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The Influence of Nicotine Craving and Personality Characteristics on Risky Decision Making in Nicotine Dependence College Students

A dissertation presented to the faculty of the College of Arts and Sciences of Ohio University

In partial fulfillment of the requirements for the degree Doctor of Philosophy

Melissa T. Buelow August 2009 © 2009 Melissa T. Buelow. All Rights Reserved.

2 This dissertation titled The Influence of Nicotine Craving and Personality Characteristics on Risky Decision Making in Nicotine Dependent College Students

by MELISSA T. BUELOW

has been approved for the Department of Psychology and the College of Arts and Sciences by

Julie A. Suhr Associate Professor of Psychology

Benjamin M. Ogles Dean, College of Arts and Sciences

3 Abstract BUELOW, MELISSA T., Ph.D., August 2009, Psychology The Influence of Nicotine Craving and Personality Characteristics on Risky Decision Making in Nicotine Dependent College Students (85 pp.) Director of Dissertation: Julie A. Suhr Previous research has shown that nicotine dependent individuals exhibit riskier behavior on decision making and other executive function tasks relative to non-smokers, and that performance varies as a function of nicotine satiation level. Links have also been found between impulsive sensation seeking and nicotine dependence, but their relationships with craving and decision making have rarely been studied. The current study examined the relationships between nicotine dependence, craving, impulsive sensation seeking, reward drive, and risky decision making in smokers and non-smokers. Smokers were randomly assigned to either overnight nicotine abstinence or to smoke as usual, and then all participants completed a series of questionnaires, the Iowa Gambling Task (IGT), and the Balloon Analogue Risk Task (BART). No group differences were found on the IGT or the BART. Abstinent smokers reported significantly higher nicotine craving than ad libitum smokers. Prior to testing the hypothesized models, confirmatory factor analyses of the personality characteristics and decision making variables did not support either as a unitary construct in the present sample. The predicted non-smoker and smoker models did not fit the data; however, a regression model was found for the nonsmoker group for the IGT data. Results of additional regression analyses showed limited support for nicotine craving and dependence affecting IGT and BART performance, but

4 indicated that, among ad libitum smokers only, personality characteristics affected performance on the IGT. The results of this study run contrary to numerous studies showing worse performance on decision making tasks among substance dependent individuals; however, they are in keeping with conflicting findings in the literature regarding performance stability across nicotine satiation level.

Approved: _____________________________________________________________ Julie A. Suhr Associate Professor of Psychology

5 Acknowledgments I would like to take this opportunity to say thank you to several individuals who have helped me to complete this dissertation project. I want to express my thanks to my husband, family, and friends who have provided their support during pursuit of my doctoral degree. I would also like to thank the Ohio University Department of Psychology, the Graduate Student Senate, and Dr. Julie Suhr’s laboratory for financial support that made this project possible. I would like to thank my dissertation advisor, Julie Suhr, Ph.D., for her guidance and support throughout my graduate and dissertation experiences. I would like to express my appreciation to my dissertation committee, John Garske, Ph.D., Ken Holroyd, Ph.D., Frank Bellezza, Ph.D., and Karen Coschigano, Ph.D. I would also like to acknowledge the support of my fellow lab members and research assistants, including Kristine Buelow, Dylan Welch, Jessica Hoy, Tara Riddle, and Stephanie Bewley.

6 Table of Contents Page Abstract ............................................................................................................................... 3 Acknowledgments............................................................................................................... 5 List of Tables ...................................................................................................................... 7 List of Figures ..................................................................................................................... 8 Introduction ......................................................................................................................... 9 Method .............................................................................................................................. 17 Results ............................................................................................................................... 25 Discussion ......................................................................................................................... 34 References ......................................................................................................................... 40 Appendix A: Methods ....................................................................................................... 78

7 List of Tables Page Table 1: Descriptive statistics for independent and dependent variables .......................56 Table 2: Correlation matrix .............................................................................................57 Table 3: Unrotated factor loadings from confirmatory factor analyses: Non-smokers ..59 Table 4: Unrotated factor loadings from confirmatory factor analyses: Smokers ..........60 Table 5: Correlations for IGT and BART variables by group ........................................61 Table 6: Varimax rotation factor loadings for five factor solution: Non-smokers .........62 Table 7: Varimax rotation factor loadings for four factor solution: Smokers ................63 Table 8: Summary of fit indices for alternative model: Non-smokers ...........................64 Table 9: Regression of personality characteristics on FTND score ................................65 Table 10: Regression of personality characteristics on HONC score .............................66 Table 11: Regression of personality characteristics on QSU score ................................67 Table 12: Regression of personality characteristics on PANAS-Negative score ...........68 Table 13: Regression of nicotine dependence on QSU score .........................................69 Table 14: Regression of nicotine dependence on PANAS-Negative score ....................70 Table 15: Regression of nicotine dependence on QSU score: Abstinent smokers .........71 Table 16: Regression of nicotine dependence on PANAS-Negative score: Abstinent smokers .................................................................................................72 Table 17: Regression of personality characteristics on IGT performance: Ad libitum smokers ...............................................................................................73

8 List of Figures Page Figure 1: Hypothesized structural equation model .........................................................74 Figure 2: IGT performance by smoking status ...............................................................75 Figure 3: BART performance by smoking status ...........................................................76 Figure 4: Regression weights for confirmatory factor model: Non-smokers .................77

9 The Influence of Nicotine Craving and Personality Characteristics on Risky Decision Making in Nicotine Dependent College Students Tobacco use is among the leading preventable causes of disease and disability related to disease in the United States (CDC, 2004). Nicotine, an addictive substance found in cigarettes (Stolerman & Jarvis, 1995), is rewarding to the brain, affecting mood and cognitive performance by targeting the dopaminergic mesocorticolimbic pathway (Kelley & Berridge, 2002; Pomerleau & Pomerleau, 1984). Acute nicotine administration increases activation to structures along this pathway, as evidenced by increased regional cerebral blood flow and brain drug concentration distribution on fMRI, and is correlated with cigarette craving and self-reported feelings of reward (Rose, Behm, Westman, Mathew, London, Hawk, et al., 2003; Stein, Pankiewicz, Harsch, Cho, Fuller, Hoffman, et al., 1998). A reward deficit can occur when nicotine levels are not maintained in the brain (Kenny & Markou, 2005). Behavioral choices, such as returning to smoking during a quit attempt, may be guided by this reward deficit. Individuals dependent on other substances than nicotine also make riskier decisions than non-substance dependent individuals. Risky decision making, a type of executive function, is thought to involve the failure to use a safer strategy to avoid a known negative consequence (Fishbein, Eldreth, Hyde, Matochik, London, Contoreggi, et al., 2005). Two of the most common behavioral measures of risky decision making in substance abuse research are the Iowa Gambling Task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994) and the Balloon Analogue Risk Task (BART; Lejuez, Read, Kahler, Richards, Ramsey, Stuart, et al., 2002). Risky

10 decision making on the IGT, in that individuals do not learn to select from the advantageous instead of the disadvantageous decks, is seen in alcohol, cocaine, and opioid dependent individuals (for a review, see Buelow & Suhr, 2009). Riskier and more impulsive selections are seen on the BART among current alcohol and drug users (Hopko, Lejuez, Daughters, Aklin, Osborne, Simmons, et al., 2006; Lejuez et al., 2002; Lejuez, Aklin, Zvolensky, & Pedulla, 2003). Few researchers have examined risky decision making in nicotine dependence with these two measures, even in light of the strong findings with other substances of abuse. Those that have assessed risky decision making have found conflicting results. Although some have found smokers are more likely than non-smokers to make decisions favoring smaller, more immediate rewards than larger, less immediate rewards (Mitchell, 2004), others have found no differences between smokers and non-smokers on risky decision making tasks (Harmsen, Bischof, Brooks, Hohagen, & Rumpf, 2006). In the only substance abuse study utilizing both tasks, more impulsive and riskier performance by smokers in comparison to non-smokers was seen on the BART but not the IGT (Lejuez, Aklin, Jones, et al., 2003). It is unclear why these differing results have been found; however, they may be due in part to differences in nicotine satiation level. As previously mentioned, a reward deficit can occur when nicotine levels are not maintained in the brain. Nicotine withdrawal symptoms, which include depressed mood, anxiety, irritability, concentration difficulties, and nicotine craving, can appear within 6-12 hours of last cigarette use (APA, 2000; Hughes, 2007). It is possible that nicotine satiation level affects performance on

11 decision making tasks; however, this has not been directly examined. With regard to other substances of dependence, abstinence is associated with both continued impairment (Fishbein, Eldreth, Hyde, Matochik, London, Contoreggi, et al., 2005) and with better performance (Bartzokis, Lu, Beckson, Rapoport, Grant, Wiseman, et al., 2000), in comparison to ad libitum substance use. Although nicotine satiation level effects on decision making have not been examined directly, its effects have been examined on other executive function tasks. Deeper delay discounting is seen in heavier than milder smokers (Reynolds, 2004), and ex-smokers perform similarly to non-smokers on this task (Bickel, Odum, & Madden, 1999). Worse performance on other risk taking and executive measures has been shown in abstinent versus ad libitum smokers (Havermans, Debaere, Smulders, Wiers, & Jansen, 2003; Powell, Dawkins, & Davis, 2002; Zack, Belsito, Scher, Eissenberg, & Corrigas, 2001). 13-hour nicotine abstinence is also associated with risky decision making on the IGT (Field, Santarcangelo, Sumnall, Goudie, & Cole, 2006). Collectively, these studies provide some evidence for the transience of nicotine’s effects on decision making, as smokers, after a period of abstinence, tend to perform worse on various executive tasks than ad libitum smokers. Few researchers have investigated these effects using the IGT and BART. Generally these results lead to speculation that craving for nicotine in the abstinent smoker, which can occur as part of the nicotine withdrawal process, may be related to risky decision making; however, the influence of nicotine craving has not been directly examined in the risky decision making literature to date.

