:
10.1080/14459795.2014.1000357
To link to this article: http://dx.doi.org/10.1080/14459795.2014.1000357
Published online: 09 Feb 2015.
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Predicting gambling behaviour and problems from implicit and
explicit positive gambling outcome expectancies in regular gamblers
Melissa J. Stewart
a
*, Sherry H. Stewart
a,b,c
, Sunghwan Yi
d
and Michael Ellery
e
a
Department of Psychology & Neuroscience, Dalhousie University, Halifax, Canada;
b
Department
of Psychiatry, Dalhousie University, Halifax, Canada;
c
Department of Community Health and
Epidemiology, Dalhousie University, Halifax, Canada;
d
Department of Marketing & Consumer
Studies, University of Guelph, Guelph, Canada;
e
Addictions Foundation of Manitoba, Winnipeg,
Canada
(Received 27 August 2014; accepted 15 December 2014)
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 (N¼58 in Study 1; N¼96 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.
Keywords: gambling; addictive behaviour; problem gambling; cognition; addiction
Outcome expectancies involve the perceived positive or negative effects that individuals
anticipate may occur from engaging in a given behaviour. Over the past 25 years, an
abundance of research has highlighted the role of outcome expectancies in addictive
behaviours, such as alcohol use (Goldman, Darkes, & Del Boca, 1999; Sayette, 1999).
Indeed, alcohol outcome expectancies have been found to be associated with increased
alcohol consumption and alcohol-related problems (Goldman et al., 1999). In light of the
theoretical significance of outcome expectancies in the alcohol field, along with the
similarities between alcohol use and gambling as addictive behaviours (American
Psychiatric Association, 2013; Potenza, 2006), it is natural to postulate that outcome
expectancies may also be relevant in the gambling domain. However, researchers have
only recently turned their attention towards investigating the role of outcome expectancies
in gambling. Similar to research on alcohol outcome expectancies, the emerging gambling
literature has found that gambling outcome expectancies are associated with increased
levels of gambling behaviour and problems (e.g. Gillespie, Derevensky, & Gupta, 2007b;
q2015 Taylor & Francis
*Corresponding author. Email: stewart.melissa@dal.ca
International Gambling Studies, 2015
Vol. 15, No. 1, 124–140, http://dx.doi.org/10.1080/14459795.2014.1000357
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Shead & Hodgins, 2009; St-Pierre, Temcheff, Gupta, Derevensky, & Paskus, 2014;
Wickwire, Whelan, & Meyers, 2010).
Although previous research on gambling outcome expectancies has been helpful in
elucidating the role of outcome expectancies in gambling, such research has primarily
relied on direct modes of assessment (e.g. self-report questionnaires). ‘Direct’ or ‘explicit’
measures refer to a class of measurement procedures that tap into cognitions and
behaviours thought to be deliberate and controlled, and those that involve conscious
engagement (De Houwer, 2006). Despite significant research advances made using direct
modes of assessment, as well as the importance of assessing outcome expectancies via
direct measures, its limitations have been increasingly recognized in the alcohol outcome
expectancy literature (e.g. Palfai & Ostafin, 2003). For example, it is unlikely that a
specific episode of drinking is the result of a deliberate and conscious consideration of the
expected outcomes of drinking the construct reflected in self-report measures of alcohol
outcome expectancies.
Influenced by cognitive psychology, addiction researchers have increasingly adopted
the view that outcome expectancies are represented in the associative memory network
(Goldman et al., 1999; Stacy, 1997). In accordance with this view, gambling outcome
expectancies are operationalized as the speed with which the concept of gambling (or
exposure to gambling cues) activates a given outcome expectancy in an individual’s
memory. For example, individuals who have a very strong positive outcome expectancy of
gambling should experience an increased activation of the positive outcome expectancy
when exposed to gambling to gambling activities or gambling cues compared to with weak
positive gambling outcome expectancies. In order to assess individual differences in the
strength of outcome expectancies, addiction researchers have employed ‘indirect’ or
‘implicit’ measures, such as reaction time (RT) tasks, in conjunction with direct measures
(Kramer & Goldman, 2003; Wiers et al., 2002). In contrast to direct measures, indirect
measures are thought to assess attitudes and cognitions in an automatic manner, are said to
be unconscious and involuntary in nature and appear to influence individuals’ memory
without explicit recall or introspection (see De Houwer, 2006).
Consistent with the reflective-impulsive model of social behaviour (Strack & Deutsch,
2004), both direct (e.g. self-report) and indirect (e.g. RT tasks) measures can be considered
complementary in assessing outcome expectancies in that self-report measures assess
deliberative determinants of behaviour, while RT measures are said to assess automatic
determinants of behaviour (see Wiers & Stacy, 2006). As direct and indirect measures
have been found to tap into different facets of outcome expectancies in the alcohol field
(e.g. de Jong, Wiers, van de Braak, & Huijding, 2007; Kramer & Goldman, 2003), and
provide unique contributions to the prediction of alcohol use behaviours in the alcohol
outcome expectancy area (Wiers et al., 2002), it may be similarly important to utilize both
assessment modes when examining the role of outcome expectancies in gambling.
However, to date, research has yet to assess the utility of direct and indirect measures of
gambling outcome expectancies in independently predicting gambling behaviour and
associated problems.
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. Palfai & Ostafin, 2003; Wall, Hinson, McKee, & Goldstein, 2001), results of
these studies revealed an activation of positive but not negative gambling outcome
expectancies following exposure to gambling cues. Similar to research in the alcohol field
International Gambling Studies 125
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(e.g. Jones, Corbin, & Fromme, 2001), these results suggest that negative outcome
expectancies reflect outcomes less proximal to gambling than positive outcome
expectancies. In light of such findings, it appears important to assess whether implicit
and explicit positive gambling outcome expectancies are capable of predicting gambling
behaviours and gambling problems, as has been previously shown in the alcohol outcome
expectancy area (e.g. Kramer & Goldman, 2003; Wiers et al., 2002).
Therefore, the purpose of the current set of studies was to investigate whether implicit
and explicit positive gambling outcome expectancies independently predicted two
important indices of gambling behaviour (i.e. amount of time spent and money risked
gambling; Study 1), as well as problem gambling severity (Study 2). Specifically, the
current research assessed the incremental contributions of direct (i.e. explicit) and indirect
(i.e. implicit) measures of positive gambling outcome expectancies in the prediction of
self-reported gambling behaviour and problem gambling severity. Overall, it was
predicted that the direct and indirect measures of positive gambling outcome expectancies
would predict unique as well as shared variance in the amount of time spent and money
risked gambling (Study 1), and problem gambling severity (Study 2).
