Executive Functioning in Pathological Gamblers and Healthy Controls

Study Final Report Submitted to Ontario Problem Gambling Research Centre

Principal Investigator: David M. Ledgerwood, Ph.D.(1)

Co-Investigators/Collaborators: Leslie H. Lundahl, Ph.D.(1) G. Ron Frisch, Ph.D.(2) Nick Rupcich (3)

1. Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, 2761 E. Jefferson Ave., Detroit, MI, USA, 48207. 2. Department of Psychology, University of Windsor, 401 Sunset, Windsor, Ontario, N9B 3P4. 3. Problem Gambling Services, Windsor Regional Hospital, 2109 Ottawa St., Windsor, Ontario, N8Y 1R8.

Table of Contents

Page Number List of Tables 3 List of Figures 4 Acknowledgements 5 Abstract 6 Executive Summary 7 Introduction 9 Methods 12 Participants 12 Inclusion/Exclusion 12 Pathological Gamblers 12 Controls 12 Measures 13 Demographics, Gambling, Inclusion and Exclusion Measures 13 Impulsivity Measures 13 General Intelligence 13 Executive Function 14 Ethics Approval 15 Procedure 15 Sample Justification 16 Data Analysis 16 Results 18 Demographic and Gambling Variables 18 Executive Function 18 Decision Making 18 Response Inhibition 18 Memory 19 Impulsivity 19 Discussion 20 Executive Function 20 Decision Making 21 Impulsivity 22 Changes to the Original Proposal 22 Limitations & Strengths 22 Implications and Conclusions 23 References 25 Tables 28 Figures 34

List of Tables

Table 1. Demographic and gambling variables.

Table 2. Raw means and standard deviations on executive function measures.

Table 3. Correlations between executive function variables and intelligence.

Table 4. Raw means and standard deviations for Wechsler Memory Scale scores.

Table 5. Correlations between Delay Discounting Area Under the Curve (AUC), Barrett Impulsiveness Scale (BIS), executive function measures and full scale intelligence.

List of Figures

Figure 1. Flow of participants through the study.

Figure 2. Mean Iowa gambling task: (a) raw scores; and (b) T scores, for pathological gamblers (closed square) and controls (open diamonds).

Figure 3. Median GoStop scores for pathological gamblers (PGs) and controls at 50, 150, 250 and 350 msec time-points

Figure 4. Delay discounting subjective dollar amounts and delay periods for pathological gamblers (PGs) and controls.

Acknowledgements

We would like to thank the staff of the Windsor Regional Hospital Problem Gambling Services for their support and assistance in completing this project. We would also like to thank our graduate students and research staff members at Wayne State University for their assistance, particularly Emily Orr, Aleks Milosevic, Kristen Hodges, Ken Bates, Debra Kish, Joi Moore, Lisa Sulkowski and Dr. Caren Steinmiller. Additional thanks go to Dr. Donald Dougherty, who provided the GoStop task. We thank the Ontario Problem Gambling Research Centre for providing funding for this project through the Level III funding mechanism. Finally, we thank all of those who served as participants in this study.

Abstract

Pathological gambling (PG) is categorized as an impulse control disorder in DSM-IV, and research has consistently demonstrated that gambling problems are associated with higher levels of trait impulsivity. However, very little research has focused on identifying the underlying neuropsychological factors associated with impulsivity in pathological gamblers (PGs). In other clinical and non-clinical populations, research findings point to a relationship between trait impulsivity and executive functions (EF), which involve cognitive processes implicated in the formation of goal-directed behaviours and learning. The current lack of research on gamblers greatly limits our understanding of the role of EF deficits in the development and maintenance of PG. In this study, we examined the potential role of EF dysfunction in PG. Specifically, we compared PGs with non-problem gambling controls on several EF and impulsivity tasks. In total, 45 PGs and 45 controls were recruited and tested. PGs and controls were well matched on gender and age, and EF analyses were controlled for full-scale intelligence. EF tasks included measures of response inhibition, working memory, cognitive flexibility and perseveration, and planning. PGs differed from controls only on measures of planning, when the analysis was controlled for intelligence. PGs also experienced significantly more difficulty with decision-making as measured by the Iowa Gambling Task and scored significantly higher on two measures of impulsivity. EF and impulsivity were not correlated with each other. These findings provide evidence that, while PGs may exhibit substantial impulsivity relative to healthy controls, their potential EF deficits appear to be specifically related to processes associated with planning and decision-making.

Key Words: Gambling, Executive Function, Impulsivity, Pathological Gambler, Decision Making

Executive Summary

Pathological gamblers (PGs), as a group, are often characterized as being more impulsive than individuals who gamble without problems. Recent studies have begun to explore possible neurocognitive correlates impulsivity in PGs, but these studies are relatively few. One proposed area of study is executive functions (EFs), which are a set of neurocognitive processes that involve learning and the formation of goal-directed behaviours. Specific EFs include: 1) response inhibition (ability to respond to a stop signal after being presented with a ‘go’ signal); 2) working memory (temporary memory used to form complex cognitive and planning tasks); 3) cognitive flexibility and perseveration (ability to change one’s behaviour in the face of shifting rules); 4) planning behaviours based on environmental feedback; and, 5) decision-making to maximize positive consequences and minimize negative ones.

We examined these EF processes in the present study. We compared PGs (n = 45) to non- problem gambling control participants (n = 45) on several measures of EF and impulsivity. Our primary research questions were: Do PGs experience deficits in their EF compared with control participants who do not experience any gambling problems? Do PGs experience greater impulsivity than non-problem gambling controls, as has been demonstrated in several past studies? And, will EF deficits correlate with greater impulsivity among PGs and control participants? Participants completed several measures of gambling severity, EF, impulsivity and general cognitive functioning (intelligence and memory).

Our main findings were as follows:

• We were able to recruit PG and control groups that were similar on several demographic factors, such as age and gender. • Contrary to our predictions, PGs and controls differed significantly on full scale intelligence (IQ), and subsequent EF analyses statistically corrected for IQ. • As predicted, PGs scored higher than controls on all gambling severity measures. • Compared with control participants, PGs demonstrated greater deficits on two measures of planning and one measure of perseveration, but the perseveration differences were no longer significant when we controlled statistically for group differences in IQ. • PGs experienced greater difficulty on a measure of decision making. • No other measures of EF differed between the two groups. • PGs were more impulsive than controls on our two impulsivity measures (self-reported impulsivity and a delay discounting task). • Contrary to our hypothesis, EF was mostly uncorrelated with measures of impulsivity. The one exception was that one planning measure was significantly associated with self- reported impulsivity.