12 Nicotine craving levels can be directly manipulated with the presence of smoking cues (i.e., a lit cigarette). Such cues act as salient, attention grabbing devices that can distract smokers, but not non-smokers, from other tasks and stimuli. Increased nicotine craving is evident following exposure to smoking but not neutral cues (Bordnick, Graap, Copp, Brooks, Ferrer, & Logue, 2004; Conklin, Tiffany, & Vrana, 2000; Field, Rush, Cole, & Goudie, 2007). Activation to the mesocorticolimbic pathway also increases in response to smoking cues in smokers but not non-smokers (for a review, see McClernon & Gilbert, 2004; Due, Huettel, Hall, & Robin, 2002). Cue reactivity is greater among abstinent than ad libitum smokers (McDonough & Warren, 2001), and among smokers experiencing withdrawal symptoms than those not experiencing such symptoms (Payne, McClernon, & Dobbins, 2007). Some evidence exists to indicate exposure to smoking cues while abstinent from nicotine both increases craving and decreases performance on decision making tasks (Powell et al., 2002), as well as creates a preference for immediate cigarette reinforcers over longer-term monetary reinforcers (Lussier, Higgins, & Badger, 2005). Collectively these studies provide evidence for risky decision making in nicotine dependent individuals; however, their chronicity is as of yet unclear. Deficits may be a temporary function of nicotine abstinence, or may reflect longer-lasting changes in frontal lobe functioning. Craving may also affect the relationship between nicotine dependence and risky decision making. Personality characteristics may also play a role in both the initiation and maintenance of nicotine use, as well as in risky decision making.

13 Longer-term personality characteristics, such as impulsive sensation seeking and reward drive, can influence the initial decision to try cigarettes. Zuckerman’s (2006) conceptualization of impulsive sensation seeking is based in part on Gray’s (1987) theory of behavioral inhibition and activation systems (BIS/BAS). Individuals high in BIS are more likely to withdraw from decisions when a threat is experienced (risk avoidance), whereas individuals high in BAS are more likely to respond to indications of potential rewards than of potential risks (Carver, 2004; Carver & White, 1994). Zuckerman (2006) outlined three levels of substance use and abuse: 1) curiosity leads to experimentation; 2) pleasurable sensations associated with use maintain drug use; and 3) physical symptoms of withdrawal prevent cessation. Impulsive sensation seeking and BAS have been shown associated with first drug use (Clark, Robbins, Ersche, & Sahakian, 2006; Knyazev, 2004; Wills, Vaccaro, & McNamara, 1994). Impulsive tendencies may also aid in the continuance of drug use (Doran, Spring, McChargue, Pergadia, & Richmond, 2004; Levitt, Selman, & Richmond, 1991), even after controlling for levels of nicotine craving (Lipkus, Barefoot, Feaganes, Williams, & Siegler, 1994). Negative emotions can appear following a period of nicotine abstinence. It is avoidance of this affect that may in part keep individuals from attempting to quit smoking. Impulsivity, as well as length of abstinence, is associated with intensity of nicotine craving (Zilberman, Tavares, Hodgins, & el-Guebaly, 2007). More impulsive individuals also report greater relief of negative affect after smoking (Doran et al., 2006). Overall, impulsive sensation seekers, in that they tend to take risks while seeking out new, novel experiences (Zuckerman, 2006), are more likely to use alcohol and other

14 drugs (Leland & Paulus, 2005), and impulsivity in general has been linked with smoking behaviors (Doran, Cook, McChargue, Myers, Spring, 2008; Geist & Hermann, 1990). In addition, these personality characteristics can themselves be directly related to performance on decision making tasks. Riskier performance on the IGT is associated with sensation seeking (Crone, Vendel, & van der Molen, 2003), impulsivity (Davis, Patte, Tweed, & Curtis, 2007), behavioral activation (Franken & Muris, 2005; Suhr & Tsanadis, 2007; van Honk, Hermans, Putman, Montagne, & Schulter, 2002), and state negative mood (Must, Szabo, Bodi, Szasz, Janka, & Keri, 2006; Suhr & Tsanadis, 2007). Riskiness on the BART is associated with impulsivity (Hunt, Hopko, Bare, & Lejuez, 2005; Lejuez, Aklin, Zvolensky, et al., 2003; Lejuez, Aklin, Daughters, Zvolensky, Kahler, & Gwadz, 2007). High levels of impulsivity, sensation seeking, and BIS/BAS are associated with risky performance on other decision making tasks (Kambouropoulos & Staiger, 2004; Mitchell, 1999). Abstinence may make smokers more impulsive on decision making tasks. 13-hour nicotine abstinence promoted increased impulsivity on behavioral decision making tasks (Field et al., 2006). High nicotine craving during abstinence was predicted in part by high scores on impulsivity and disinhibition measures (Reuter & Netter, 2001). It is possible that impulsive sensation seeking, BAS, and nicotine abstinence combine to affect this process. Individuals who are in a state of withdrawal and who also score high on measures of these personality characteristics are more likely to need a higher level of stimulation than lower scorers, and selecting immediate reinforcers may provide that needed “thrill.” In fact, high sensation seekers experience a stronger “rush” from nicotine

15 than low sensation seekers given the same dose (Zuckerman, 2006). Although it has been hypothesized that BIS and BAS activation may mediate biases towards smoking-related cues in nicotine dependent smokers (Munafo, Mogg, Roberts, Bradlye, & Murphy, 2003), few have investigated this phenomenon. Although research has shown that nicotine satiation level can affect performance on various executive function tasks, there has been no consensus reached as to the directionality of this effect. Satiation level effects have been found on the IGT and BART; however, this research has been relatively limited to other substances of abuse, not nicotine. Many who have examined performance across nicotine satiation levels have not included a measure of self-reported nicotine craving, although craving may also impact task performance. Strong effects of personality characteristics have been found on the IGT and BART, and these same personality characteristics affect both the initiation and maintenance of substance use; yet prior studies have not included personality variables in their analyses. The present study aimed to expand upon previous research by linking these areas. The Present Study In the present study, moderately to severely nicotine dependent smokers were randomly assigned to either an ad libitum or overnight abstinence condition, and a control group of non-smokers also participated. All participants were exposed to a smoking cue during completion of behavioral decision making tasks, and craving was assessed at multiple times throughout the session. This methodological paradigm was designed to test a model linking nicotine dependence, craving, impulsive sensation seeking, BAS, and

16 risky decision making (Figure 1). The model proposed that nicotine dependence is directly related to performance on decision making tasks; however, two other factors, personality characteristics and nicotine craving, are predicted to affect this relationship. Pre-morbid personality characteristics both directly relate to decision making, as well as affect the relationships between nicotine dependence and decision making and between nicotine craving and decision making. The model also proposed that nicotine craving is directly related to decision making, but is affected by nicotine dependence level. It was hypothesized that abstinent and ad libitum smokers would perform worse on the BART and IGT than non-smokers, and that abstinent smokers would perform worse than ad libitum smokers on these tasks. It was also hypothesized that nicotine craving would be higher in both groups of smokers than non-smokers, and that abstinent smokers would have higher craving than ad libitum smokers. With regard to the proposed model, it was hypothesized that the path coefficients would not be significantly different between the two smoker groups on the paths from personality characteristics to decision making and from personality characteristics to nicotine dependence and nicotine craving. It was also hypothesized that the path coefficients related to nicotine craving would be greater in the abstinent than the ad libitum smokers. The only predicted path for the nonsmoker group was between personality characteristics and decision making, as nicotine dependence and craving should not be a factor for non-smokers

17 Method Participants 132 undergraduate students, ages 18 – 33, enrolled in psychology courses at a medium-sized Midwestern University, participated in the present study. Inclusion criteria for the smoker group were a score of 3 or higher on the Fagerstrom Test of Nicotine Dependence (FTND), and compliance with a randomly assigned smoking status group (abstinent or ad libitum). 27 non-smokers were culled from further analysis due to meeting one or more of the following exclusion criteria: 14 reported use of over 10 cigarettes in their lifetime, 8 scored above a 0 on the nicotine dependence measures, 4 reported recent cigarette use, and 1 reported using chewing tobacco. 2 participants were removed from analysis due to unusable data. Of the remaining 102 participants, 36 were male, and racial/ethnic status was reported as the following: 91 Caucasian, 5 African American, 3 Asian/Asian American, 2 Hispanic, and 1 biracial. 48 were in the nonsmoker group, 25 were in the abstinent smoker group, and 29 were in the ad libitum smoker group. Measures Additional psychometric information and copies of each measure are available in Appendix A. Demographic and Smoking History Questionnaire. This questionnaire assessed basic demographic information and current and past smoking habits. Smoking history questions were adapted from the National College Health Risk Behavior Survey (CDC, 2003), which was originally used to determine the occurrence of six categories of

18 risky/unhealthy behaviors on college campuses. Factor analyses provide support for a 7item smoking subscale (Buelow, 2005; Everett et al., 1999), which was used in the present study. Fagerstrom Test of Nicotine Dependence (FTND). The FTND assesses level of nicotine dependence in smokers (Heatherton et al., 1991). Scores range from 0 (lower dependence) to 10 (higher dependence). Various classifications of severity of nicotine dependence on the FTND exist (Lesch et al., 2004; Mogg et al., 2005; Hillemacher et al., 2006); however, in the present study a score of 3 or higher was used to classify individuals as moderately to severely dependent smokers. Internal consistency is moderate (α = .61, Heatherton et al., 1991), and the FTND correlates with self-reported number of cigarettes smoked per day (Mogg et al., 2005) and other measures of current tobacco use (Wellman, Savageau, et al., 2006). Hooked on Nicotine Checklist (HONC). The Hooked on Nicotine Checklist is a 10-item measure of nicotine dependence level with high internal consistency in adolescents (α = .90 - .94; DiFranza, Savageau, Fletcher, Ockene, Rigotti, McNeill, et al., 2002) and adults (α = .82 - .83; Wellman, DiFranza, Savageau, Godiwala, Friedman, & Hazelton, 2005). For the purposes of the present study, the HONC was used to confirm smoking status on the FTND. PiCO MicroSmokerlyzer. The PiCO MicroSmokerlyzer was developed for use in smoking cessation trials and research studies, and provides a digital read out of breath carbon monoxide (CO) levels in parts per million (ppm; Bedfont Scientific Ltd.). The Smokerlyzer products have been used to assess recent smoking in several previous