Study 1
Method
Participants
Participants (N¼58; 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 (M¼29.97, SD ¼12.04). 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. No significant
differences were found between the two sites on any key demographic or outcome
variables.
To qualify for participation, individuals had to meet the following inclusion criteria:
(a) 19 years of age or older, (b) gambled at a casino or online at least three times over the
past two months, and (c) reported English as their native language (given that RT measures
require extremely rapid responses to English words). Individuals were excluded if they
were currently attempting to quit gambling or engaging in treatment for problem
gambling.
Using the Problem Gambling Severity Index (PGSI) from the Canadian Problem
Gambling Index (CPGI; Ferris & Wynne, 2001), participants consisted of 3 non-problem
gamblers, 8 low-risk gamblers, 32 moderate-risk gamblers and 15 high-risk/problem gamblers.
Total scores on the PGSI ranged from 0 to 25 (M¼6.26; SD ¼5.20). In terms of
gambling behaviour (as measured by the Gambling Timeline Followback [G-TLFB;
Weinstock, Whelan, & Meyers, 2004]), participants reported spending between 1.92 and
204.00 hours (M¼35.84, SD ¼39.07) gambling over the three months prior to
participating in the study. The amount of money participants risked over the three months
prior to completing the study ranged from $42 to $3690 (M¼$991.41, SD ¼$916.09).
Measures
Problem gambling symptoms. The nine-item PGSI scale of the CPGI (Ferris & Wynne,
2001) was used to assess the presence and severity of gambling problems among
126 M.J. Stewart et al.
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participants for sample description purposes. The PGSI contains five items that assess
problem gambling behaviour (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 with which they had engaged in the behaviour or
experienced the given consequence in the last 12 months using a 4-point Likert scale
ranging from 0 (never)to3(almost always).
Self-reported gambling behaviour. The Gambling Timeline Followback (G-TLFB;
Weinstock et al., 2004) was used to obtain a self-report measure of the amount of time
participants spent gambling, as well as the amount of money risked gambling (i.e. the
amount of money wagered gambling) over the three months prior to taking part in the
study, which were used as outcome measures in the current study. The G-TLFB is a
behavioural assessment instrument that consists of an individually administered
retrospective calendar (covering the past three months) that collects information on
gambling frequency, duration, type of game played, intent, risk, win loss, and number of
standard alcoholic drinks consumed while gambling.
Self-reported gambling outcome expectancies. The 3 positive subscales (enjoyment/
arousal, self-enhancement and money) of the 23-item Gambling Expectancy
Questionnaire (GEQ; Gillespie, Derevensky, & Gupta, 2007a) were used to assess self-
reported positive gambling outcome expectancies. 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. ‘I win money’; ‘I feel excited’) will occur when
gambling on a 7-point Likert scale ranging from 1 (no chance)to7(certain to happen).
In the current sample, this 15-item measure demonstrated good reliability (
a
¼.83).
Gambling outcome expectancy RT task. Adapted from the classic affective priming task
(Fazio, Sanbonmatsu, Powell, & Kardes, 1986), this RT-based task was used to assess
implicit gambling outcome expectancies. The task was designed to measure how quickly
individuals respond to positive and negative gambling outcome expectancy words (i.e.
targets) immediately after being primed by gambling versus control (i.e. track and field)
pictures. 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, as well as synonyms
of words from these measures. In total, there were 10 positive outcome expectancy words
(e.g. ‘excitement’, ‘relaxation’) and 10 negative outcome expectancy words (e.g. ‘anxiety’
‘guilt’) used as targets. In addition, 10 gambling-related and 10 non-gambling-related
pictures were used as primes. The task began with 1 block of 4 practice trials, and 2 blocks
with 20 test trials each (i.e. total number of trials was 44). The stimuli for practice trials
were different from those presented during the test trials. During the task, each outcome
expectancy target word was presented twice: once preceded by a gambling prime picture,
and once preceded by a non-gambling prime picture. The order of primes and targets
within each block was counterbalanced across participants.
International Gambling Studies 127
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Each trial started with the 200 ms presentation of either a gambling-related or non-
gambling-related (i.e. track and field) picture in the centre of the screen. This was
immediately 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. ‘enjoyment’) 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
had a positive connotation by clicking the ‘/’ key (which was reversed for half of
participants). The length of the inter-trial interval was 1000 ms. 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 were
practice trials.
Procedure
As the current investigation was part of a larger study (Stewart et al., 2014), only those
methodological details germane to the present study are described, as the protocol for the
larger study is available elsewhere. Upon arrival at the laboratory, participants provided
informed consent. The G-TLFB (Weinstock et al., 2004) was then administered in order to
obtain a measure of participants’ self-reported gambling behaviour over the past three
months. Participants then engaged in some tasks germane to the larger study (Stewart
et al., 2014) and completed a demographic questionnaire. Following this, more tasks
pertaining to the larger study were completed. Participants then engaged in the RT task
followed by completion of the GEQ (Gillespie et al., 2007a). Participants were then
debriefed and compensated $30.
1
Results
Prior to testing our hypotheses, a log transformation was performed on the RT data to
reduce the characteristic positive skewness of RT latencies and normalize the distribution
(see Fazio, 1990; Greenwald, McGhee, & Schwartz, 1998). Also, following the
recommended procedures for correcting extremely slow and fast responses (Greenwald
et al., 1998), values below 300 ms were recoded to 300 ms and those above 3000ms were
recoded to 3000 ms. The same procedures were employed in Study 2. Following this,
correlation analyses (see Table 1) were conducted on the variables of interest, which
included the amount of time spent and money risked gambling, participants’ self-reported
Table 1. Correlations between explicit and implicit positive gambling outcome expectancies and
measures of gambling behaviour (Study 1).
1234
1. Time spent gambling
2. Money risked gambling .73**
3. Positive outcome expectancy RT .34** .32*
4. Positive GEQ .42** .34** .23
Note: Time spent gambling and money risked gambling was assessed by the Gambling Timeline Followback
(G-TLFB; Weinstock et al., 2004). The positive outcome expectancy RT was computed by subtracting the log-
transformed RTs to trials involving gambling primes from the log-transformed reaction times involving non-
gambling primes. Positive GEQ ¼scores on the positive subscale of the Gambling Expectancy Questionnaire
(Gillespie et al., 2007a).