These findings have several implications for understanding pathological gambling (PG). First, PG appears to be associated with fairly specific deficits in EF. Planning and decision making measures accounted for the most variability between these two groups, and should be a focus of additional study. PGs were significantly more impulsive than controls, and self-reported impulsivity may be related to planning processes. Thus, new treatment approaches should 8 address potential deficits in planning and decision making, in addition to focusing on impulsivity.

Introduction

Pathological gambling (PG) is classified as an impulse control disorder in the DSM-IV- TR (American Psychiatric Association, 2000), and impulsivity is considered one of the most prominent features of PG. Characteristics of impulsivity include risk-taking, acting quickly without thinking, and failure to plan for the future. Many studies have shown that self-reported impulsivity is correlated with PG (e.g., Blaszczynski & Steel, 1998; Ledgerwood, Alessi, Phoenix, & Petry, 2009; Petry, 2001; Steel & Blaszczynski, 1998; Vitaro, Ferland, Jacques & Ladouceur, 1998), and some evidence indicates that self-reported impulsivity may be related to treatment failure (Leblond, Ladouceur & Blaszczynski, 2003).

Despite the importance and complexity of the relationship between impulsivity and PG, very few studies have addressed the neurocognitive deficits that potentially underlie impulsivity in pathological gamblers (PGs). That is, research has largely not investigated the extent to which identifiable neurocognitive dysfunctions may directly contribute to impulse control issues often seen among PGs. Impulsivity is thought to be associated with underlying functional deficits in particular areas of the brain (e.g., prefrontal cortex) related to executive function (EF) (Hinson, Jameson & Whitney, 2003). EF involves processes implicated in goal-directed behaviours (Lezak et al., 2004), such as planning and initiating behaviours, anticipating (positive and negative) consequences of actions, and the ability to adjust behaviours based on environmental feedback. Identifying potential EF dysfunction in PGs is essential not only because EF deficits frequently underlie impulsive behaviour, but also because EF deficits may interfere with an individual gamblers’ capacity to engage in and benefit from psychosocial treatments for PG.

A number of studies have explored the influence of EF on performance on gambling and risk taking tasks. Brand and colleagues (2005a) found that alcohol dependent patients with frontal lobe damage secondary to Korsakoff syndrome were more likely than age- and gender- matched control participants to make excessively risky choices on gambling tasks. Specifically, disadvantageous risk taking on gambling tasks was negatively associated with ability to categorize, engage in set-shifting and cognitive flexibility, although there was no association between risk taking and perseveration. Clark et al. (2003) compared the performance of individuals with left-side frontal lesions, individuals with right-side frontal lesions, and age- matched healthy controls on a number of gambling tasks thought to assess impulsive and risky decision-making. Both brain-lesion groups made poorer choices on the Iowa gambling task compared to the control group. Interestingly, right-lesion participants demonstrated the most impaired performance on the gambling tasks relative to the controls. Right frontal lesions also appear to be associated with impaired response inhibition (i.e., one’s ability to stop oneself from making an elicited response in the presence of a stop signal) on go-no go or stop signal tasks (Aron et al., 2003; Garavan, Ross & Stein, 1999; Konishi et al., 1999), and impaired decision making relative to healthy controls on gambling tasks (Clark et al., 2003). These results demonstrate that damage to certain frontal regions of the brain (e.g., prefrontal cortex, orbital frontal cortex) is associated with more risky and disadvantageous decision making during gambling tasks.

Only a few studies have examined EF and decision making in PGs. Goudriaan and colleagues (Goudriaan, Oosterlaan, de Beurs & van den Brink, 2006) found that PGs 10

demonstrated greater impairment on response inhibition, time estimation, cognitive flexibility and planning tasks compared with healthy controls. Consistent with the findings of Brand et al. (2005a), Goudriaan and colleagues did not report significant differences between PGs and controls on perseveration measures. Cavadini et al. (2002) compared PGs and healthy controls on the Iowa gambling task, Weigle’s sorting test and the Wisconsin card-sorting test. PGs made significantly more disadvantageous card choices on the Iowa gambling task, but did not differ from controls on the other two tasks. Thus, PGs may experience specific impairments in decision-making, but be relatively unimpaired when it comes to set-shifting measures such as the Wisconsin card-sorting task. However, it is important to note that PGs and controls differed significantly on several key demographic factors, including age (PGs were older (38.5 vs. 30.3 yrs)) and gender (PGs were more likely to be male (95% vs. 45%)). Brand et al. (2005b) further examined decision-making in PG by having PGs and controls complete a gambling task and EF assessment. Relative to controls, PGs exhibited significantly more disadvantageous risk taking, which in turn was correlated with categorization, cognitive flexibility and set-shifting measures of EF. The association between PG and response inhibition (ability to respond to a stop signal after receiving a go signal) is less clear. For example, we found that PGs and controls did not differ on response inhibition (Ledgerwood, Petry, Alessi and Phoenix, 2009). However, others (e.g., Goudriaan et al., 2006) have found significant deficits in response inhibition among PGs relative to control participants.

Recent fMRI and EEG studies have offered confirmatory evidence for the presence of elevated EF deficits in PGs. Potenza and colleagues (2003b), for example, compared brain scans of male PGs and healthy controls, and found that PGs displayed decreased activity in the frontal and orbitofrontal cortex, caudate/basal ganglia, and thalamus, areas of the brain that are important for EF. In a separate study, Potenza et al. (2003a) reported differences between PGs and controls ventromedial prefrontal cortex functioning during completion of a Stroop task, a measure that purports to measure cognitive flexibility. Regard and colleagues (Regard, Knoch, Gutling & Landis, 2003) found 65% of PGs had dysfunctional EEG activity, compared with 26% of control participants. Thus, areas of the brain implicated in EF appear to function differently in PGs versus controls.

The studies reviewed above, when taken together, suggest that PGs experience deficits in EF when compared to the functioning of non-PGs. Thus, PG may be associated with significant co-occurring neurological dysfunction in a large subset of PGs. The clinical implications of this possibility are that EF difficulties may hinder an individual’s ability to engage in and benefit from treatment for PG. For example, individuals with impaired planning ability and disinhibition might not benefit from treatment approaches that require patients to set primarily long-term goals, but they might benefit most from interventions designed to help them learn to anticipate consequences, both negative and positive, of more immediate behaviours.