19 studies requiring nicotine abstinence (e.g., Mogg et al., 2005; Field & Duka, 2004). The half-life of CO is approximately 2-5 hours (Powell et al., 2002). Bell and colleagues (1999) tracked changed in CO levels over 16 hours of abstinence, finding that CO levels average: 18.1 ppm after 4 hours, 11.1 ppm after 8 hours, and 6.7 ppm after 16 hours. A cut-off level of 10ppm was utilized to confirm overnight nicotine abstinence, consistent with recommendations from the manufacturer (Bedfont Scientific Ltd). Questionnaire on Smoking Urges (QSU). The QSU was created to assess smoking urges and cravings (Tiffany & Drobes, 1991). This 32-item measure has a twofactor structure encompassing both the desire to smoke and anticipation of relief from withdrawal symptoms (Tiffany & Drobes, 1991). The measure has high internal consistency (α = .93 - .95), and scores increase with increased length of nicotine abstinence (Tiffany & Drobes, 1991). For the purposes of the present study, an average score was calculated. Positive and Negative Affect Schedule (PANAS). The PANAS was created to assess a two-factor model of affect: positive (e.g., enthusiasm, alertness, full concentration) and negative (e.g., anger, contempt, disgust, nervousness) (Watson, Clark, & Tellegen, 1988). Average scores were found on each subscale based on feelings in the present moment. Internal consistency is high for both subscales (α = .85-.89), 8-week test-retest reliability is moderate (r = .45-.54), and scores are correlated with scores on the Beck Depression Inventory (Watson et al., 1988). Higher scores on the negative affect subscale have been found following 12-hour nicotine abstinence (Leventhal et al., 2007).

20 BIS/BAS. The BIS/BAS was created to assess Gray’s theory of behavioral inhibition and activation systems (Carver & White, 1994). BAS is sensitive to reward, punishment, and the escape from punishment, whereas BIS is sensitive to signals of punishment and the possibility of negative outcomes. Individuals respond to the 24 items on a 4-point scale ranging from 1 (very true for me) to 4 (very false for me). For the present study, a total score was calculated for each BAS subscale: BAS-Reward Responsiveness, BAS-Fun Seeking, and BAS-Drive. Internal consistency varies on each subscale (α = .55 - .84; Knyazev & Slobodskoj-Plusnin, 2007; Meyer et al., 1999), and 8week test-retest reliability is moderate (r = .59 - .69; Carver & White, 1994). Impulsive Sensation Seeking Subscale of the Zuckerman-Kuhlman Personality Questionnaire (ImpSS). The ImpSS is a 19-item measure of the personality characteristics of impulsivity and sensation seeking (Zuckerman & Kuhlman, 2000). A summed total score was calculated and used in the present study (range: 0 – 19). Internal consistency (α = .80; Ball, 1995) and two month test-retest reliability (.82 - .87, Zuckerman & Kuhlman, 2000) are high, and it is highly correlated with other measures of sensation seeking (McDaniel & Zuckerman, 2003; Zuckerman & Cloninger, 1996). Wechsler Abbreviated Scale of Intelligence (WASI). The WASI is a brief measure of intelligence adapted from the Wechsler Adult Intelligence Scale (Wechsler, 1999). In the present study, only the Vocabulary and Matrix Reasoning sections were administered in order to assess general cognitive abilities of participants, and to ensure the absence of group differences in basic intellectual skills. Test-retest reliability for the two-scale measure is high (α = .96; Wechsler, 1999).

21 Iowa Gambling Task (IGT). The IGT was created as a means of assessing decision making deficits in a laboratory setting (Bechara et al., 1994; see Buelow & Suhr, 2009, for a review). Individuals are told to maximize their profit by selecting from one of four decks of cards: Decks A and B are termed “disadvantageous” and Decks C and D “advantageous” based on the extent of their long-term gains and losses (Bechara et al., 1994). Validity has been shown through expected results in populations in which risky decision making is predicted (Bechara et al., 1994; Bechara & Martin, 2004; Goudriaan, Oosterlaan, et al., 2005). Little reliability data is available due to strong practice effects (Ernst, Grant, et al., 2003; Verdejo-Garcia et al., 2007; Lejuez et al., 2003). For the purposes of the present study, a total score was calculated by subtracting the total number of disadvantageous selections from the total number of advantageous selections in the last 40 trials, as these are associated with decision making “under risk” (Brand et al., 2007). Balloon Analogue Risk Task (BART). The BART was created to assess risk taking by mimicking risk taking in the real-world, in that a behavior would be rewarded up until a point, after which repeated engagement in that behavior would lead to worse outcomes (Lejuez et al., 2002). Participants are told to earn money by pumping up a balloon, receiving 5 cents per pump, and to collect the money before the balloon pops. Participants are not provided with any information about the likelihood of the balloon popping on a given trial (Lejuez et al., 2002). In the present study, the average number of pumps adjusted for only unexploded balloons was the dependent variable used. The BART is correlated with measures of sensation seeking and impulsivity (Lejuez et al.,

22 2002), but not with age, intelligence, depression, or empathy (Lejuez et al., 2003; Lejuez et al., 2002). Procedure Information about the study was made available via an online sign-up system to undergraduate students enrolled in psychology courses. Interested students completed the FTND online, and only those scoring a 0 (“non-smoker”) or a 3 and above (“smoker”) were able to sign-up for the study. At that time, smokers were randomly assigned via a coin flip to one of two conditions: overnight abstinence or ad libitum smoking. Participants in the abstinent group were instructed to refrain from smoking beginning at 10pm the night before, and told that compliance would be checked via breath CO reading. Any reading of 10ppm or lower confirmed overnight abstinence. Any participant in the abstinent group with a CO reading over 10ppm was invited to reschedule (n = 1). All experimental sessions occurred between 8am and 12pm. Following informed consent procedures and breath CO reading, participants completed the self-report questionnaires presented in a random order, followed by the WASI subtests. Next, participants were seated at a computer with a smoking cue (an open purse with a pack of cigarettes visible to participants) for the remainder of the session. The IGT and BART were administered in a random order. The two nicotine craving measures were presented before the first computerized task, before the second task, and after the second task. Participants were then given a copy of the debriefing information, given credit for their participation, and provided with a monetary compensation.

23 Data Analysis Prior to testing the hypotheses, all variables were examined for accuracy of data entry, missing values, and violations of the analysis assumptions. Comparisons between the three groups on demographic variables and measures of personality characteristics were conducted using univariate ANOVA for the continuous variables and chi-square for the categorical variables (Table 1). Significant omnibus ANOVAs were followed-up with Tukey HSD post-hoc pairwise comparisons. A correlation matrix for the study variables can be found in Table 2. There were no differences between the three groups with regard to age, F(2, 99) = 1.87, p = .159; gender, χ2(2, N = 102) = 0.67, p = .714; estimated intelligence, F(2, 97) = 0.05, p = .956; and racial/ethnic status, χ2(2, N = 102) = 0.64, p = .726. Significant differences between the three groups were noted for impulsive sensation seeking, F(2, 99) = 10.17, p < .001. Specifically, both groups of smokers scored significantly higher on impulsive sensation seeking than the non-smokers. There were no differences between the three groups on BAS, F(2, 92) = 1.079, p = .344. Next, a series of one-way ANOVAs were conducted to examine differences between the three groups on the risky decision making tasks and nicotine craving measures. If a significant main effect of group was found, Tukey’s HSD tests were used in post-hoc pairwise comparisons among the groups. Structural equation modeling (SEM) was used to identify, estimate, and assess the fit of the hypothesized models for each group. The models were created using SPSS 15.0 and AMOS 7.0, and analyzed with maximum likelihood estimation. Multiple fit indices were utilized to examine model fit, including the overall chi-square statistic (CMIN),

24 normed fit index (NFI), comparative fit index (CFI), parsimony adjusted comparative fit index (PCFI), parsimony ratio (PRATIO), root-mean-square error of approximation (RMSEA) with a 90% confidence interval, and Akaike’s information criterion (AIC). Interpretation of these fit indices was guided by Kline (2004) and Byrne (2001). A nonsignificant CMIN value, as well as values of NFI and CFI over .90 and a RMSEA value less than .05, all indicate adequate model fit. AIC values are used to compare the fit of competing models, with lower values indicating a better model fit. First, confirmatory factor analyses were conducted to determine whether the hypothesized constructs (i.e., personality characteristics, decision making) were supported by the data. Based on the results of these analyses, exploratory factor analyses were conducted to examine the relationships between the target variables. The results of the exploratory factor analyses were used to examine several alternative models, as the applicability of the hypothesized structural equation model to the data was not supported by these analyses. Linear regression analyses were also conducted to examine the hypothesized relationships between personality characteristics and decision making, personality characteristics and nicotine dependence, and nicotine dependence and decision making. Power Analysis. Power analyses were conducted using Cohen’s standard for medium effect sizes (f = .25) using G-Power (Faul, Erdfelder, Lang, & Buchner, 2007). For the current sample of 49 non-smokers, 25 abstinent smokers, and 29 ad libitum smokers (103 total participants), the obtained power for the one-way omnibus ANOVA was .6025. As the hypothesized structural equation model could not be defined in the

25 present sample, power analysis was not conducted for it. The obtained power for the additional regression analyses ranged from .2436 (for the 25 abstinent smokers) to .8847 (for the 103 total participants). Results Hypothesis 1. No support was found for the hypothesis that smokers would perform worse than non-smokers on the risky decision making tasks (see Table 1). No differences were found between abstinent smokers and non-smokers, or between ad libitum smokers and non-smokers, on the last 40 trials of the IGT, F(2, 98) = 0.92, p = .403, partial η2 = .018 (Figure 2). No differences were found between abstinent smokers and non-smokers, or between ad libitum smokers and non-smokers, on the number of pumps on the BART adjusted for only the unpopped balloons, F(2,98) = 0.02, p = .986, partial η2 = .000 (Figure 3). Hypothesis 2. No support was found for the hypothesis that abstinent smokers would perform worse than ad libitum smokers on risky decision making tasks (see Table 1). No differences were found between abstinent and ad libitum smokers on the last 40 trials of the IGT, t(51) = 0.66, p = .510, d = 0.18. Similarly, no differences were found between abstinent and ad libitum smokers on the number of pumps on the BART adjusted for only the unpopped balloons, t(50) = -0.14, p = .891, d = 0.04. An exploratory repeated measures ANOVA with IGT Block (1 – 5) as the within subjects variable and smoking status as the between subjects variable was conducted. There was a significant main effect for block, Wilks’ Lambda = .867, F(4, 95) = 3.643,