*p,.05 ** p,.01.
128 M.J. Stewart et al.
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positive gambling outcome expectancies, and the RT difference to positive expectancy
targets following gambling primes versus non-gambling primes. This RT difference score
was computed by subtracting the mean log-transformed RT to trials involving gambling
primes from the mean log-transformed RT to trials involving non-gambling primes.
As such, a higher positive value represented a faster RT to positive targets when they
followed gambling primes (i.e. greater implicit positive gambling outcome expectancies).
As displayed in Table 1, both the self-report and RT task measure of positive gambling
outcome expectancies were moderately positively correlated with the amount of time
spent and money risked gambling. In contrast, the self-report and RT measure of positive
gambling outcome expectancies were not significantly correlated.
To test our prediction regarding the incremental contributions of the RT measure and
self-report measure of positive gambling outcome expectancies in the prediction of
gambling behaviour, a set of hierarchical regressions were conducted. Beginning with the
amount of time spent gambling, two hierarchical multiple regression analyses were
conducted to assess the unique and shared contributions of the self-report and RT measure
of positive gambling outcome expectancies to the prediction of this measure of gambling
behaviour (see Table 2). In both cases, participants’ self-reported amount of time spent
gambling over the three months prior to taking part in the study (as assessed by the G-
TLFB) served as the criterion variable.
In Model 1, the self-report measure of positive gambling outcome expectancies was
entered into the initial step of the regression and the RT measure of positive gambling
outcome expectancies was entered as an additional predictor in the second step. In Model
2, the RT measure of positive gambling outcome expectancies was entered into the first
step of the regression and the self-report measure of positive gambling outcome
expectancies was entered as an additional predictor in the second step of the regression.
In Model 1, after controlling for the self-report measure of positive gambling outcome
expectancies in Step 2 of the regression, the RT measure of positive gambling outcome
expectancies remained a significant predictor of the amount of time spent gambling,
DR
2
¼.062, p¼.04. In Model 2, after controlling for the RT measure in Step 2 of the
regression, the self-report measure of positive gambling outcome expectancies remained a
significant predictor of the amount of time spent gambling, DR
2
¼.124, p¼.004.
Table 2. Self-report and RT measures of positive gambling outcome expectancies as predictors of
the amount of time spent gambling (Study 1).
bDFpDR
2
Cumulative R
2
Model 1
Step 1:
Self-report GOE
a
.420 12.005 .001 .177** .177
Step 2:
RT measure of GOE .257 4.508 .038 .062* .239
Model 2
Step 1:
RT measure of GOE .339 7.293 .009 .115** .115
Step 2:
Self-report GOE .257 8.938 .004 .124** .239
Shared Variance
b
.053
*p,.05 ** p,.01.
a
GOE ¼gambling outcome expectancies.
b
This shared variance was calculated by taking the total proportion of explained variance and subtracting the
unique variance explained by the RT measure and the unique variance explained by the self-report measure.
International Gambling Studies 129
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Together, these models accounted for 23.9% of the total variance in the amount of time
spent gambling, R
2
¼.239, p¼.001. Of this explained variance in the amount of time
spent gambling, 26% was contributed uniquely by the RT measure of positive gambling
outcome expectancies, 52% was contributed uniquely by the self-report measure of
positive gambling outcome expectancies, and the remaining 22% was contributed by what
the RT and self-report measure of positive gambling outcome expectancies held in
common (see Figure 1).
Two hierarchical regression analyses were then conducted to assess the distinct and shared
contributions of the RT and self-report measures of positive gambling outcome expectancies
to the prediction of the amount of money risked gambling (see Table 3). In both cases,
participants’ self-reported amount of money risked gambling over the three months prior to
taking part in the study (as assessed by the G-TLFB) served as the criterion variable. Aside
from the difference in criterion variable, the analyses were performed as above.
In Model 1, after controlling for the self-report measure of positive gambling outcome
expectancies in Step 2 of the regression, the RT measure of positive gambling outcome
remained a significant predictor of the amount of money risked gambling, DR
2
¼.062,
p¼.04. In Model 2, after controlling for the RT measure in Step 2 of the regression, the
self-report measure of positive gambling outcome expectancies remained a significant
predictor of the amount of money risked gambling, DR
2
¼.077, p¼.03. Together, these
models accounted for 18.0% of the total variance in the amount of money risked gambling,
R
2
¼.18, p¼.004. Of this explained variance in the amount of money risked gambling,
34% was contributed uniquely by the RT measure of positive gambling outcome
expectancies, 43% was contributed uniquely by the self-report measure of positive
gambling outcome expectancies, and the remaining 23% was contributed by what the RT
and self-report measure of positive gambling outcome expectancies held in common (see
Figure 1). Thus, as hypothesized, results revealed that the self-report and RT measure of
positive gambling outcome expectancies each predicted unique as well as shared variance
in the prediction of the amount of time spent and money risked gambling.
Time Spent
Gambling
(Study 1)
Proportion (%) of Variance Contributed
Money
Risked
Gambling
(Study 1)
Stud
y
1 and 2 Outcome Measures
Problem
Gambling
Severity
(Study 2)
0
5
10
15
20
25 Unique Variance: RT task
Unique Variance: Self-report
Shared Variance: RT & Self-report
Figure 1. Proportion (%) of unique and shared variance contributed by the direct (self-report) and
indirect (RT task) measures of positive gambling outcome expectancies in the prediction of the
amount of time spent and money risked gambling (Study 1) and problem gambling severity (Study 2).
130 M.J. Stewart et al.
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Study 2
Results of Study 1 point to the utility of the direct and indirect measures of positive
gambling outcome expectancies in significantly predicting unique as well as shared
variance in two important indices of gambling behaviour the amount of time spent and
money risked gambling. While such findings are important, it was also desirable to
examine the relations of positive gambling outcome expectancies with problem gambling
severity. However, this proved difficult in Study 1 due to the limited range of gambling
problems in the sample (i.e. only 19% of the sample scored in the non-problem or low-risk
range on the PGSI; Ferris & Wynne, 2001). To address this limitation and allow for an
examination of the utility of the direct and indirect measures of positive gambling outcome
expectancies in predicting problem gambling severity, we purposely recruited a new
sample of participants that exhibited a broader range of gambling problems (i.e. 40% of
the sample scored in the non-problem or low-risk range on the PGSI; Ferris & Wynne,
2001) in Study 2. Thus, Study 1 and Study 2 were independent studies.