These potential implications are not limited to a small number of individuals. Up to 2.6% of Ontario’s residents report gambling problems with approximately 1% experiencing severe gambling problems (Wiebe, Mun & Kauffman, 2006). Thus, over 330,000 Ontario residents experience problem gambling or PG. These findings underscore the need to understand the 11 aetiology of PG so that we may develop effective interventions. One promising area of investigation concerns the neuropsychological concomitants of impulsivity. Although impulsivity is frequently elevated in PGs, and EF processes are thought to underlie impulsivity, to date few controlled studies have investigated the relationship between impulsivity and EF. Thus, an important line of research in the study of PG is to determine the extent to which PGs evidence EF impairment that may both explain some of the causes of PG and be the focus of interventions.

The primary aim of the present study was to compare PGs and non-PGs on performance tests of EF and impulsivity. The EF measures focused on four primary cognitive areas: 1) Response inhibition; 2) working memory; 3) cognitive flexibility and perseveration; and 4) planning. Based on limited prior research, we predicted that PGs would evidence significant deficits on response inhibition, cognitive flexibility and planning measures relative to controls. Impulsivity measures included the Barratt Impulsiveness Scale, and a delayed discounting task (which assesses the tendency to select smaller immediate rewards over bigger, delayed ones). We predicted that PGs would demonstrate greater impulsivity on tasks involving rate of future discounting and planning. Further, we conducted secondary analyses comparing scores on EF and impulsivity tasks, and we predicted that scores indicative of EF impairment would correlate positively with indices suggesting greater impulsivity. We also examined other aspects of cognitive functioning, including intelligence and overall memory, that are not explicitly related to EF. We did not expect that PGs and controls would differ on these measures, but that they would serve to discriminate between EF and other cognitive functions.

Methods

Participants

Participants included pathological gamblers (PGs; n=45) and non-pathological gambling controls (n=45) recruited from the community of Windsor, Ontario. PGs and non-PGs were recruited using newspaper and/or radio advertising. We also recruited PGs from the Problem Gambling Services of Windsor Regional Hospital, the primary gambling outpatient and inpatient treatment facility in Windsor Ontario, and one of the busiest treatment facilities in Ontario. We attempted to obtain groups of gambling and control participants that were similar on factors such as age and gender to control for their effects on neuropsychological measures. Finally, because females have been disproportionately underrepresented in much of the past research on PGs, we made an effort to recruit roughly equal numbers of men and women. The flow of participants into the study is presented in Figure 1.

Inclusion/Exclusion

Pathological gamblers all met current DSM-IV diagnostic criteria for PG as assessed by the NORC DSM Screen for Gambling Problems (NODS, described below); all were 18 years or older; all were English speaking; and all were able to provide informed consent. Participants were excluded if they had current severe psychiatric disorders (i.e., current acute suicidality, uncontrolled mania or uncontrolled psychosis). They were also excluded if they provided a positive toxicology screen for any substance (including cocaine, opioids, marijuana and alcohol), or were currently dependent on any substance (e.g., alcohol, cocaine, opiates, marijuana, etc.), with the exception of nicotine or caffeine. Current substance abstinence was confirmed using urine toxicology and breath screening measures for cocaine, opioids, alcohol and marijuana using Ezscreen test sticks (Editek, Burlington, NC) and Alcosensor IV Alcometer (Intoximeters, St. Louis, MO; results > 0.003 ng/dl was considered positive). The only exception was when participants could document appropriate medical use of a prescribed medication that resulted in a positive test (controls and PGs did not differ, p = .37). Participants with a history of head injury involving loss of consciousness were not excluded, given a recent study that found over 80% of gamblers recruited had experienced a head injury (Regard et al., 2003). Rates of head injury did not differ significantly between the two groups (p = .46).

We considered recruiting participants who commonly participated in one specific type of gambling (e.g., slots or video lottery terminal only). However, we determined that this would limit the generalizability of our findings to other types of gamblers and yield only a narrow perspective on gambling and EF. Therefore, to maintain strong external validity, we did not restrict recruitment in terms of specific gambling type. We also considered excluding PGs with other axis I DSM-IV diagnoses (e.g., depression or anxiety disorders), however, we felt that this, too, would adversely affect the external validity of the study because a majority of PGs also have co-occurring disorders (Petry, Stinson & Brant, 2005).

Control participants met the same inclusion and exclusion criteria as PGs, except that control participants were required to obtain lifetime and/or past year NODS scores of no more than 1. 13

Measures

Demographics, Gambling, Inclusion and Exclusion Measures

A demographic form and the Canadian Problem Gambling Index (CPGI; Ferris & Wynne, 2001) were used to collect basic demographic information (e.g., age, gender, race, employment etc.), and gambling history. The CPGI is a valid and reliable measure of gambling behaviour and symptoms (Ferris & Wynne, 2001), and was used to characterize problem gambling behaviours.

The NORC DSM Screen for Gambling Problems (NODS) was used to diagnose PG. The NODS is based on DSM-IV criteria for PG, and assesses lifetime and current PG (American Psychiatric Association, 2000; Gerstein et al., 1999). The NODS has been found to be a valid and reliable diagnostic measure of PG (Hodgins, 2004).

Sections of the Structured Clinical Interview for the DSM-IV (SCID; First, Spitzer, Gibbon & Williams, 1997) were used to assess exclusion criteria related to drug dependence and severe psychiatric difficulties, and to assess co-occurring psychiatric disorders.

Impulsivity Measures Barratt’s Impulsiveness Scale is a 34-item self-report measure with good reliability and validity (Barrett, 1985; Patton, Stanford & Barratt, 1995). Higher scores represent greater impulsivity. The Barratt scale has been used in past research on PG (e.g., Martins et al., 2004) and was used in the current study to examine impulsivity difference between PGs and controls, as well as associations between impulsivity and EF deficits.