26 p = .008, partial η2 = .133. Performance on Block 1 was significantly lower than performance on Blocks 2 (p = .008), 3 (p = .004), and 5 (p = .025), and marginally lower than performance on Block 4 (p = .063). There was also a significant interaction between Block and group, Wilks’ Lambda = .840, F(8, 190) = 2.126, p = .033, partial η2 = .083, indicating that the pattern of performance across blocks varied according to group status. Hypothesis 3. Scores on the measures of nicotine craving and negative state affect were significantly different across groups (see Table 1). Abstinent smokers showed significantly greater levels of nicotine craving on the QSU across all four times assessed in comparison to both ad libitum smokers and non-smokers (all ps < .001), providing support for the hypothesis that abstinent smokers would have greater craving than ad libitum smokers. Ad libitum smokers also showed significantly greater levels of nicotine craving on the QSU than non-smokers across all four times assessed (all ps < .001), providing support for the hypothesis that both groups of smokers would have greater craving than the non-smokers. In terms of negative state affect, no differences were found between the groups at the first time assessed (i.e., start of the session), F(2, 100) = 2.72, p = .071. At each of the remaining three assessments, negative state affect was greater among both groups of smokers in comparison to the non-smokers (all ps < .05); no differences were found between the abstinent and ad libitum smokers (all ps > .40). Craving and negative state affect measured at Time 3 (i.e., prior to the second computerized task) were compared to their measurement at Time 1, in order to assess the effect of nicotine cue exposure on these variables. Results of a paired-samples t-test indicated that there was no difference in craving, t(43) = -1.45, p = .398, and negative

27 state affect, t(47) = 0.99, p = .328, from Time 1 to Time 3 among non-smokers. Cue exposure increased self-reported nicotine craving among abstinent, t(24) = -2.37, p = .026, and ad libitum smokers, t(27) = -2.77, p = .010; however, no differences were found in negative state affect among either abstinent, t(23) = -0.03, p = .980, or ad libitum smokers, t(28) = 0.59, p = .561. This increase in self-reported nicotine craving was not significantly different between the two groups of smokers, t(46) = -0.525, p = .602. Confirmatory Factor Analyses. In order to examine whether the predicted latent variables (i.e., personality characteristics, risky decision making, nicotine dependence, nicotine craving) were indeed supported by the data, confirmatory factor analyses (CFAs) were conducted using SPSS 15.0. Due to the small sample size of the abstinent and ad libitum smoker groups, these participants were combined into one overall “smoker” group (N = 54). Results of the non-smoker and smoker analyses can be seen in Tables 3 and 4 respectively. The personality characteristics construct was not supported in either smokers or non-smokers, as the impulsive sensation seeking variable did not load highly on the factor. Although the nicotine dependence construct could not be examined in the non-smokers due to the FTND scores having a 0 variance, the nicotine dependence construct was supported in the smoker group. Both QSU and PANAS-negative loaded highly on the nicotine craving construct in smokers but not non-smokers. Similarly, the BART and IGT both loaded highly on the decision making construct in smokers but not non-smokers. Due to the lack of support for several of the predicted constructs in the present data, additional causal model analyses could not be conducted as hypothesized.

28 Exploratory Factor Analyses. Exploratory factor analyses (EFAs) were conducted to examine the relationships between the variables of interest among smokers and nonsmokers. As results of the CFAs indicate that the IGT and BART do not make up a unitary construct among non-smokers, additional BART and IGT outcome variables needed to be added. The following variables were included in the exploratory factor analyses: the three BAS subscales, impulsive sensation seeking, QSU, PANAS-negative, advantageous minus disadvantageous selections on the IGT across each of the five blocks of trials, and three BART outcome measures (average number of pumps adjusted for just unexploded balloons, total money earned, and total number of explosions). The nicotine dependence measures (FTND and HONC) were also included in the EFA for the smoker group only. See Table 5 for correlations among the IGT and BART variables by group. For the non-smokers, the Kaiser-Meyer-Oklin value was low (.460). Principal components analysis revealed the presence of five components with eigenvalues exceeding 1, explaining 23.102%, 19.344%, 12.816%, 9.987%, and 7.713% of the variance respectively. Varimax rotation was conducted to aid interpretation of the components (Table 6). Factor 1 included the 3 BART outcome variables, while Factor 2 included performance on Blocks 2, 3, and 5 of the IGT. Block 4 performance on the IGT also loaded weakly on Factor 2. Factor 3 included the 3 BAS subscales. Block 4 IGT performance was the only factor loading highly on Factor 4; however, both BAS-reward responsiveness and impulsive sensation seeking also loaded weakly on this factor. Factor 5 included scores on both impulsive sensation seeking and the QSU. Neither PANASnegative nor Block 1 IGT performance loaded highly on any of the five factors.

29 For the smokers, the Kaiser-Meyer-Oklin value was again low (.452). Principal components analysis revealed the presence of five components with eigenvalues exceeding 1, explaining 18.908%, 16.279%, 13.704%, 10.343%, and 6.791% of the variance respectively. Examination of the scree plot showed a break after the fourth factor; therefore, varimax rotation was conducted to aid interpretation on the first four factors (Table 7). Factor 1 included the 3 BART outcome variables. Blocks 3, 4, and 5 of the IGT loaded highly on Factor 2, whereas performance on Blocks 1 and 2 only loaded moderately on this factor. Factor 3 included both the nicotine dependence (i.e., FTND, HONC) and nicotine craving (i.e., QSU, PANAS-negative) variables. The three BAS subscales loaded highly on Factor 4, whereas impulsive sensation seeking only loaded weakly on this factor. Based on these exploratory factor analyses, further analyses of the hypothesized and alternative models were conducted utilizing the three BART variables as a “BART decision making” construct, performance across Blocks 3, 4, and 5 of the IGT as an “IGT decision making” construct, and the three BAS subscales as the personality characteristics construct. For the smokers, the nicotine dependence and nicotine craving variables were combined into one overall nicotine characteristics construct. Fitting the Structural Equation Models. As previously described, the results of the confirmatory factor analyses indicated that the hypothesized causal models could not be conducted in the present study. Therefore, alternative models were examined. These alternative models were performed after reviewing the results of the previously described exploratory factor analyses. Hence, any adequately-fitting models found cannot be

30 considered confirmed by the data, and additional data would be needed to confirm these models. Non-Smokers. Several alternative structural equation models, in that the components of the personality characteristics variable changed, were examined. For each, the decision making latent variable encompassed just the IGT performance on Blocks 3, 4, and 5. A causal model in which just the three BAS subscales affected IGT performance was significant, χ2(9, N = 45) = 17.754, p = .038, indicating that the model was not an adequate fit for the data. A causal model in which just the three BAS subscales affected BART performance could not be fit to the data, due to the presence of negative error terms. Confirmatory factor analysis models were examined to assess model fit in the non-smoker group. Again, analyses were conducted separately for the BART and IGT data. For these models, the personality characteristics variable included just the three BAS subscales. For non-smokers, the overall chi-square for a model containing the personality characteristics and IGT decision making (Blocks 3, 4, and 5) was nonsignificant, χ2(9, N = 45) = 14.518, p = .105 (Figure 4). The correlation between decision making and personality characteristics was marginally significant, p = .068. Examination of the fit indices shows that not all support this model as adequately fitting the data (Table 8). While the CFI is above .900, NFI is low and the 90% RMSEA confidence interval is outside the accepted .000 - .080 range. A model containing the personality characteristics (i.e., three BAS subscales) and BART decision making could not be fit to the data, due to the presence of negative error

31 terms. An alternative model, which contained the personality characteristics and a BART decision making variable that included just the adjusted number of pumps and the total earned money, was significant, and therefore was not an adequate fit for the data,

χ2(5, N = 50) = 16.045, p = .007. Smokers. Several alternative structural equation models were examined. For each, the decision making latent variable encompassed either just IGT performance on Blocks 3, 4, and 5, or the three BART outcome variables (number of adjusted pumps, total explosions, total money earned). A causal model in which the three BAS subscales affected IGT performance could not be fit to the data, due to the presence of negative error terms. Similarly, negative error terms were found when examining alternative causal models with personality affecting BART performance, the combined nicotine variable (i.e., FTND, HONC, QSU, and PANAS-negative) affecting IGT performance, and the combined nicotine variable affecting BART performance. Confirmatory factor analysis models were examined to assess model fit in the smoker group. Again, analyses were conducted separately for the BART and IGT data. For these models, the personality characteristics variable included just the three BAS subscales, and the nicotine characteristics variable included the nicotine dependence (FTND, HONC) and nicotine craving (QSU, PANAS-negative) scores. For smokers, a model containing the personality characteristics and IGT decision making variable could not be fit to the data, due to the presence of negative error terms. Similarly, negative error terms were found when examining models containing the following: the personality

32 characteristics and BART variables, the nicotine and IGT variables, and the nicotine and BART variables. In sum, no confirmatory models could be found in the smoker group. Additional Regression Analyses. A set of additional regression analyses was conducted to address the hypothesized relationships in the overall model that could not be addressed with analysis of the hypothesized models due to the lack of confirmatory evidence from the CFAs and EFAs. First, the following regressions were conducted in the overall sample: 1) personality characteristics (ImpSS, BAS) on risky decision making, 2) nicotine dependence (FTND, HONC) on risky decision making, 3) nicotine craving (QSU, PANAS-negative) on risky decision making, 4) personality characteristics on nicotine dependence, 5) personality characteristics on nicotine craving, and 6) nicotine dependence on nicotine craving. Next, these regressions were conducted by smoking status (i.e., non-smoker, abstinent smoker, ad libitum smoker) to examine differences by smoking status group. Regressions on the Overall Sample. A model containing just the personality characteristics did not explain a unique proportion of the variance in performance on the IGT, F(4, 89) = 1.153, p = .337, R2 = .049. A model containing just the nicotine dependence measures also did not explain a unique proportion of the variance in performance on the IGT, F(2, 98) = 0.428, p = .653, R2 = .009. Similarly, a model containing just the nicotine craving measures did not explain a unique proportion of the variances in performance on the IGT, F(2, 94) = 0.627, p = .536, R2 = .013. With regard to performance on the BART, a model containing just the personality characteristics did not explain a unique proportion of the variance, F(4, 89) = 2.068,