Method
Participants
Participants (N¼96; 66 males and 30 females) for the present investigation consisted of
gamblers who were part of a larger study that investigated the impact gambling
advertisement exposure on implicit and explicit gambling outcome expectancies (Stewart
et al., under review). Participants ranged in age from 19 to 71 years (M¼29.63;
SD ¼12.08). Participants were recruited through posters placed in local universities, and
advertisements in local newspapers and classified websites. Fifty participants were
recruited from the Halifax Regional Municipality in Nova Scotia, 22 participants from the
greater Guelph area in Ontario, and 24 participants from the Winnipeg area in Manitoba.
The three sites did not significantly differ on any key demographic or outcome variables.
In order to be eligible to participate, individuals had to be 19 years of age or older and had
to have gambled at a casino or played any casino games outside a casino, gambled on a slot
machine or video lottery terminal, bet on horses at a racetrack or played dice games for
Table 3. Self-report and RT measures of positive gambling outcome expectancies as predictors of
the amount of money risked gambling (Study 1).
bDFpDR
2
Cumulative R
2
Model 1
Step 1:
Self-report GOE
a
.344 7.499 .008 .118** .118
Step 2:
RT measure of GOE .256 4.153 .040 .062* .180
Model 2
Step 1:
RT measure of GOE .321 6.433 .014 .103* .103
Step 2:
Self-report GOE .285 5.163 .027 .077** .180
Shared Variance
b
.041
*p,.05 ** p,.01.
a
GOE ¼gambling outcome expectancies
b
This shared variance was calculated by taking the total proportion of explained variance and subtracting the
unique variance explained by the RT measure and the unique variance explained by the self-report measure.
International Gambling Studies 131
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money at least once over the past three 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. Using the PGSI from the CPGI
(Ferris & Wynne, 2001), participants consisted of 11 non-problem gamblers, 27 low-risk
gamblers, 39 moderate-risk gamblers and 19 high-risk/problem gamblers. Total scores on
the PGSI ranged from 0 to 21 (M¼4.73; SD ¼4.57).
2
Measures
The measures used to assess implicit (gambling outcome expectancy RT task) and explicit
(GEQ; Gillespie et al., 2007a) positive gambling outcome expectancies and problem
gambling severity (PGSI; Ferris & Wynne, 2001) were identical to those used in Study 1
and are not reiterated here. Both the GEQ (
a
¼.86) and PGSI (
a
¼.87) demonstrated
good internal consistency in the present sample.
Procedure
As mentioned earlier, this current investigation was part of a larger study (Stewart et al.,
under review). Only those methodological details germane to the present study are
described here as the protocol for the larger study is available elsewhere. Upon arrival at
the laboratory, participants provided informed consent. Participants then engaged in tasks
relevant to the larger study and completed a demographic questionnaire. Following their
involvement in some other tasks relevant to the larger study (Stewart et al., under review),
participants engaged in the RT task and then completed the GEQ (Gillespie et al., 2007a),
after which they were debriefed and compensated $30.
3
Results
Correlation analyses were conducted to assess the relation between problem gambling
severity, participants’ self-reported positive gambling outcome expectancies on the GEQ
(Gillespie et al., 2007a) and the RT difference to positive expectancy targets following
gambling primes vs. non-gambling primes. As in Study 1, this RT difference score was
computed by subtracting the mean log-transformed RT to trials involving gambling primes
from the mean log-transformed RT to trials involving non-gambling primes. Consistent
with predictions, both the self-report (r(94) ¼.34, p,.001) and RT measure of positive
gambling outcome expectancies (r(94) ¼.26, p¼.006) were positively associated with
level of problem gambling severity. In addition, as in Study 1, the self-report and RT
measures of positive gambling outcome expectancies were not significantly correlated
(r(94) ¼.04, p¼.37).
Given the significant relationship between problem gambling severity and both the
implicit and explicit measures of positive gambling outcome expectancies, it was of
interest to examine the incremental contributions of the RT measure and self-report
measure of positive gambling outcome expectancies in predicting problem gambling
severity. To do so, the same analytic strategy employed in Study 1 was performed.
Specifically, two hierarchical multiple regression analyses were conducted to assess the
unique and shared contributions of the self-report and RT measure of positive gambling
outcome expectancies to the prediction of problem gambling severity (see Table 4).
132 M.J. Stewart et al.
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In both cases, participants’ level of problem gambling severity (as assessed by the PGSI of
the CPGI [Ferris & Wynne, 2001]) served as the criterion variable.
In Model 1, after controlling for the self-report measure of positive gambling outcome
expectancies in Step 2 of the regression, the RT measure of positive gambling outcome
remained a significant predictor of problem gambling severity, DR
2
¼.059, p¼.01.
In Model 2, after controlling for the RT measure in Step 2 of the regression, the self-report
measure of positive gambling outcome expectancies remained a significant predictor of
problem gambling severity, DR
2
¼.110, p¼.001. Together, these models accounted for
17.5% of the total variance in problem gambling severity, R
2
¼.175, p,.001. Of this
explained variance in problem gambling severity, 34% was contributed uniquely by the
RT measure of positive gambling outcome expectancies, 63% was contributed uniquely
by the self-report measure of positive gambling outcome expectancies, and the remaining
3% was contributed by what the RT and self-report measure of positive gambling outcome
expectancies held in common (see Figure 1). Thus, as hypothesized, results revealed that
the self-report and RT measure of positive gambling outcome expectancies each predicted
unique as well as shared variance in the prediction of problem gambling severity.
Discussion
The current research investigated whether positive gambling outcome expectancies,
assessed using both direct and indirect measures, independently predicted the amount
of time spent and money risked gambling (Study 1), and problem gambling severity
(Study 2). Specifically, the present studies examined the incremental contributions of the
direct and indirect measures of positive gambling outcome expectancies in the prediction
of self-reported gambling behaviour and problems.