Delayed Discounting of Monetary Rewards task is identical to the one developed by Petry and Casarella (1999), and assesses the participant’s ability to accept larger delayed reinforcement over smaller but immediate reinforcement. Participants choose between a hypothetical dollar amount delivered immediately, versus $1000 delivered after a specified delay. The amount of delay varies with each presentation, ranging from 1 day to 25 years. The dependent measure is area under the curve (AUC), a composite score of the point at which the participant switches from selecting the delayed reinforcement to selecting the immediate reinforcement. Greater impulsivity is indicated when participants discount greater delayed rewards in favour of smaller immediate rewards at higher rates (lower AUC). The delayed discounting task was used in the current study to examine differences in impulsivity between PGs and controls, and to examine correlations between AUC and EF deficits.

General Intelligence

Wechsler Abbreviated Scale of Intelligence (WASI) is a valid and reliable brief assessment of intellectual functioning (The Psychological Corporation, 1999). The WASI was used to assess full scale IQ, and took approximately 30 minutes to administer. The four sub-tests of the WASI include Matrix Reasoning (a measure of fluid abilities), Block Design (a measure of visuomotor skills), Vocabulary and Similarities.

Executive Function

Wechsler Memory Scale, 3rd Edition (WMS-III) was developed to assess learning, memory and working memory (The Psychological Corporation, 1997). It consists of eight primary memory indexes, including Auditory Immediate, Visual Immediate, Immediate Memory, Auditory Delayed, Visual Delayed, Auditory Reception Delayed, General Memory, and Working Memory. Working memory involves the temporary storage of information in short- term memory for the purposes of performing complex cognitive and planning tasks, and it is most associated with EF. Other scales served as general memory measures to discriminate EF from other cognitive processes. Higher scores indicate better memory function.

GoStop Impulsivity Paradigm (GoStop) measures response inhibition (Dougherty, 2003). Participants were presented with a series of 5-digit numbers on a computer screen, and were instructed to click the mouse when presented with the ‘go’ stimulus, and refrain from clicking the mouse when presented with a subsequent ‘stop’ signal. Go signals are 5-digit numbers that are identical to the preceding number and are presented in black font. Stop signals are numbers that are identical to the preceding number but change from black to red font at latency intervals of 50msec, 150msec, 250msec and 350msec. Half of the responses are novel numbers that are not identical to the ones that preceded them. In this study, we used percentage of inhibited responses (proportion of correctly inhibited responses to the number of stop signals presented) as a measure of response inhibition.

Wisconsin Card Sorting Test (WCST). The WCST assesses abstraction and the ability to shift or maintain cognitive set (Berg, 1948; Milner, 1963). An inability to shift cognitive set in the face of new information is indicative of perseveration. Participants were asked to sort computerized cards by rules that are not known to the participant (e.g., sort by shape or colour or number). The computer provided feedback after each response. Following 10 consecutive correct responses, the computer changed the response rules without notifying the participant, and the participant had to shift his/her strategy to adjust to the new rules. Responses were recorded, and scoring involved calculation of several variables including number correct, number of errors, number of perseverative errors, and others. The WCST has been used to assess perseveration and set-shifting in several populations including PGs (Goudriaan et al., 2006). In this study, we used two scales, number of categories completed and percent perseverative responses, as measures of set shifting. Greater EF dysfunction is associated with completing fewer categories and having a greater proportion of perseverative errors.

Stroop Test. The Stroop is a measure of cognitive flexibility that assesses ability to suppress a habitual response to perform a novel one (Stroop, 1935). During the test, participants were first asked to read colour names printed in black ink on a form. In the next step, they were asked to read colour names of ‘X’s printed in coloured ink. Finally, participants were asked to name the colour in which the colour names are printed, disregarding the printed verbal word, which was the name of a different colour. Greater difficulty adapting to rule changes during this task is associated with EF dysfunction, and reflects an interference effect. The dependent measure used in the current study was score on the Stroop Interference scale. Lower scores are indicative of greater EF dysfunction. 15

Controlled Oral Word Association Test (COWAT). The COWAT is a measure of verbal fluency. Participants are given one minute each to list as many words as possible that begin with the letters F, A and S. Proper names and multiple words beginning with the same word stem (e.g., fond and fondly) are not allowed. The dependent measures are the total number of words listed (fluency), and the total number of rule breaks (i.e., repeated words or use of proper names). Greater EF dysfunction is associated with lower total scores and higher total rule break scores. Verbal fluency measures assess spontaneous cognitive flexibility, or one’s ability to generate novel responses to a particular problem (Strauss, Sherman & Spreen, 1998).

Tower of London (TOL). The TOL is a planning test in which the participant must move a set of coloured balls inserted on pegs from a start position to a predetermined end position, one ball at a time, using the fewest possible number of moves (Shallice, 1982). The TOL test measures the participant’s ability to identify and organize the steps involved in realizing a complex goal. In the current study, the total number of moves needed to complete the task, and the number of rule violations (e.g., moving two beads at once or other incorrect moves), were included as dependent measures. More total moves and rule violations are associated with greater EF dysfunction.

Iowa Gambling Task (IGT). The IGT is a computer-based measure of decision-making in which participants are given a hypothetical amount of money to play, and must choose between four decks of cards (labelled A, B, C and D) that are presented on the computer screen (Bechara et al., 1997). Decks A and B are associated with higher (hypothetical) monetary rewards, but also associated with higher punishment (money lost) than decks C and D. Overall, decks A and B result in losses, while decks C and D result in gains. The gains and losses associated with each card turn are not predictable. Risky decision-making is associated with more selection from decks A and B than decks C and D. The task ends after 100 cards are selected. This task has been used to assess decision-making in multiple studies, including studies of PGs (e.g., Goudriaan et al., 2005).

Ethics Approval

This study protocol was reviewed and approved by the Wayne State University Human Investigation Committee (HIC), prior to its initiation. The HIC is the primary Institutional Review Board for WSU. The study protocol was also reviewed and approved by the Windsor Regional Hospital Research Ethics Board (REB), which is the umbrella REB for Problem Gambling Services.

Procedure

Participants found out about the study through newspaper advertising, posted fliers in community centres or other settings, or through their clinician (in the case of participants recruited from treatment). Interested participants called a toll free number and were screened over the telephone to determine eligibility. The number of participants included and excluded at the telephone screen and later in the process are included in Figure 1. 16

Psychological testing occurred at the Windsor Regional Hospital Problem Gambling Services in Windsor, Ontario. Participants provided written informed consent. The interviewer collected and tested urine and breath samples to detect the presence of drugs or alcohol. An intake interview followed including screening and self-report measures. The participant completed all tasks in a sound resistant room that included a desk, chair and laptop computer with a 14” monitor. All tasks were administered in the same order. Testing took approximately 7 hours, on average. After completion of the tasks, the research assistant answered any questions the participant had and provide payment. Participants received $100 for study participation, plus $20 for transportation and lunch. If a participant was ineligible during the initial consent or interview portion of the study day, he/she was informed and given $20 to compensate for his/her time.