33 p = .092, R2 = .085. Similarly, a model containing just the nicotine dependence measures did not explain a unique proportion of the variance in performance on the BART, F(2, 97) = 0.322, p = .726, R2 = .007. A model containing just the nicotine craving measures also did not explain a unique proportion of the variances in performance on the BART, F(2, 94) = 1.347, p = .265, R2 = .028. A series of regressions were performed on nicotine dependence and craving, with the personality characteristics as predictors. A model containing the personality characteristics explained a unique proportion of the variance in scores on both the FTND, F(4, 90) = 6.127, p < .001, R2 = .214, and on the HONC, F(4, 90) = 7.312, p < .001, R2 = .245 (see Table 9 and Table 10, respectively). Higher impulsive sensation seeking was associated with higher FTND and HONC scores. Personality characteristics also explained a unique proportion of the variance in scores on both the QSU, F(4, 90) = 6.149, p < .001, R2 = .215, and on the PANAS, F(4, 89) = 4.971, p < .01, R2 = .183 (see Table 11 and Table 12, respectively). Higher impulsive sensation seeking was associated with higher report of nicotine craving and increased negative state affect. Lower scores on BAS fun seeking were associated with increased negative affect. Finally, a model containing the nicotine dependence measures accounted for a unique proportion of the variance in scores on both the QSU, F(2, 96) = 131.679, p < .001, R2 = .733, and on the PANAS, F(2, 98) = 9.693, p < .001, R2 = .165 (See Table 13 and Table 14, respectively). Higher scores on the nicotine dependence measures were associated with increased self-reported nicotine craving and negative state affect. Regressions by Smoking Status Group. For the non-smokers, none of the regression analyses were significant. For the abstinent smokers, a model containing just the nicotine

34 dependence measures explained a unique proportion of the variance in scores on both the QSU, F(2, 21) = 3.996, p = .034, R2 = .276, and on the PANAS, F(2, 21) = 4.167, p = .030, R2 = .284 (See Table 15 and 16, respectively). Higher scores on the nicotine dependence measures were associated with increased self-reported nicotine craving and increase negative affect. For the ad libitum smokers, a model containing just the personality characteristics explained a unique proportion of the variance in scores on the IGT, F(4, 23) = 2.981, p = .040, R2 = .341 (See Table 17). Higher scores on BAS-Drive were associated with lower scores (i.e., poorer performance) on the IGT. Discussion The results of the present study run counter to numerous studies of substance dependence and risky decision making, yet are consistent with the mixed results often found in nicotine dependence. No differences were found between non-smokers, abstinent smokers, and ad libitum smokers on either the IGT or BART. Riskier decision making on the IGT and BART has consistently been found in other substances of dependence; however, the effects of nicotine, and nicotine satiation level, remain unclear. This inconsistent finding in comparison to other substances of dependence is unexpected, as most substances act on the same mesocorticolimbic pathway and therefore similar findings among substances are expected. Similarly, with the consistent findings of poorer performance on various other executive function tasks with nicotine abstinence, the present results do not fit. Significant group differences existed on nicotine craving, and smokers reported increased craving following exposure to a smoking-related cue. Thus, the manipulation of craving (abstinence and smoking cue) did appear to be effective in inducing craving in

35 the present study. However, based on the present findings, nicotine craving was not related to performance on measures of behavioral risky decision making. Negative affect, which has been shown in prior studies to be related to IGT performance, did not significantly differ between the abstinent and ad libitum smokers, even after presentation of the smoking cue. Although multiple exploratory models were examined in both smokers and nonsmokers, no causal models were found that adequately fit the present data and were interpretable for either group. A correlational model in the non-smoker group, in which the BAS subscales and performance on Blocks 3, 4, and 5 of the IGT were associated was found, pointing towards some relationship between these variables although not in the hypothesized form. Based on regression analyses, higher impulsive sensation seeking was associated with both higher nicotine dependence and higher craving. Thus, personality is associated with nicotine variables, consistent with prior literature (Leland & Paulus, 2005; Lipkus, Barefoot, Williams, & Siegler, 1994; Wills et al., 1994; Zuckerman, 2006). Additional regression analyses found only limited evidence of personality characteristics affecting BART and IGT performance, namely that higher BAS-drive in ad libitum smokers was associated with poorer performance on the IGT. These findings are in contrast with previous research indicating a consistent relationship between BAS, impulsive sensation seeking, and performance on the IGT (Crone et al., 2003; Davis et al., 2007; Franken & Muris, 2005; Suhr & Tsanadis, 2007; van Honk et al., 2002). Taken together, the results of the present study do not provide support for nicotine dependence or craving directly affecting risky decision making. In terms of personality

36 characteristics, one significant association was found for ad libitum smokers on the IGT. All of the remaining analyses were non-significant. However, the two groups of smokers scored significantly higher on a measure of impulsive sensation seeking than the nonsmokers, indicating that this premorbid personality characteristic may have affected some aspect of nicotine dependence (i.e., initiation or maintenance). The results of this study provide support for continued study of the complex relationships between dependence, craving, and personality characteristics. Coupled with the results of previous research showing a relationship between long-standing personality characteristics and performance on risky decision making tasks, the results of this study indicate the importance of assessing underlying personality characteristics when examining risky decision making performance in those who abuse substances, including nicotine. Many studies of the effects of substance dependence on IGT performance, both on abstinent and ad libitum substance users, have failed to include measures of personality characteristics that may be underlying these relationships. Future studies should also consider the influence of gender, which has been shown related to smoking behavior (Assaf, Parker, Lapane, McKenney, & Carleton, 2002; Cropsey, Linker, & Waite, 2008; Hsia & SpruijtMetz, 2008), and other personality characteristics that may be related to smoking behaviors, such as anxiety (Morisette, Tull, Gulliver, Kamholz, & Zimering, 2007). Limitations There were several important limitations that likely affected the present findings. The relatively small sample size prevented several hypotheses regarding the abstinent and ad libitum smoker model path coefficients from being fully examined, as the two groups

37 of smokers had to be combined into one group in order to have a large enough sample size to complete the SEM analysis. However, the relatively low effect sizes seen in the analysis of the first hypothesis may be indicating that there are no group differences between smokers and non-smokers on risky decision making tasks. In the present study, several exclusion criteria were used for the non-smoker group in order to gain a representative sample of never-smokers. Individuals were required to score a 0 on the FTND to be included in the study; however, several participants were removed from analysis due to endorsing recent or past smoking behaviors. This removal was conducted to keep clear boundaries between the non-smoker and smoker groups, but in future research a “past smoker” group may be worthy of inclusion to further assess the possible transience of nicotine’s effect on risky decision making. Among the abstinent and ad libitum smokers, the average FTND score fell in the low to moderate range of dependence. In other studies of nicotine dependence and executive functions, FTND scores were in general in the moderate to severe range. Further, the average age of participants in previous nicotine dependence studies has been in the 30s; in the present study it was less than 20. It is possible that the lower level of nicotine dependence, and/or the briefer history of nicotine use, may have impacted the results, and larger group differences would be seen with higher levels of dependence. Finally, it is possible that a self-selection bias occurred among those smokers who elected to sign-up for the study. As participants were notified in advance that random assignment to either abstain overnight or smoke as usual would occur, heavily nicotine

38 dependent individuals may have elected not to participate in the study. However, there is no way to determine the potential impact of this self-selection on the present findings, as no information was collected regarding the number of potential participants (and their FTND scores). Implications These results should be viewed as an exploratory examination of the relationships between nicotine dependence, craving, personality characteristics, and risky decision making. It is unclear to what extent the relatively small sample size may have affected the results. The regression analyses, which were not as affected by the low sample size, provided some limited support for the effects of personality characteristics on decision making among ad libitum smokers. However, the group comparison on IGT and BART scores indicates that there was no relationship between smoking status and risky decision making evident in this sample. The results provide support for the continued examination of long-standing personality characteristics in both the substance dependence and risky decision making research, as these three variables appear to have a complex relationship per this and other studies. The results of these studies have important implications for substance dependence treatment. Although no research has been conducted on such a topic to date, it is possible that poorer performance on risky decision making tasks may predict a lower likelihood of long-term substance abstinence. As a consistent relationship has been found between impulsive sensation seeking and the initiation and maintenance of substance use,

39 it is possible that this personality characteristic may also predict successful treatment outcomes.

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56 Table 1. Descriptive statistics for independent and dependent variables.