As predicted, results from Study 1 revealed that the self-report and RT measure of
positive gambling outcome expectancies each predicted unique as well as shared variance
in the amount of time spent and money risked gambling. Building upon previous research
highlighting the relation between explicit gambling outcome expectancies and
adolescents’ self-reported gambling behaviour (e.g. Wickwire et al., 2010), the present
findings suggest that implicit gambling outcome expectancies are also associated with
Table 4. Self-report and RT measures of positive gambling outcome expectancies as predictors of
problem gambling severity (Study 2).
bDFpDR
2
Cumulative R
2
Model 1
Step 1:
Self-report GOE
a
.340 12.292 .001 .116** .116
Step 2:
RT measure of GOE .243 6.654 .011 .059* .175
Model 2
Step 1:
RT measure of GOE .255 6.524 .012 .065* .065
Step 2:
Self-report GOE .332 12.373 .001 .110** .175
Shared Variance
b
.006
*p,.05 ** p,.01.
a
GOE ¼gambling outcome expectancies.
b
This shared variance was calculated by taking the total proportion of explained variance and subtracting the
unique variance explained by the RT measure and the unique variance explained by the self-report measure.
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self-reported gambling behaviour. In sum, our findings indicate that there is merit in
including not only self-report but also RT measures when investigating the role of
outcome expectancies on gambling behaviour. However, our findings should be
interpreted with caution since participants’ gambling behaviour was based solely on their
retrospective self-report (see below for further discussion of this potential limitation).
To increase our understanding of the mechanisms involved in the relationship between
outcome expectancies and gambling behaviour, it is important that future research
examine the impact of such expectancies on prospective gambling behaviour. Indeed,
given the cross-sectional design of the current research, as well as the fact that no studies
to date have established a causal role of gambling outcome expectancies on subsequent
gambling behaviour, an important first step in establishing this causal role would be to
examine the prospective prediction of gambling behaviour in a longitudinal design, similar
to research on alcohol outcome expectancies (e.g. Wiers et al., 2002). Moreover,
considering the current findings, it is important that future research make use of both
indirect and direct assessment modes when examining the role of outcome expectancies on
prospective gambling behaviour.
Similar to Study 1 findings, the results of Study 2 revealed that both the direct and
indirect measures of positive gambling outcome expectancies were significant predictors
of problem gambling severity, with both assessment modes predicting unique as well as
shared variance in problem gambling severity. Such findings build upon previous research
examining the relationship between self-reported gambling outcome expectancies and
gambling problems (e.g. Gillespie et al., 2007b; Shead & Hodgins, 2009) by revealing that
explicit positive gambling outcome expectancies are significant predictors of gambling
problems even after accounting for the effects of another predictor (i.e. implicit positive
gambling outcome expectancies). Along with Study 1 findings, these results highlight the
potential role of implicit cognitions in gambling behaviour and problems. Although
implicit positive outcome expectancies have been found to play an important role in other
addictive behaviours, (e.g. alcohol use; see Wiers & Stacy, 2006), to our knowledge the
current research is the first to highlight the potential utility of implicit positive gambling
outcome expectancies in the prediction of gambling behaviour and associated problems.
In both studies, results revealed that relatively more variation in the prediction of the
amount of time spent and money risked gambling (Study 1) and problem gambling
severity (Study 2) was due to unique rather than shared aspects of the direct and indirect
measures of positive gambling outcome expectancies. Thus, as previously shown in the
alcohol research area (e.g. Wiers et al., 2002), our results suggest that implicit and explicit
positive gambling outcome expectancies are independent and unique predictors of
gambling behaviour (Study 1) and problem gambling severity (Study 2). In line with the
reflective-impulsive model of behaviour (Strack & Deutsch, 2004), such findings suggest
that the RT task and self-report measure of gambling outcome expectancies are tapping
into different underlying constructs. Specifically, the self-report measure may assess
gamblers’ outcome expectancies after conscious deliberation, whereas the RT measure
may assess the automatic activation of participants’ gambling outcome expectancies in
memory by exposure to the gambling prime pictures. An examination of the relationship
between the direct and indirect measures of positive gambling outcome expectancies in
Study 1 and Study 2 provides further support for this claim. Consistent with findings from
the alcohol outcome expectancy literature (e.g. de Jong et al., 2007; Jajodia & Earleywine,
2003; Kramer & Goldman, 2003; Wiers, Van Woerden, Smulders, & De Jong, 2002), the
direct and indirect measures of positive gambling outcome expectancies were not
significantly correlated, suggesting that the underlying constructs assessed using these
134 M.J. Stewart et al.
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measures are independent. Alternatively, this non-significant correlation may be related to
motivational biases (e.g. social desirability) in self-reported gambling outcome
expectancies or a lack of introspective access to implicitly assessed constructs (Hofmann,
Gawronski, Gschwendner, Le, & Schmitt, 2005). To provide further insight into this
relationship, future research should investigate whether indirect measures of gambling
outcome expectancies assess a unique facet of the gambling cognition domain that is not
accessible via direct assessment modes.
Although the RT and self-report measures of positive gambling outcome expectancies
each provided a unique contribution to the prediction of the amount of time spent and
money risked gambling (Study 1), as well as problem gambling severity (Study 2), the
self-report measure contributed somewhat more variation in the prediction of gambling
outcomes. It is important to note, however, that the greater contribution of the self-report
measure may be due to common method variance bias, which involves the artificial
inflation of relationships between variables that are assessed using the same method (Reio,
2010). Specifically, both the questionnaires measuring gambling behaviour and problems
and those measuring positive gambling outcome expectancies were assessed using self-
report methods.
Limitations
Some limitations of the current research should be noted. First, the present studies are
correlational in nature, making it impossible to infer causality. Second, participants’
gambling behaviour in Study 1 was assessed via self-report, which is prone to social
desirability bias, acquiescence and extreme responding, and demand characteristics
(Paulus & Vazire, 2009). Further, measuring gambling behaviour via self-report relies on
the notion that gamblers are consciously aware of their gambling behaviour, and are
willing to accurately and truthfully report such behaviour to researchers (Birch et al.,
2008). In addition, there was significant variability in some of the outcome measures (i.e.
time spent and money risked gambling) and this should be taken into consideration when
interpreting the present findings. However, it should be noted that this measure of
gambling behaviour (i.e. G-TLFB; Weinstock et al., 2004) has been found to produce
reliable and valid indices of gambling behaviour (Hodgins & Makarchuk, 2003;
Weinstock et al., 2004). Nonetheless, it is important that future research examine whether
gambling outcome expectancies are capable of predicting subsequent gambling behaviour,
as has been found in the alcohol domain (e.g. Palfai & Ostafin, 2003; Wiers et al., 2002).