Sample Justification

Proposed sample sizes were based on statistical power analysis (Cohen, 1988). We used a two tailed test, a Type I error rate of α= .05 and a Type II error rate of β = .20, resulting in a power, [1-β] = .80. Goudriaan and colleagues (2006) found medium to large effect sizes (d > .60) on measures of response inhibition, time estimation, cognitive flexibility and planning, comparing PGs to controls. In developing our sample size estimates, we assumed a medium effect size (d = .60) and a required power of >0.80. According to this analysis, 45 patients should be included in each group (Cohen, 1988; G*Power Version 3).

Data Analysis

Initial data analysis involved assessing differences between PGs and controls on demographic and gambling variables, using t-test and chi-square statistics as appropriate. For all analyses, PG and control groups were compared globally (rather than using a match control approach).

A Multivariate analysis of variance (MANOVA) was conducted to examine gambling group differences (PGs vs. controls) on the on EF variables (WMS letter number sequencing, Stroop Interference, COWAT total correct, COWAT rule breaks, TOL total moves, TOL rule breaks, WCST perseverative responses and WCST categories). Variables were log or square root transformed as appropriate prior to analysis. After conducting the MANOVA, we ran the analysis again including full-scale intelligence (IQ) as a covariate because gambler groups differed on IQ. Intercorrelations among the EF variables and full scale IQ were calculated.

IGS and GoStop task data were analyzed separately from the other EF data, because these variables are more amenable to within subject analysis of variance (ANOVA). The within subject variable in the analysis of the IGS was groups of five successive response blocks, each consisting of 20 responses. The within subject variable for the GoStop was delay interval (i.e., 50 msec, 150 msec, 250 msec, and 350 msec). Gambling group (PG vs. control) served as the between-subjects independent variable for both repeated measures ANOVAs, and as with the MANOVA, the ANOVAs were each conducted twice, first excluding and then including full scale IQ as a covariate. 17

A second MANOVA was conducted to examine potential differences between PGs and controls on 7 subtests of the WMS – Logical Memory I, Faces I, Verbal Paired Associates I, Logical Memory II, Faces II, Verbal Paired Associates II and Auditory Recognition. These subscales were selected because they measure cognitive functions that are relatively unrelated to EF. As in other analyses, the independent variable was gambling status (PG vs. control), and full scale IQ was included as a covariate.

To examine impulsivity differences, we used independent sample t-tests to compare PGs and controls on the BIS and delay discounting AUC scores. We also conducted Pearson correlation analyses to examine the associations between impulsivity and EF variables.

Results

Demographic and Gambling Variables

Table 1 presents comparisons of PGs and controls on demographic and gambling variables, as well as full scale IQ. PGs and controls were well matched on gender, age, race and education (all p > .05). PGs were more likely than controls to be employed and less likely to be married or cohabitating.

PGs scored significantly higher than controls on all gambling variables (all p < .05). PGs also scored lower than controls on full scale IQ. Subsequent analyses were conducted both with and without IQ as a covariate.

Executive Function

The overall multivariate comparison of PGs and controls on EF variables was statistically significant (F(8,81) = 2.63, p = .013, η2 = .21), with PGs scoring higher on the TOL total move score and lower on the TOL rule break score and on WCST categories than the control participants (see Table 2).

We conducted the same MANOVA again using IQ as a covariate. The results were similar to the first analysis. The overall multivariate comparison for EF was still significant (F(8,80) = 2.18, p = .04, η2 = .18). PGs evidenced impairment on the TOL total move (F(1,87) = 6.32, p = .01, η2 = .07) and rule break (F(1,87) = 6.28, p = .01, η2 = .07) measures, relative to controls. WCST categories scores no longer differed between the two groups (F(1,87) = 2.04, p = .16, η2 = .02). The multivariate effect for IQ was significant (F(8,80) = 4.16, p < .001, η2 = .29). Higher IQ was associated with higher letter number sequencing scores (F(1,87) = 29.68, p < .001, η2 = .25), higher total FAS score (F(1,87) = 4.70, p = .03, η2 = .05), completion of more WCST categories (F(1,87) = 4.96, p = .03, η2 = .05) and fewer WCST perseverative responses (F(1,87) = 10.96, p < .001, η2 = .11).

Decision-Making

Analysis of the Iowa Gambling Task revealed a significant group X time-block interaction (F(4,332) = 2.86, p < .05). Control participants demonstrated a pattern of more advantageous card choices over time blocks (Figure 2a), while the PGs evidenced more variable responding. Main effect for time block (F(4,332) = .43, p = .79), gambler group (F(1,83) = .01, p = .91), full scale IQ (F(1,83) = .85, p = .36) and the time block X full scale IQ interaction (F(4,332) = .41, p = .80) were not significant. We repeated this analysis using demographic- corrected T-Scores, and found similar results (data not shown, means presented in Figure 2b).

Response Inhibition

Results of the GoStop response inhibition task are presented in Figure 3. We found that 12 participants (9 PGs, 3 controls) responded to the task in ways that were inconsistent with the instructions, and these data were excluded in the analysis, leaving a total of 78 responses. A 19

significant within-subject effect was found F(3,73) = 3.05, p < .05, η2 = .11), such that the longer the latency time until the stop signal, the less ability participants had to inhibit their responding. The gambling group X latency (F(3,73) = 1.00, p = .40, η2 = .04) and full scale IQ X latency (F(3,73) = .50, p = .71, η2 = .02) interactions were not statistically significant. Main effects for gambling group (F(1,75) = .33, p =.57, η2 = .00) and full scale IQ (F(1,75) = 3.45, p = .07, η2 = .04) were also not significant.