Variables Age Gender (% Male) WASI Breath CO FTND HONC BAS ImpSS QSU (T1) QSU (T2) QSU (T3) QSU (T4) PANAS-N (T1) PANAS-N (T2) PANAS-N (T3) PANAS-N (T4) IGT BART

Non-smokers (N = 48) N Mean SD 48 18.75 0.98 48 31.25%

Abstinent (N = 25) N Mean SD 25 19.52 2.8 25 40.00%

Ad libitum (N = 29) N Mean SD 29 19.72 3.28 29 37.93%

47 48 48 48 43 48 44 46 46 42 48

102.66 1.96 0 0 39.35 7.04 1.20 1.22 1.24 1.17 1.29

8.14 1.38 0 0 5.64 3.39 0.22 0.26 0.28 0.23 0.43

24 25 25 25 23 25 24 23 24 21 25

102.25 6.84 2.88 6.8 40.74 11.00 4.47 4.76 4.85 5.20 1.56

12.00 3.59 2.07 2.53 5.58 4.60 1.03 1.35 1.35 1.31 0.58

29 29 29 29 29 29 28 29 29 28 29

101.97 10.79 3.07 6.38 38.21 10.55 3.58 4.08 4.04 4.23 1.49

10.93 6.64 1.53 2.5 7.29 4.91 0.96 1.15 1.23 1.11 0.62

.956 .000*** .000* .000* .344 .000* .000** .000** .000** .000** .080

48

1.32

0.38

24

1.66

0.62

29

1.60

0.56

.009*

48

1.24

0.31

24

1.53

0.56

29

1.46

0.54

.020*

45

1.19

0.27

23

1.59

0.51

28

1.49

0.55

.001*

48 48

4.00 33.16

14.49 25 11.28 23

2.00 33.08

18.06 28 13.64 29

-1.07 33.59

15.67 .403 13.35 .986

P-value .159 .714

Note: WASI = Wechsler Abbreviated Scale of Intelligence; FTND = Fagerstrom Test of Nicotine Dependence; HONC = Hooked on Nicotine Checklist; BAS = Behavioral Activation System; ImpSS = Impulsive Sensation Seeking Subscale; QSU = Questionnaire on Smoking Urges; PANAS-N = Positive and Negative Affect Schedule, Negative Subscale; IGT = Iowa Gambling Task; BART = Balloon Analogue Risk Task. *

Non-smoker < Abstinent and Ad libitum smokers, Abstinent = Ad libitum smokers Non-smoker < Abstinent and Ad libitum smokers, Ad libitum < Abstinent smokers *** Non-smoker < Abstinent and Ad libitum smokers, Abstinent < Ad libitum smokers **

57 Table 2. Correlation matrix. Variables 1 1. FTND -2. HONC 3. BAS-R 4. BAS-F 5. BAS-D 6. ImpSS 7. QSU (T1) 8. QSU (T2) 9. QSU (T3) 10. QSU (T4) 11. PANAS-N (T1) 12. PANAS-N (T2) 13. PANAS-N (T3) 14. PANAS-N (T4) 15. IGT 16. BART

2 3 0.79* -0.24* -- -0.24* --

4 0.13 0.09 0.47* --

5 0.01 0.10 0.35* 0.39* --

6 7 0.10 0.76* 0.10 0.85* 0.08 -0.17 0.15 0.15 0.07 0.15 -0.09 --

8 0.81* 0.85* -0.20 0.08 0.13 0.10 0.95* --

9 10 0.77* 0.77* 0.85* 0.86* -0.15 -0.14 0.11 0.16 0.14 0.19 0.14 0.19 0.94* 0.93* 0.98* 0.97* -0.98* --

Note: FTND = Fagerstrom Test of Nicotine Dependence; HONC = Hooked on Nicotine Checklist; BAS = Behavioral Activation System; ImpSS = Impulsive Sensation Seeking Subscale; QSU = Questionnaire on Smoking Urges; PANAS-N = Positive and Negative Affect Schedule, Negative Subscale; IGT = Iowa Gambling Task; BART = Balloon Analogue Risk Task. *p < .01

58 Table 2. Correlation Matrix, cont. Variables 11 1. FTND 0.28* 2. HONC 0.37* 3. BAS-R - 0.12 4. BAS-F - 0.14 5. BAS-D - 0.02 6. ImpSS 0.16 7. QSU (T1) 0.34* 8. QSU (T2) 0.27* 9. QSU (T3) 0.26* 10. QSU (T4) 0.26* 11. PANAS-N (T1) -12. PANAS-N (T2) 13. PANAS-N (T3) 14. PANAS-N (T4) 15. IGT 16. BART

12 0.37* 0.37* -0.17 -0.18 -0.08 0.08 0.35* 0.40* 0.38* 0.40*

13 14 0.37* 0.41* 0.40* 0.48* -0.17 -0.05 -0.24* -0.09 -0.14 -0.04 0.13 0.14 0.31* 0.41* 0.35* 0.45* 0.35* 0.45* 0.35* 0.46*

15 -0.07 -0.09 -0.04 0.13 -0.04 0.13 -0.08 -0.05 -0.03 -0.07

16 0.08 0.04 -0.01 0.22* 0.08 -0.12 0.07 0.10 0.11 0.18

0.71* 0.78* 0.78* -0.13 -0.11 --

0.76*

0.73* -0.16 -0.01

--

0.84* -0.11 -0.08 --

-0.01 --

0.01 0.05 --

Note: FTND = Fagerstrom Test of Nicotine Dependence; HONC = Hooked on Nicotine Checklist; BAS = Behavioral Activation System; ImpSS = Impulsive Sensation Seeking Subscale; QSU = Questionnaire on Smoking Urges; PANAS-N = Positive and Negative Affect Schedule, Negative Subscale; IGT = Iowa Gambling Task; BART = Balloon Analogue Risk Task. *p < .01

59 Table 3. Unrotated factor loadings from confirmatory factor analyses: Non-smokers.

Variables 1. IGT 2. BART 3. BAS-F 4. BAS-D 5. BAS-R 6. ImpSS 7. HONC 8. FTND 9. QSU, Time 3 10. PANAS-N, Time 3

Decision Making Factor .726 -.726

Personality Factor

Dependence Factor

Craving Factor

.860 .793 .748 .158 * * .733 -.733

Note: IGT = Iowa Gambling Task, Blocks 4 and 5; BART = Balloon Analogue Risk Task, number of pumps adjusted for unpopped balloons; BAS-F = Behavioral Activation System, Fun Seeking subscale; BAS-D = Behavioral Activation System, Drive subscale; BAS-R = Behavioral Activation System, Reward Responsiveness subscale; ImpSS = Impulsive Sensation Seeking Subscale; HONC = Hooked on Nicotine Checklist; FTND = Fagerstrom Test of Nicotine Dependence; QSU = Questionnaire on Smoking Urges, Time 3; PANAS-N = Positive and Negative Affect Schedule, Negative, Time 3. *CFA could not be conducted on the nicotine dependence variables, due to the zero variance in scores on the FTND.

60 Table 4. Unrotated factor loadings from confirmatory factor analyses: Smokers.

Variables 1. IGT 2. BART 3. BAS-R 4. BAS-F 5. BAS-D 6. ImpSS 7. HONC 8. FTND 9. QSU, Time 3 10. PANAS-N, Time 3

Decision Making Factor .748 .748

Personality Factor

Dependence Factor

Craving Factor

.812 .766 .760 .309 .839 .839 .809 .809

Note: IGT = Iowa Gambling Task, Blocks 4 and 5; BART = Balloon Analogue Risk Task, number of pumps adjusted for unpopped balloons; BAS-R = Behavioral Activation System, Reward Responsiveness subscale; BAS-F = Behavioral Activation System, Fun Seeking subscale; BAS-D = Behavioral Activation System, Drive subscale; ImpSS = Impulsive Sensation Seeking Subscale; HONC = Hooked on Nicotine Checklist; FTND = Fagerstrom Test of Nicotine Dependence; QSU = Questionnaire on Smoking Urges, Time 3; PANAS-N = Positive and Negative Affect Schedule, Negative, Time 3.

61 Table 5. Correlations for IGT and BART variables by group. Non-Smokers Variables 1. IGT-1 2. IGT-2 3. IGT-3 4. IGT-4 5. IGT-5 6. BART-P 7. BART-E 8. BART-M

1 --

2 0.14 --

3 4 -0.03 -0.23 -0.75* 0.19 -0.32* --

5 0.01 0.46* 0.66* 0.41* --

6 7 8 -0.19 -0.25 -0.03 -0.10 -0.03 -0.13 -0.02 0.12 -0.11 -0.02 0.13 -0.16 -0.08 0.00 -0.06 -0.86* 0.86* -0.51* --

Note: IGT-1 = Iowa Gambling Task, Block 1; IGT-2 = Iowa Gambling Task, Block 2; IGT-3 = Iowa Gambling Task, Block 3; IGT-4 = Iowa Gambling Task, Block 4; IGT-5 = Iowa Gambling Task, Block 5; BART-P = Balloon Analogue Risk Task, number of pumps adjusted for unpopped balloons; BART-E = Balloon Analogue Risk Task, total explosions; BART-M = Balloon Analogue Risk Task, total money earned. *p < .05

Smokers Variables 1. IGT-1 2. IGT-2 3. IGT-3 4. IGT-4 5. IGT-5 6. BART-P 7. BART-E 8. BART-M

1 --

2 0.17 --

3 0.12 0.30* --

4 0.22 0.32* 0.52* --

5 0.15 0.22 0.34* 0.75* --

6 7 8 -0.11 -0.10 -0.17 -0.01 -0.03 0.02 0.06 -0.01 0.14 0.06 0.06 0.04 0.16 0.16 0.10 -0.92* 0.76* -0.49* --

Note: IGT-1 = Iowa Gambling Task, Block 1; IGT-2 = Iowa Gambling Task, Block 2; IGT-3 = Iowa Gambling Task, Block 3; IGT-4 = Iowa Gambling Task, Block 4; IGT-5 = Iowa Gambling Task, Block 5; BART-P = Balloon Analogue Risk Task, number of pumps adjusted for unpopped balloons; BART-E = Balloon Analogue Risk Task, total explosions; BART-M = Balloon Analogue Risk Task, total money earned. *p < .05

62 Table 6. Varimax rotation factor loadings for five factor solution: Non-smokers. Variables 1. BART-P 2. BART-E 3. BART-M 4. IGT-3 5. IGT-2 6. IGT-5 7. BAS-F 8. BAS-R 9. BAS-D 10. PANAS-N 11. IGT-1 12. IGT-4 13. ImpSS 14. QSU % variance explained

Factor 1 .979 .855 .848

Factor 2

Factor 3

Factor 4

.892 .840 .811

.308 .768 .738 .698

.415

23.10%

19.34%

Factor 5

.375

.625 .343

12.82%

9.99%

.750 .722 7.71%

Note: BART-P = Balloon Analogue Risk Task, number of pumps adjusted for unpopped balloons; BART-E = Balloon Analogue Risk Task, total number of explosions; BART-M = Balloon Analogue Risk Task, total money earned; IGT = Iowa Gambling Task, Blocks 1 through 5; BAS-F = Behavioral Activation System, Fun Seeking subscale; BAS-D = Behavioral Activation System, Drive subscale; BAS-R = Behavioral Activation System, Reward Responsiveness subscale; PANAS-N = Positive and Negative Affect Schedule, Negative, Time 3; ImpSS = Impulsive Sensation Seeking Subscale; QSU = Questionnaire on Smoking Urges, Time 3.