An additional limitation relates to the fact that a self-report measure of gambling
outcome expectancies designed for adolescent gamblers (i.e. GEQ; Gillespie et al., 2007a)
was used in the present research as there currently is no such measure specifically designed
for adults. Consequently, the GEQ (Gillespie et al., 2007a) may not be the most accurate
representation of the different outcome expectancies held by adult gamblers. That said,
however, the factor structures of other measures of adolescent gambling outcome
expectancies (i.e. the Adolescent Gambling Expectancies Survey; Wickwire et al., 2010)
have been successfully replicated in adult gamblers (Ginley et al., 2013), suggesting that
the outcome expectancies captured by these measures are relevant to both adult and
adolescent gamblers. Further, the current findings point to the relevance and validity of the
GEQ items among adults, as it was predictive of both gambling and problems.
Nonetheless, it is important that future research investigate the specific gambling outcome
expectancies held by adult gamblers and that priority be given to the development of a
self-report measure of adult gamblers’ outcome expectancies. A further limitation relates
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to the RT task used in both studies. Specifically, the present studies examined associations
between gambling and outcome expectancies relative to associations with a control
activity (i.e. track and field). Although we chose a control activity that we believed was
comparable to gambling, future research should examine whether similar results are found
using a different control activity. Finally, it is important to note that conclusions of the
current findings are based on the assumption that the RT measure assessed implicit
cognition. However, there is no direct evidence from the present findings that this is the
case. Despite this potential caveat, there is a broad literature base demonstrating that
implicit cognition can be reliably assessed via RT measures (see Stacy & Wiers, 2010).
Implications and directions for future research
The current research has a number of important research and clinical implications.
In relation to research implications, the present findings highlight the importance of
utilizing both direct and indirect measures when investigating the role of outcome
expectancies in gambling as both assessment modes were found to contribute unique
variance in the prediction of gambling behaviour and problems. Given the paucity of
research on gambling outcome expectancies in general, and implicit gambling outcome
expectancies more specifically, results of the present research provide an important
framework for future research examining the impact of implicit and explicit outcome
expectancies in gambling. In addition to assessing whether implicit and explicit positive
gambling outcome expectancies are capable of predicting gambling behaviour as
measured by behavioural observation in either a laboratory or natural environment (as
highlighted above), it is important that future research investigate whether direct and
indirect measures of positive gambling outcome expectancies predict cognitions and
behaviours associated with gambling problems (e.g. craving to gamble, gambling
motives). Similarly, it is important that future research examine potential mediators and
moderators of the relationship between implicit and explicit gambling outcome
expectancies and gambling behaviour, such as gambling abstinence self-efficacy
(Hodgins, Peden, & Makarchuk, 2004), problem gambling severity (Gillespie et al.,
2007b) and self-regulatory resources (Hofmann, Friese, & Strack, 2009). Given that the
current research examined global positive gambling outcome expectancies, it is important
that future research investigate the potential impact of specific positive gambling outcome
expectancies (e.g. winning, excitement) on gambling behaviour and problems among adult
gamblers. Such research may provide insight into whether certain types of positive
gambling outcome expectancies are more closely associated with increased gambling
behaviour and problems than others. It is also important that future research examine the
role of negative outcome expectancies on gambling behaviour and problems. Based on
findings from the alcohol field (e.g. Jones et al., 2001) and emerging gambling literature
(Stewart et al., 2014) suggesting that negative outcome expectancies play a less proximal
role in addictive behaviours, such expectancies may not be closely associated with
problematic gambling.
In relation to clinical implications, results of the present studies point to the potential
utility of targeting implicit gambling-related cognitions, in addition to explicit cognitions,
when designing prevention and treatment initiatives for disordered gambling. In the
alcohol field, support has been shown for the effectiveness of recently developed cognitive
retraining methods that alter implicit alcohol associations from positive to negative
(Houben, Havermans, & Wiers, 2010), with such strategies being associated with reduced
alcohol consumption and improved treatment outcomes (e.g. Wiers, Eberl, Rinck, Becker,
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& Lindenmeyer, 2011). Given the promise of cognitive retraining methods in the alcohol
field, these interventions may be effective in altering implicit positive gambling outcome
expectancies as a treatment for disordered gambling.
In relation to explicit gambling outcome expectancies, it may prove useful to focus on
expectancy challenges as a gambling prevention initiative and intervention for disordered
gambling. Indeed, expectancy challenges, which aim to reduce individuals’ expectancies
about the rewarding properties of an addictive behaviour, have been used to successfully
reduce positive explicit outcome expectancies in the alcohol area (Darkes & Goldman,
1993,1998). Future research in this area may wish to educate gamblers on the role of
outcome expectancies in gambling behaviour and problems, and include exercises in
which gamblers examine environmental factors and cues (e.g. gambling advertisements)
that contribute to the expectancies they hold regarding the positive effects of gambling.
Declaration of interests
Funding sources: This work was supported by a research grant from the Ontario Problem Gambling
Research Centre awarded to Sherry H. Stewart & Sunghwan Yi, as well as an operating grant from
the Manitoba Gambling Research Program (MGRP) of Manitoba Lotteries awarded to Sherry
H. Stewart, Michael Ellery & Sunghwan Yi (Grant #410-2009-1043). However, the findings and
conclusions of this paper are those solely of the authors and do not necessarily represent the views of
Manitoba Lotteries. Melissa J. Stewart was supported by doctoral scholarships from Social Sciences
and Humanities Research Council, Killam Trusts, and Gambling Awareness Nova Scotia.
Conflicts of interest: No potential conflict of interest was reported by the authors.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1. As part of the larger study (Stewart et al., 2014), participants were randomly assigned to one of
two cue exposure conditions: a casino cue video condition (n¼29) or a control cue video
condition (n¼29). Given that cue exposure did not moderate the raw effect of each measure on
the outcomes of interest, nor did it moderate the unique effect of each measure on the outcomes
of interest, we collapsed across cue conditions and only examined participants’ post-cue RT
scores and self-reported outcome expectancies when conducting the analyses.
2. A square root transformation was performed (Tabachnick & Fidell, 2007) to reduce the positive
skewness of the distribution of scores on the PGSI (Ferris & Wynne, 2001). As a comparison of
the analyses conducted using the original and transformed data revealed no differences in
results, the original, untransformed scores on this measure were retained.