Memory

The multivariate comparison of PGs and controls on memory measures from the WMS was not statistically significant (F(7,81) = 1.34, p = .24, η2 = .10). The multivariate effect for IQ, however, was significant (F(7,81) = 6.21, p < .001, η2 = .35). Higher IQ score was significantly associated with higher scores on the Logical Memory I (r = .43, p < .001), Verbal Paired Associates (r = .51, p < .001), Logical Memory II (r = .37, p < .001), Verbal Paired Associates II (r = .34, p < .001) and Auditory Recognition (r = .32, p < .01) scales.

Impulsivity

Due to administration errors, 9 delayed discounting assessments (6 PGs, 3 controls) were omitted from analysis. We conducted a t-test of AUC for PGs (n = 39) and controls (n = 42). Because of our reduced sample size, and the directional nature of our hypothesis (i.e., we predicted PGs to have less AUC than controls), we conducted a one-tailed t-test. PGs experienced significantly lower AUC scores (Mean .38, SD .23) than controls (Mean .47, SD .23; t(79) = 1.79, p < .05).

Similarly, PGs had significantly higher scores (Mean 64.7, SD 10.9) than controls (Mean 50.4, SD 7.8) on the BIS (t(88) = -7.2, p < .001). Correlations between the impulsivity measures and measures of EF are presented in Table 5. Only two significant correlations were identified. Greater AUC was significantly associated with higher intelligence, and needing more moves to complete the TOL task was associated with higher impulsivity scores on the BIS.

Discussion

Executive Function

We found that PGs seem to have deficits on specific aspects of EF. Planning, in particular, appears to be a deficit among PGs, relative to control participants. Goudriaan and colleagues (2006) similarly found that PGs performed more poorly on the TOL than did normal controls and individuals with Tourette Syndrome. Impulsivity studies have also found that PGs experience planning difficulties. In a recently published study, we found significant differences between PGs and controls on the non-planning scale of the BIS (Ledgerwood et al., 2009). Other studies have found PG to be associated with deficits on planning impulsivity scale responses (e.g., Fischer & Smith, 2008; Voon et al., 2007). Our results, in conjunction with past research, suggest that planning deficits may be the most consistently observed EF deficit in PG.

Perseveration, as measured by the WCST was also identified as a potential deficit among PGs. However, this deficit was no longer statistically significant once the analysis and included control for IQ. Goudriaan et al. (2006) also found that PGs completed fewer categories than healthy controls, but evidenced no significant differences in percentage of perseverative errors. By contrast, Cavedini et al. (2002) found significant difference between PGs and controls on a decision making task, but found no significant differences on the WCST categories or perseverative errors. Our study differed from Cavedini’s in that our sample size was substantially larger, which may account for our identification of significant findings on the categories measure before controlling for IQ. Thus, these findings suggest that PGs may experience perseveration relative to healthy control participants. However, interpretation of this finding should be made with caution as controlling for IQ resulted in this difference to no longer reaching statistical significance.

Remaining EF variables examined in our study (i.e., working memory, Stroop Interference, COWAT, etc.) were not statistically significant, and results of past studies have been inconsistent on the extent to which PGs exhibit deficits on these EF traits. Goudriaan et al. (2006), for example, found several deficits among PGs compared with controls on response inhibition, time estimation, cognitive flexibility and planning tasks. The two studies included similar sample sizes. However, our study included participants from both treatment and community sources, while Goudriaan’s study included primarily treatment-recruited samples. Thus, our more specific findings with regard to EF may reflect sampling differences between the two studies. Nevertheless, it is also notable that we found few differences between PGs recruited from treatment and community on EF measures (data not reported).

Lawrence and colleagues (2009) noted that PGs performed similarly to healthy controls on a measure of response inhibition despite scoring higher on impulsivity measures, a finding that we also recently reported (Ledgerwood et al., 2009), and found again in the current investigation. Lawrence et al. (2009), however, found that alcohol dependent individuals experienced significant deficits in response inhibition relative to controls, suggesting that PGs and individuals with alcohol dependence may differ in the extent to which they evidence disinhibition deficits. Another study of risk behaviours in preadolescents also found that problematic gambling behaviours were associated with impulsivity and sensation seeking, but 21

not on working memory and only very modestly correlated with the Stroop task (r = -.11; Romer et al., 2009). Gambling was reported at a relatively high prevalence rate in Romer’s study (27.6%). Thus, our findings are consistent with prior investigations that suggest focal deficits in certain types of EF (i.e., planning) and perhaps subtle deficits in other forms of EF, that are not always detectable in smaller and varied (e.g., by age, or gender) samples.

Although PGs and controls may not differ substantially in EF on several measures, they may evidence differential brain activation. For example, Potenza et al. (2003) found that PGs performed similarly to controls on the Stroop, and evidenced activation in the same areas as controls on fMRI (i.e., dorsal anterior cingulated, right middle and inferior frontal gyri, bilateral inferior frontal gyri, right insula, and right thalamus). However, PGs evidenced decreased activity in a region of the ventromedial prefrontal cortex that involves the left middle and superior frontal gyri, an area of the brain associated with decision making, reward processing and expectancy. As explained below, we also experienced differences between PGs and controls on decision-making. Such findings are important because there is evidence to suggest that some forms of EF dysfunction (e.g., disinhibition) may place PGs at risk for relapse (Goudriaan, Oosterlaan, De Beurs, & van den Brink, 2008).

Overall, EF measures were not highly intercorrelated. One exception was working memory; higher functioning on the working memory tasks was associated with higher functioning on verbal fluency, planning, set-shifting and full scale IQ. Otherwise, EF measures were largely uncorrelated except when separate measures within the same test are compared (e.g., TOL total moves and TOL rule breaks). These findings are partially consistent with past research that demonstrates that EF is a multifaceted construct, but that individual dimensions of EF are not entirely independent (Miyake et al., 2000). The measures selected for the present study appear to have identified relatively distinct aspects of EF; this finding supports our overall conclusion that PGs appear to exhibit specific (rather than diffuse) EF deficits relative to control participants. However, additional research should further examine the interrelationship of different EF dimensions.

Decision Making

Our findings are consistent with past research examining potential decision-making deficits in PGs relative to healthy controls. For example, Cavedini et al. (2002) found significant differences between PGs and controls on the Iowa gambling task. However, their study may have been confounded by age and/or gender effects. Brand et al. (2005b) found that PGs exhibited more disadvantageous risk taking, which was also associated with categorization, cognitive flexility and set-shifting measures of EF. Finally, as described above, Potenza et al. (2003) found that, while PGs and controls did not differ on Stroop task performance, their brain activation while performing the task is consistent with a model that suggests PG is associated with deficits in decision making.