63 Table 7. Varimax rotation factor loadings for four factor solution: Smokers. Variables 1. BART-P 2. BART-E 3. BART-M 4. IGT-4 5. IGT-5 6. IGT-3 7. IGT-2 8. IGT-1 9. FTND 10. QSU 11. HONC 12. PANAS-N 13. BAS-F 14. BAS-R 15. BAS-D 16. ImpSS % variance explained

Factor 1 .952 .860 .819

Factor 2

Factor 3

Factor 4

.892 .806 .701 .459 .419 .709 .699 .698 .674 .850 .729 .651 .336 18.91%

16.28%

13.70%

10.34%

Note: BART-P = Balloon Analogue Risk Task, number of pumps adjusted for unpopped balloons; BART-E = Balloon Analogue Risk Task, total number of explosions; BART-M = Balloon Analogue Risk Task, total money earned; IGT = Iowa Gambling Task, Blocks 1 through 5; FTND = Fagerstrom Test of Nicotine Dependence; HONC = Hooked on Nicotine Checklist; PANAS-N = Positive and Negative Affect Schedule, Negative, Time 3; QSU = Questionnaire on Smoking Urges, Time 3 BAS-F = Behavioral Activation System, Fun Seeking subscale; BAS-D = Behavioral Activation System, Drive subscale; BAS-R = Behavioral Activation System, Reward Responsiveness subscale; ImpSS = Impulsive Sensation Seeking Subscale.

64 Table 8. Summary of fit indices for alternative model: Non-smokers. Model Fit Measures χ2 df p Normed Fit Index (NFI) Comparative Fit Index (CFI) PRATIO Parsimony-Adjusted Comparative Fit Index (PCFI) Root-Mean-Square Error of Approximation (RMSEA) RMSEA 90% Confidence Interval Akaike Information Criterion (AIC): Model Saturated Model Independence Model

14.518 9 0.105 0.822 0.917 0.600 0.550 0.118 0.000-0.225 38.518 42.000 93.396

65 Table 9. Regression of personality characteristics on FTND score. Variable Step 1 ImpSS BAS-R BAS-F BAS-D

B

SE B

0.166 -0.155 0.044 -0.053

0.053 0.080 0.105 0.087

β

R2 0.214

0.385* -0.229** 0.056 -0.065

Note: FTND = Fagerstrom Test of Nicotine Dependence; BAS-R = Behavioral Activation System, Reward Responsiveness; BAS-F = Behavioral Activation System, Fun Seeking; BAS-D = Behavioral Activation System, Drive; ImpSS = Impulsive Sensation Seeking Subscale. *p < .01 **p = .057

66 Table 10. Regression of personality characteristics on HONC score. Variable Step 1 ImpSS BAS-R BAS-F BAS-D

B

SE B

0.376 -0.244 -0.130 0.078

0.097 0.147 0.193 0.160

β

R2 0.245

0.464* -0.193 -0.089 0.051

Note: HONC = Hooked on Nicotine Checklist; BAS-R = Behavioral Activation System, Reward Responsiveness; BAS-F = Behavioral Activation System, Fun Seeking; BAS-D = Behavioral Activation System, Drive; ImpSS = Impulsive Sensation Seeking Subscale. *p < .01

67 Table 11. Regression of personality characteristics on QSU score. Variable Step 1 ImpSS BAS-R BAS-F BAS-D

B

SE B

0.195 -0.051 -0.110 0.048

0.049 0.075 0.098 0.081

β

R2 0.215

0.486* -0.081 -0.152 0.063

Note: QSU = Questionnaire on Smoking Urges, Time 3 measurement; BAS-R = Behavioral Activation System, Reward Responsiveness; BAS-F = Behavioral Activation System, Fun Seeking; BAS-D = Behavioral Activation System, Drive; ImpSS = Impulsive Sensation Seeking Subscale. *p < .01

68 Table 12. Regression of personality characteristics on PANAS-Negative score. Variable Step 1 ImpSS BAS-R BAS-F BAS-D

B

SE B

0.046 0.020 -0.089 -0.027

0.013 0.019 0.025 0.022

β

R2 0.183

0.445* 0.128 -0.486* -0.136

Note: PANAS-Negative = Positive and Negative Affect Schedule, Negative Affect, Time 3 measurement; BAS-R = Behavioral Activation System, Reward Responsiveness; BASF = Behavioral Activation System, Fun Seeking; BAS-D = Behavioral Activation System, Drive; ImpSS = Impulsive Sensation Seeking Subscale. *p < .01

69 Table 13. Regression of nicotine dependence on QSU score. Variable Step 1 FTND HONC

B 0.280 0.304

SE B 0.081 0.043

β

R2 0.733

0.296* 0.604*

Note: QSU = Questionnaire on Smoking Urges, Time 3 measurement; FTND = Fagerstrom Test of Nicotine Dependence; HONC = Hooked on Nicotine Checklist. *p < .01

70 Table 14. Regression of nicotine dependence on PANAS-Negative score. Variable Step 1 FTND HONC

B 0.034 0.035

SE B 0.035 0.019

β

R2 0.165

0.146 0.281*

Note: PANAS-Negative = Positive and Negative Affect Schedule, Negative Affect, Time 3 measurement; FTND = Fagerstrom Test of Nicotine Dependence; HONC = Hooked on Nicotine Checklist. *p = .064

71 Table 15. Regression of nicotine dependence on QSU score: Abstinent smokers. Variable Step 1 FTND HONC

B 0.255 0.119

SE B 0.139 0.117

β

R2 0.276

0.387* 0.215

Note: QSU = Questionnaire on Smoking Urges, Time 3 measurement; FTND = Fagerstrom Test of Nicotine Dependence; HONC = Hooked on Nicotine Checklist. *p = .081

72 Table 16. Regression of nicotine dependence on PANAS-Negative score: Abstinent smokers. Variable Step 1 FTND HONC

B 0.046 0.092

SE B 0.056 0.046

β

R2 0.284

0.175 0.426*

Note: PANAS-Negative = Positive and Negative Affect Schedule, Negative Affect, Time 3 measurement; FTND = Fagerstrom Test of Nicotine Dependence; HONC = Hooked on Nicotine Checklist. *p = .056

73 Table 17. Regression of personality characteristics on IGT performance: Ad libitum smokers. Variable Step 1 ImpSS BAS-R BAS-F BAS-D

B

SE B

0.027 -1.526 2.737 -4.447

0.609 0.861 1.477 1.725

β

R2 0.341

0.009 -0.390 0.495* -0.563**

Note: ImpSS = Impulsive Sensation Seeking subscale; BAS-R = Behavioral Activation System, Reward Responsiveness; BAS-F = Behavioral Activation System, Fun Seeking; BAS-D = Behavioral Activation System, Drive; IGT = Iowa Gambling Task. *p = .077 **p = .017

74 Figure 1. Hypothesized structural equation model.

75 Figure 2. IGT performance by smoking status.

4

3

2

1

Non-Smoker 0

Abstinent Smoker 1

2

3

-1

-2

-3

-4 IGT Block

4

5

Ad Libitum Smoker

76 Figure 3. BART performance by smoking status.

45

40

35

30

25

Non-Smoker Abstinent Smoker Ad Libitum Smoker

20

15

10

5

0 1

2 BART Trials

3

77 Figure 4. Regression weights for the confirmatory factor model: Non-smokers.

Note. P-values for the path coefficients are in parentheses.

78 Appendix A Methods Demographic and Smoking History Questionnaire. This questionnaire assessed basic demographic information and both current and past smoking habits. Smoking history questions were adapted from the National College Health Risk Behavior Survey (CDC, 2003), which was originally used to determine the occurrence of six categories of risky/unhealthy behaviors on college campuses: alcohol and drug use, tobacco use, physical inactivity, nutrition, unintentional and intentional injury, and sexual behaviors. Factor analyses have shown evidence of a 7-item smoking subscale on this measure (Buelow, 2005; Everett, Huston, Kann, Warren, Sharp, & Crossett, 1999), which was used in the present study. Internal consistency of this scale is high (α = .96; Buelow, 2005). Fagerstrom Test of Nicotine Dependence (FTND). The FTND assesses levels of nicotine dependence in smokers (e.g., Hertling, Ranskogler, Dvorak, Klingler, SaletuZyhlarz, Schoberberger, et al., 2005; Mogg, Field, & Bradley, 2005; Powell, Dawkins, & Davis, 2002). Scoring ranges from 0 (lower dependence) to 10 (higher dependence), with various methods used to classify severity of nicotine dependence (Mogg et al., 2005; Lesch et al., 2004; Hillemacher, Bayerlein, Wilhelm, Frieling, Thurauf, Ziegenbein, et al., 2006). For the purposes of the present study, a score of 3 or above was classified as moderate nicotine dependence. The FTND is based on the Fagerstrom Tolerance Questionnaire (FTQ), and was created to take into account psychometric concerns with this earlier instrument (Heatherton, Kozlowski, Frecker, & Fagerstrom, 1991). The original 10 items of the FTQ

79 were reduced to the 6 items of the FTND. The FTND correlates with other measures of tobacco use, including the Hooked on Nicotine Checklist (r = .44; Wellman, Savageau, Godiwala, Savageau, Friedman, Hazelton, et al., 2006) and self-reported number of cigarettes smoked per day (Mogg et al., 2005). Internal consistency ranges between α = .61 and α = .67, with differing opinions as to its acceptability (Heatherton et al., 1991; Mogg et al., 2005; Colby, Tiffany, Shiffman, & Niaura, 2000; Wellman, Savageau, et al., 2006). Piper and colleagues (2006) agreed that the internal consistency of the scale is low; however, they argue that the brevity of the instrument makes it a valuable research and clinical instrument. Hooked on Nicotine Checklist (HONC). The Hooked on Nicotine Checklist is a 10-item measure of nicotine dependence with high internal consistency in adolescents (α = .90 - .94; DiFranza, Savageau, Fletcher, Ockene, Rigotti, McNeill, Coleman, & Wood, 2002) and in adults (α = .82 - .83; Wellman, DiFranza, Savageau, Godiwala, Friedman, & Hazelton, 2005). 6-month test-retest reliability was high (r = .93), and scores are correlated with the reported number of cigarettes smoked per day (r = .69) and the number of days smoked in the past month (r = .68; Wellman, DiFranza, et al., 2005). PiCO MicroSmokerlyzer. The PiCO MicroSmokerlyzer is a carbon monoxide detection instrument developed for use in smoking cessation trials and research studies that provides a digital read out of breath carbon monoxide (CO) levels in parts per million (Bedfont Scientific Ltd). Measuring breath CO levels is a noninvasive, valid means of assessing recent smoking (Cropsey, Eldridge, Weaver, Villalobos, & Stitzer, 2006), and the Smokerlyzer products have been used in several previous studies requiring