3. As part of the larger study (Stewart et al., under review), participants were randomly assigned to
one of two cue exposure conditions: a gambling advertisement cue condition (n¼51) or a
control advertisement condition (n¼45). Given that cue exposure did not moderate the raw
effect of each measure on the outcomes of interest, nor did it moderate the unique effect of each
measure on the outcomes of interest, we collapsed across cue conditions and only examined
participants’ post-cue RT scores and self-reported outcome expectancies when conducting the
analyses.
Notes on contributors
Melissa J. Stewart is currently completing her PhD in Clinical Psychology at Dalhousie University in
Halifax, Nova Scotia. Her research interests include the role of implicit and explicit gambling-
related cognitions, with a particular focus on the role of implicit and explicit outcome expectancies
in gambling. They also include responsible gambling strategies aimed at reducing the risk of
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problematic slot machine gambling, as well as prevention and treatment initiatives for disordered
gambling.
Sherry H. Stewart is professor in the Departments of Psychology and Neuroscience, Psychiatry, and
Community Health and Epidemiology at Dalhousie University in Halifax, Nova Scotia. Her research
is focused on psychological factors (e.g. motives, personality, implicit cognitions) contributing to
alcohol abuse, disordered gambling, and the co-morbidity of mental health and addictive disorders.
She has published several clinical trials of novel approaches for the treatment and prevention of
addictive disorders and co-occurring mental health problems.
Sunghwan Yi is an associate professor in the Department of Marketing & Consumer Studies at the
University of Guelph. His research interests include the automatic determinants of addictive,
impulsive and compulsive consumer behaviour (e.g. excessive buying, gambling), as well as
affective motives of compulsive/excessive buying and gambling.
Michael Ellery is currently a clinical specialist with the Addictions Foundation of Manitoba. His
research in the area of gambling has included examining electronic gambling behaviours,
investigating psychometric properties of gambling measures and evaluating the effectiveness of
prevention and treatment initiatives for gambling problems.
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... The same G-TLFB was used in the current study to examine two dimensions of gambling: time spent gambling, in minutes, and money spent gambling, in dollars. These two indices were selected because previous gambling research has found positive OEs to correlate with time and money spent gambling (Stewart, Stewart, Yi, & Ellery, 2015). ...
... The five scales of the GEQ represent both positive (enjoyment/arousal, self-enhancement, and money) and negative (over-involvement and emotional impact) gambling OEs (Gillespie et al., 2007a). Each participant was given a total score for an overall higher order positive subscale of the GEQ, calculated by summing across the three positive subscales (see Stewart et al., 2015). This higher order subscale showed good internal reliability in the present study (a = .86). ...
... Only trials involving positive OE target words were included in the analyses, as we were interested in examining correlations with the G-BOAT, which assessed only positive OEs. The Affective Priming Task has been shown to have good validity and good internal consistency, with split-half reliabilities that are satisfactory to high (Hudson et al., 2016;Stewart et al., 2015). ...
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.
... In contrast, controlled cognitive processes are often measured using explicit self-report measures and reflect cognitions that exert selfregulatory influences on addictive behaviour (Stewart & Zack, 2008). Indeed, research has shown that both automatic and controlled cognitive processes have unique effects on addictive behaviour (Stacy & Wiers, 2010), including problem gambling (Brevers et al., 2013;Florez et al., 2016;Stewart, M. J., Stewart, S. H., Yi, & Ellery, 2015). ...
... For example, it has often been argued that impulsive processes should be most pivotal to spontaneous and risky drinking (e.g., Strack & Deutsch, 2004). This either/or approach has also been used frequently in the gambling literature (e.g., Brevers et al., 2013;Florez et al., 2016;Stewart, M. J. et al., 2015). However, emerging evidence suggests that it is erroneous to view addictive behaviour as guided simply by either impulsive or self-regulatory cognitions . ...
Article
Dual process models propose that behaviour is influenced by the interactive effect of impulsive (i.e., automatic or implicit) and self-regulatory (i.e., controlled or explicit) processes. Recently, evidence from the alcohol literature demonstrates that the impulse to engage in risky behaviour is mitigated by a high capacity to self-regulate. The current study aimed to extend this model to behavioural addictions, namely frequent gambling behaviour. It was hypothesized that impulsive processes favouring gambling (positive implicit gambling cognition) would predict frequent gambling, but only if the capacity to self-regulate was low. A treatment-seeking sample of 57 adults with problem gambling (Mage = 45.20 years, 54% men) completed two Single Category Implicit Association Tests, one reflecting tension-reduction, and the other enhancement, implicit gambling cognition. Participants also completed self-report measures of past week gambling frequency and the Gambling Abstinence Self-Efficacy Scale, which provided a measure of the self-regulatory capacity to abstain from gambling when emotionally aroused. Controlling for age and gender, consistent with hypotheses, implicit tension reduction gambling cognition positively predicted gambling frequency at low (p =.046) but not at high (p =.191) self-efficacy for gambling abstinence when feeling emotionally bad. However, self-efficacy for gambling abstinence when feeling emotionally good was not supported as a moderator of the effect of implicit enhancement gambling cognition on gambling frequency. Results suggest that the cognitions inherent in the impulsive process leading to frequent gambling are tension reduction or escape-related. Furthermore, emotionally relevant nuances to the ability to self-regulate gambling do exist; these nuances may contribute to both risk model specificity and interventions. © 2018, Centre for Addiction and Mental Health. All rights reserved.
... 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]. ...
... 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.
... This result was in line with previous research assessing gambling outcomes in predicting gambling behaviors (Stewart et al., 2015). ...
Article
Full-text available
Objectives We investigated the relationships between cultural worldviews, gambling risk perception, and gambling behavior with a sample of tourists in Macao. Methods Participants were enrolled at famous landmarks and casinos in Macao, China. Data were collected using several instruments to assess an individual's cultural worldviews, gambling risk perceptions, and gambling intentions. Results We found that the three‐dimensional solution was valid for the Chinese version of the gambling expectancy scale. Correlational and mediational analyses revealed that the relationship between an individualistic worldview and gambling intention was fully mediated by gambling risk perception. Respondents with an egalitarian worldview perceived greater risk associated with gambling than those with other worldviews. Conclusion These findings demonstrated the important influence of cultural variables on perceived risk and behavior in gambling. Moreover, understanding gamblers’ worldviews could be beneficial for problem gambling interventions. Future research directions and the limitations of the findings were discussed.
... 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]. ...
... This result was in line with previous research assessing gambling outcomes in predicting gambling behaviors (Stewart et al., 2015). ...