Impulsivity

Results of analyses of the delay discounting AUC data were largely consistent with past studies that have examined this form of impulsivity among PGs (Ledgerwood et al, 2009; Petry, 2001). We found that PGs discounted at a significantly greater rate than control participants. However, it is notable that relative to our recently published study in which used the same discounting procedures were used (Ledgerwood et al., 2009), our PGs and controls were more similar on their rates of discounting. Specifically, the PGs in this study seem to have discounted at a somewhat lower rate than those who participated in our previous study. The reasons for this potential discrepancy are unclear, as both samples were made up of roughly equal numbers of PGs recruited from outpatient treatment and the community.

Nevertheless, the AUC data are consistent with the hypothesis that PGs are more impulsive than control participants, a finding that has been confirmed in numerous prior studies (Blaszczynski & Steel, 1998; Ledgerwood et al., 2009; Petry, 2001; Steel & Blaszczynski, 1998; Vitaro, Ferland, Jacques & Ladouceur, 1998). Comparison of PGs and controls on the BIS scale also revealed significant differences in impulsivity. As a secondary aim, we examined the relationship between scores on these measures of impulsivity and EF measures and found two significant correlations. First, higher impulsivity scores on the BIS were associated with taking more moves to complete the TOL test. Second, greater AUC on the Delay Discounting task was significantly correlated with higher full scale IQ. It is not surprising that a planning measure (TOL) would correlate with the BIS, which has a number of self-report items reflecting ability to plan. However, we expected several other EF measures to correlate significantly with impulsivity. Similarly, the lack of any correlations between EF measures and AUC is quite surprising.

Changes to the Original Proposal

Essentially, we had only two substantive changes to the original proposal. Both changes were related to struggles with recruitment. First, because recruitment was difficult at times, we asked for, and received a no-cost extension from OPGRC to continue data collection beyond the study period. Second, even with the no cost extension period, we were unable to meet the recruitment target of 100 participants (50 PGs and 50 controls) set forth in the original proposal. We did, however, recruit 90 participants (45 PGs and 45 controls), which afforded us sufficient statistical power to conduct statistical analyses, as noted in our sample justification section in the original proposal. Thus, although we experienced some recruitment difficulties, the overall goals of the study were met and the analyses are based on a sufficient number of participants. Budgetary adjustments related to the no cost period and reduced number of participants should be addressed by our accounting department.

Limitations & Strengths

This study has several limitations. First, we are limited by our sampling method. We recruited participants from a number of sources. Our PGs were recruited from both treatment and community sources. Although it could be argued that treatment-recruited PGs may have more severe difficulties, we found few significant differences between treatment and community PGs 23

on EF measures, and the few tests that did reveal significant differences did not necessarily favour overall deficits in one particular group. Another limitation is that we conducted assessments at only one time point, in a cross-sectional design, rather than examining possible changes in EF over time. Future studies may examine changes in EF prospectively as problem gambling symptoms remit.

Despite these limitations, the current study also had several important strengths. We were able to recruit PG and control samples that are similar on age, gender and other important demographics. For both the PG and control groups, we recruited roughly equal numbers of men and women, while traditionally studies of PG have over-sampled men. Among the PGs, we were able to recruit equal numbers of participants from outpatient treatment and community sources. Thus, our study is inclusive, resulting in strong external validity. Further, our sampling method allows us the ability to conduct some preliminary analyses to determine whether these two sources of research participants differ substantially. Finally, our test battery was a comprehensive assessment of EF and other discriminating cognitive processes, and it consisted solely of measures that have established norms. Thus, our investigation is a particularly strong test of the hypothesis that PGs experience EF dysfunction.

Implications and Conclusions

This study has implications for understanding both the etiology of PG, and the development and further testing of treatments. Our findings suggest that PGs experience overall deficits in planning and decision-making. Although we did not test these individuals before they developed gambling problems, we may hypothesize that people who have more deficient planning and decision-making abilities may be especially vulnerable to developing a compulsive gambling pattern. Indeed, decision-making and planning deficits appear to be associated with neurological dysfunction in the frontal regions of the brain (e.g., Bechara et al., 2001). Similarly, our findings are consistent with neurological research on problem gamblers that demonstrates impaired functioning in frontal areas of the brain associated with EF and reward (e.g., Campbell- Meikeljohn et al., 2008; Reuter et al., 2005). Thus, our study adds confirmatory data to support the hypothesis that neurological dysfunction in frontal regions of the brain associated with EF is a potential risk factor for developing gambling problems. Prospective studies may further test this hypothesis by examining whether planning and decision-making deficits predict later gambling problems.

Our findings may also contribute to the development of efficacious treatments for PG that target interventions to specific cognitive difficulties. Interventions involving higher level cognition and psychological mindedness may not be a good fit for PGs with EF dysfunction, and treatment techniques that focus more on specific impulsive behaviours, planning and decision making deficits may be most appropriate. Cognitive behavioural interventions, for example, tend to include behavioural components geared toward concrete goal setting as well as interventions that directly challenge gambling-related cognitive distortions (e.g., Petry, 2005). Such interventions may be most suited to PGs with planning and decision-making impairments because they may present these issues to clients in manageable ways. Additional research into treatment matching may provide evidence as to whether this is the case.

In conclusion, PGs appear to experience fairly specific EF deficits, particularly in relation to planning and decision-making. These deficits appear to be somewhat unrelated to impulsivity, per se. However, they represent a significant set of impairments in impulse control that may help researchers to understand the etiology of PG, and may help to devise effective treatments. Thus, our study lays the scientific groundwork for future development of interventions that specifically target EF, decision-making and impulsivity problems.

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Table 1. Demographic and gambling variables.