Conference Paper
We investigated the association between cultural worldviews and gaming risk perceptions and intention in a sample of 364 tourists in Macao. Scales assessing individual's worldviews, gaming risk perceptions and intentions were collected at the famous casinos and land mark in Macao. Correlational and mediation analysis revealed that respondents with egalitarian worldviews perceived greater risk associated with the gambling risks than other worldview holders. In addition, respondents who scored high on individualism were less likely to perceive the gambling risks. They are in turn more likely to score highly on gambling intention scale. There was a significant association between hierarchism with gambling reason, gambling is the personal hobby, whereas fatalists are more likely gambling for emotional reason. Overall, our results suggest cultural worldviews may influence tourists' judgement on gambling. Individualists are not the major group in a Chinese population, who are more likely to overlap the customers in gaming industry.
... 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]. ...
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.
... Notwithstanding their criticism, the original dual-process theories hold a strong heuristic value for the explanation of the "addiction paradox" and the assumption that automatic information processing is biased in problematic and pathological gambling 1 has been confirmed in numerous studies: gambling has been associated with attentional biases (Ciccarelli et al., 2016a(Ciccarelli et al., , 2016bHønsi et al., 2013;Hudson et al., 2016;Vizcaino et al., 2013) and these biases seem to be specific to the preferred gambling activity (McGrath et al., 2018). Additionally, implicit memory biases between gambling-related stimuli and positive attributes (Brevers et al., 2013) as well as positive outcome expectancies have been reported (Stewart et al., 2015;Stiles et al., 2016). In the latter two studies, implicit (positive) outcome expectancies predicted unique variance in the amount of time and money spent on gambling. ...
Article
There is evidence that training addicted participants to implicitly avoid disorder-related stimuli by using a training version of the Approach-Avoidance Task (AAT) results in reduced substance consumption (i.e., Approach Bias Modification [AppBM]). The aim of the present web-based study was to investigate the feasibility and effectiveness of AppBM in reducing gambling-related symptoms. A self-selected sample of participants with problem/pathological slot-machine gambling completed an online survey and received either AppBM or Sham training (final N = 131). Attrition during study participation was high (66%). In both conditions slot-machine related and neutral pictures were presented. Within the AppBM condition all slot-machine related pictures had to be pushed and all neutral pictures had to be pulled, whereas in the Sham condition the contingency was 50:50. Eight weeks after baseline, participants were re-assessed. Both groups showed a similar reduction in gambling-related symptoms. Findings are at odds with the hypothesis claiming that only contingency trainings yield beneficial effects. However, it cannot be ruled out that effects result from other factors unrelated to training such as expectancy effects. We think this study holds valuable information how to conduct larger trials in the future and may prove helpful to improve training and its delivery.
... Notwithstanding their criticism, the original dual-process theories hold a strong heuristic value for the explanation of the "addiction paradox" and the assumption that automatic information processing is biased in problematic and pathological gambling 1 has been confirmed in numerous studies: gambling has been associated with attentional biases (Ciccarelli et al., 2016a(Ciccarelli et al., , 2016bHønsi et al., 2013;Hudson et al., 2016;Vizcaino et al., 2013) and these biases seem to be specific to the preferred gambling activity (McGrath et al., 2018). Additionally, implicit memory biases between gambling-related stimuli and positive at tributes (Brevers et al., 2013) as well as positive outcome expectancies have been reported (Stewart et al., 2015;Stiles et al., 2016). In the latter two studies, implicit (positive) outcome expectancies predicted unique variance in the amount of time and money spent on gambling. ...
Article
Biases in information processing are attributed an important role in the maintenance of tobacco dependence. As these biases are not sufficiently taken into account in current treatments, the aim of the present study was to investigate whether clinical outcome can be improved by combining treatment-as-usual (TAU) with Approach-Avoidance Modification Training (AAMT). A two group parallel (1:1) randomized-controlled single-blind study with adult smokers (N = 105) was conducted (DRKS00011406). Participants received three sessions of TAU and either six sessions of AAMT or Sham training. During AAMT, participants were trained to implicitly avoid all smoking-related and to approach all smoking-unrelated pictures, while the contingency was 50:50 in Sham training. Participants were assessed after the intervention and 6 months later. Primary outcome was daily cigarette consumption at follow-up. Participants receiving TAU + AAMT did not show a significantly greater reduction of daily cigarette consumption at follow-up compared to TAU + Sham (per-protocol: 95% CI: -2.56–4.89, p =.608; intention-to-treat: 95% CI: -3.11–2.96, p =.968). Using an implicit AAMT (vs. Sham) as an add-on to TAU did not improve clinical outcome. However, no consistent evidence for a change of bias was found. It is important for future research to explore the effectiveness of optimized training versions (e.g., explicit instructions). Pre-registration: German Clinical Trials Register (DRKS00011406).
Article
Full-text available
Rapid, continuous gambling formats are associated with higher risks for gambling-related harm in terms of excessive monetary and time expenditure. The current study investigated the effect on gambling response latency and persistence, of a new form of within-game intervention that required players to actively engage in response inhibition via monitoring for stop signals. Seventy-four experienced electronic gaming machine gamblers, with a mean age of 35.28 years, were recruited to participate in a rapid, continuous gambling task where real money could be won and lost. Participants were randomly allocated to either the control condition where no intervention was presented, or either a condition with a passive three minute break in play or a condition with a three minute intervention that required participants to engage in response inhibition. Although there was no main effect for experimental condition on gambling persistence, both interventions were effective in elevating response latency during a period of sustained losses. It was concluded that within-game interventions that create an enforced break in play are effective in increasing response latency between bets during periods of sustained losses. Furthermore, within-game interventions that require active involvement appear to be more effective in increasing response latency than standard, passive breaks in play.
Article
Full-text available
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.
Article
Full-text available
Expectancy theory posits that decisions to engage in a given behavior are closely tied to expectations of the outcome of that behavior. Gambling outcome expectancies have predicted adolescent gambling and gambling problems. When high school students' outcome expectancies were measured by Wickwire et al. (Psychol Addict Behav 24(1):75-88 2010), the Adolescent Gambling Expectancy Survey (AGES) revealed five categories of expectancies that were each predictive of gambling frequency and pathology. The present study aimed to explore if the AGES could be successfully replicated with college students. When administered to a diverse college student population, factor analyses identified five factors similar to those found in the high school sample. Several factors of the AGES were also found to predict gambling frequency and gambling problems for college students. Gambling frequency and gambling activity preference were also addressed.
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.