Variable Pathological Controls t or χ2 p <

Gamblers (N = 45)

(N = 45)

Age M(SD) 46.1(13.9) 45.8(17.3) t(88) = -.07 .95

Gender N(%) χ2(1,N=90) = .18 .67

Female 21(46.7) 23(51.1)

Male 24(53.3) 22(48.9)

Race N(%) χ2(3,N=90) = 3.11 .38

Caucasian 40(88.9) 43(95.6)

Asian 1(2.2) 0(0)

African Canadian 2(4.4) 0(0)

Other 2(4.4) 2(4.4)

Employed N(%) 33(73.3) 23(51.1) χ2(1,N=90) = 4.73 .05

Marital Status N(%) χ2(3,N=90) = 12.90 .01

Married/Cohabitating 18(40.0) 27(60.0)

Never Married 14(31.1) 13(28.9)

Divorced/Separated 13(28.9) 2(4.4)

Widowed 0(0) 3(6.6)

Education M(SD) 14.2(2.5) 14.4(2.5) t(88) = .32 .75

WASI Full Scale IQ M(SD) 101.0(10.3) 106.3(10.4) t(88) = 2.40 .02

NODS – Lifetime M(SD) 8.0(1.7) .3(.4) t(88) = -29.1 .001

NODS – Past Year M(SD) 7.5(1.8) .2(.4) t(88) = -27.0 .001 29

PGSI – M(SD) 14.0(5.6) .5(1.0) t(88) = -15.9 .001

Typical $ Wagered/30 – Median $730($1,205) $35($117) U = 1,859 .001

(interquartile range)

WASI = Wechsler Abbreviated Intelligence Scale; NODS = Norc Diagnostic Screen for

Gambling Problems; PGSI = Problem Gambling Severity Index of the Canadian Problem

Gambling Index; M(SD) = Mean (Standard Deviation).

Table 2. Raw means and standard deviations on executive function measures.

Variable Pathological Controls F(1,88) p < η2

Gamblers (N = 45)

(N = 45)

WMS Working Memory 101.0(14.2) 105.8(15.1) 2.39 .13 .03

Stroop Interference 48.9(9.9) 51.3(7.8) 1.71 .20 .02

COWAT Total Correct 37.9(9.9) 39.3(9.5) .50 .48 .01

COWAT Rule Break 1.8(1.6) 1.5(1.6) .74 .39 .01

TOL Total Moves 40.7(18.4) 30.4(14.4) 8.75 .004 .09

TOL Rule Breaks .64(1.2) .17(.52) 7.09 .01 .08

WCST Perseverative Responses 22.6(23.4) 16.3(12.7) 1.33 .25 .02

WCST Categories 4.2(2.2) 5.0(1.7) 4.02 .05 .04

WMS – Wechsler Memory Scale; COWAT – Controlled Oral Word Association Test; TOL –

Tower of London; WCST – Wisconsin Card Sort Test.

Table 3. Correlations between executive function variables and intelligence.

Variable 2 3 4 5 6 7 8 9

1. WMS Working Memory .15 .29** .01 -.28** -.03 -.37*** .23* .52***

2. Stroop Interference .14 .18 -.13 -.15 -.20 .19 .07

3. COWAT Total Correct .32** -.03 .08 -.11 .09 .24*

4. COWAT Rule Break -.13 -.08 -.06 .03 -.01

5. TOL Total Moves .34*** .27** -.15 -.22*

6. TOL Rule Breaks .22* -.21 -.09

7. WCST Perseverative -.79*** -

Responses .35***

8. WCST Categories .27**

9. WASI Full Scale IQ

* p < .05; ** p < .01; *** p < .001.

1. Wechsler Memory Scale Working Memory; 2. Stroop Interference; 3. Controlled Oral Word

Association Test Total Correct; 4. Controlled Oral Word Association Test Rule Break; 5. Tower

of London Total Moves; 6. Tower of London Rule Breaks; 7. Wisconsin Card Sort Test

Perseverative Responses; 8. Wisconsin Card Sort Test Categories; 9. Wechsler Abbreviated

Intelligence Scale Full Scale IQ

Table 4. Raw means and standard deviations for Wechsler Memory Scale scores.

Variable Pathological Controls

Gamblers (N = 45)

(N = 45)

WMS-III Logical Memory I 9.8(2.6) 11.2(2.9)

WMS-III Faces I 8.6(2.3) 9.2(2.9)

WMS-III Verbal Paired Associates I 10.1(2.1) 10.9(2.8)

WMS-III Logical Memory II 10.4(2.4) 11.6(2.8)

WMS-III Faces II 9.1(2.1) 9.8(2.4)

WMS-III Verbal Paired 10.9(2.2) 11.3(2.9)

Associates II

WMS-III Auditory Recognition 10.7(2.9) 11.0(2.5)

WMS – Wechsler Memory Scale.

Table 5. Pearson correlations between Delay Discounting Area Under the Curve (AUC), Barrett Impulsiveness Scale (BIS), executive function measures and full scale intelligence.

Variable Delay BIS

Discounting

AUC

WMS Working Memory .01 -.12

Stroop Interference -.07 .04

COWAT Total Correct -.05 -.04

COWAT Rule Break -.04 .18

TOL Total Moves -.11 .25*

TOL Rule Breaks -.07 .11

WCST Perseverative Responses .11 -.09

WCST Categories -.13 .11

WASI Full Scale IQ .36*** -.16

* p < .05; *** p < .001

Figure 1. Flow of participants through the study.

Telephone Screens (N = 159)

Excluded at Phone Screen (N = 64) - NODS between 2 and 4 (43) - Not Interested (8) - Substance Abuse (3) - Unavailable/Could not contact (4) - No show for appointment (5) - History of stroke (1) Excluded following Consent (N = 5) - Tested twice (1) - Positive for illicit substances (4) - Psychiatric (1)

Completed Completed Control Pathological Gamblers Participants (N = 45) (N = 45)

Figure 2. Mean Iowa gambling task: (a) raw scores; and (b) T scores, for pathological gamblers (closed square) and controls (open diamonds). A)

-2 MeanRaw Score

-4

-6 Block 1 Block 2 Block 3 Block 4 Block 5 Time Block

Mean Mean TScore 44

40 Block 1 Block 2 Block 3 Block 4 Block 5 Time Block

Figure 3. Estimated marginal means for response inhibition (GoStop) score at 50, 150, 250 and 350 MSEC delay for pathological gamblers (PGs) and controls.

70 Controls PGs 60

30 Percent Inhibited Percent

0 50 MSEC 150 MSEC 250 MSEC 350 MSEC Latency Delay

Figure 4. Delay discounting subjective dollar amounts and delay periods for pathological gamblers (PGs) and controls.

1000 PGs 900 Controls 800 700 600 500 400 300 200

Median Crossover Value ($) 100 0 0 200 400 600 800 1000 1200 1400 Delay in Weeks