80 nicotine abstinence (e.g., Mogg et al., 2005; Field & Duka, 2004; Taylor & Katomeri, 2006). The half-life of CO is approximately 2-5 hours (Powell et al., 2002). Bell and colleagues (1999) tracked changes in CO levels over 16 hours of abstinence, finding that CO levels average: 18.1 ppm after 4 hours, 11.1 ppm after 8 hours, and 6.7 ppm after 16 hours. A cut-off level of 10ppm was utilized to confirm overnight nicotine abstinence, consistent with recommendations from the manufacturer (Bedfont Scientific Ltd). CO measurements correlate with number of cigarettes smoked per day (r = .70), with FTND total score (r = .74), and with Tobacco Dependence Scale score (r = .49; Piper et al., 2004). Questionnaire on Smoking Urges (QSU). The Questionnaire on Smoking Urges was created to assess smoking urges and cravings (Tiffany & Drobes, 1991). This 32item measure has a two-factor structure: a factor regarding desire to smoke, and a factor regarding anticipation of relief from withdrawal symptoms (Tiffany & Drobes, 1991). The measure has high internal consistency (α = .93 - .95 for the two factors), and scores significantly increase with increased abstinence (Tiffany & Drobes, 1991). The QSU has been utilized in nicotine research to assess cravings in abstinent smokers and in smokers exposed to smoking cues (e.g., Mogg et al., 2005; Alessi, Badger, & Higgins, 2004; Powell et al., 2002). The QSU has also been used to track changes in craving after exposure to a smoking cue by administration of the instrument before, during, and after the cue exposure (Shiffman, Shadel, Niaura, Khayrallah, Horenby, Ryan, & Ferguson, 2003; Field & Duka, 2004).

81 Positive and Negative Affect Schedule (PANAS). The Positive and Negative Affect Schedule was created to assess a two-factor model of affect: positive and negative affect (Watson, Clark, & Tellegen, 1988). Internal consistency is high for both subscales (α = .89 Positive Affect, present moment; α = .85 Negative Affect, present moment; Watson et al., 1988), and test-retest reliability is moderate (8-week; r = .54 Positive Affect, r = .45 Negative Affect; Watson et al., 1988). Validity for the PANAS has been shown through correlations with the Beck Depression Inventory and the state subscale of the State Trait Anxiety Inventory (Watson et al., 1988). Higher scores on the negative affect subscale have been found following 12-hour nicotine abstinence (Leventhal, Waters, Boyd, Moolchan, Lerman, & Pickworth, 2007). BIS/BAS. The BIS/BAS was created to assess Gray’s theory of behavioral inhibition and behavioral activation systems (Carver & White, 1994). Individuals respond to the 24 items on a 4-point scale ranging from 1 (very true for me) to 4 (very false for me). Scores are reverse coded and summed on one of four factors: BIS, BAS-Drive, BAS-Fun Seeking, and BAS-Reward Responsiveness. Factor analyses have shown support for these four scales, and 8-week test-retest reliabilities have been moderate (r = .59 - .69; Carver & White, 1994). Internal consistency for the BIS has ranged from .75 to .76 (Meyer, Johnson, & Carver, 1999; Quilty, Oakman, & Farvolden, 2007), whereas internal consistency has been more variable on the BAS scales, ranging from .55 (BASReward Responsiveness; Knyazey & Slobodskoj-Plusnin, 2007) to .84 (BAS-Drive; Meyer et al., 1999). Correlations have been shown between the BAS factors and

82 performance on the IGT, as well as between BAS and measures of impulsivity and sensation seeking (Suhr & Tsanadis, 2007; Franken & Muris, 2005). Impulsive Sensation Seeking Scale of the Zuckerman-Kuhlman Personality Questionnaire (ImpSS). The ImpSS is a 19-item measure of the personality characteristics of impulsivity and sensation seeking (Zuckerman & Kuhlman, 2000). Participants respond to a series of statements such as “I like doing things just for the thrill of it” and “I sometimes do ‘crazy’ things just for fun” by indicating whether the statement is characteristic (“true”) or uncharacteristic (“false”) of himself or herself. The total score ranges from 0 to 19, with higher scores indicating a greater agreement with high sensation seeking items. The ImpSS has shown high internal consistency (α = .80; Ball, 1995) and high test-retest reliability (Zuckerman & Kuhlman, 2000). Validity of the ImpSS has been shown by a high correlation with the novelty seeking subscale of Cloninger’s Temperment and Character Inventory (r = .68; Zuckerman & Cloninger, 1996), and with the Sensation Seeking Scale Form V (r = .66; McDaniel & Zuckerman, 2003). Wechsler Abbreviated Scale of Intelligence (WASI). The Wechsler Abbreviated Scale of Intelligence is a brief measure of intelligence adapted from the Wechsler Adult Intelligence Scale (Wechsler, 1999). Two formats for the WASI are available: the foursubscale and the two-subscale format. In the present study, only the Vocabulary and Matrix Reasoning sections were administered in order to assess general cognitive abilities of participants, and to ensure that group differences in basic intellectual skills as they

83 have been shown related to other task performance. Test-retest reliability for the twoscale measure is high (α = .96; Wechsler, 1999). Iowa Gambling Task (IGT). The Iowa Gambling Task was created in the early 1990s as a means of assessing decision making deficits in a laboratory setting (Bechara, Damasio, Damasio, & Anderson, 1994). Individuals are given $2000 to start, and are told to select cards from one of four decks to maximize their profit. After each selection, participants are given feedback about the amount of money won and lost. Decks A and B yield an average profit of $100 each draw, and Decks C and D yield an average profit of $50 each draw. Losses can also occur. After 10 selections from Deck A or Deck B, individuals have incurred a net loss of $250. After 10 selections from Deck C or Deck D, individuals will instead have incurred a net gain of $250. Bechara and colleagues (1994) termed Decks A and B “disadvantageous” and Decks C and D “advantageous” in the long-term. Bechara and colleagues developed the IGT after discovering that individuals with frontal lobe damage had impairments in real-life decision-making, but passed measures of executive function in the laboratory. Previous research has found deficits on the IGT in individuals with frontal lobe damage (Bechara et al., 1994), substance abuse (Bechara & Martin, 2004), and pathological gambling (Goudriaan, Oosterlaan, de Beurs, & van den Brink, 2005). Results of neuroimaging studies provide support that increased activation to the prefrontal cortex is seen on the IGT (Clark & Manes, 2004; Fishbein, Eldreth, Hyde, Matochik, London, Contoreggi, et al., 2005). Validity has been shown through expected results in populations in which decision-making deficits are predicted (Buelow

84 & Suhr, 2009); however, little reliability data exists due to strong practice effects (Lejuez, Aklin, Zvolensky, & Pedulla, 2003). The IGT remains the most widely used behavioral measure of risk-taking and decision-making. The first 60 trials are associated with decisions made under ambiguity, whereas the later 40 trials are associated with decisions made under risk (Brand, Recknor, Grabenhorst, & Bechara, 2007). For the purposes of the present study, a total score was calculated by subtracting the total number of disadvantageous selections in the last 40 trials from the total number of advantageous selections in the last 40 trials. Balloon Analogue Risk Task (BART). The Balloon Analogue Risk Task was created to assess risk-taking behavior in a format other than self-report (Lejuez, Read, Kahler, Richards, Ramsey, Stuart, et al., 2002). The BART was intended to mimic realworld risk-taking, in that a behavior would be rewarded up until a point, after which repeated engagement in that behavior would lead to worse outcomes (Lejuez et al., 2002). The computerized task includes 30 balloons, or trials. On the computer screen a participant sees a balloon with a button below it stating “Press this button to pump up the balloon,” a button stating “press to collect $$$,” and two tallies for money earned: total money earned and money earned on the last balloon. Each click on the pump inflates the balloon and adds 5 cents to the participant’s total. This amount is not shown on the screen. Participants are instructed to pump the balloon as much as they would like, and to press the “collect $$$” money to transfer the money accumulated on that trial to their account. Collecting the money ends each trial. Each balloon has an individual break point, at which time a pop-sound would be heard, the money earned but not collected

85 would be lost, and the next trial would begin. On each additional pump, the amount that could be lost if the balloon explodes increases whereas the relative gain of any additional pump decreases (Lejuez et al., 2002). Participants are not provided with any information about the likelihood that the balloon will pop on a given trial (Lejuez et al., 2002). Probability of the balloon popping is set to 1/128 for the first pump. If the balloon does not pop on the first pump, the probability of popping on the second trial is set to 1/127, and so on. The average number of pumps until popping has been found to be 64 (Hopko, Lejuez, Daughters, Aklin, Osborne, Simmons, et al., 2006). Researchers have used several aspects of the BART as dependent variables, including the average number of pumps taking into account only the balloons that did not explode, the average number of pumps in general, and the total number of explosions (Hopko et al., 2006). In the proposed study, the average number of pumps adjusted for only unexploded balloons was used. Validity for the BART has been shown through correlations with measures of sensation seeking and impulsivity (Lejuez et al., 2002; Hunt, Hopko, Bare, Lejuez, & Robinsons, 2005), and no significant correlations with age, intelligence, depression, and empathy (Lejuez, Aklin, Jones, et al., 2003; Lejuez et al., 2002). Correlations have been found between scores on the BART and polysubstance use (Hopko et al., 2006), and smoking and other risky behaviors (Lejuez et al., 2002; Lejuez, Aklin, Jones, et al., 2003; Lejuez, Aklin, Zvolensky, et al., 2003). In a sample of smokers and non-smokers, the BART differentiated between the groups whereas no group differences were seen on the IGT (Lejuez, Aklin, Jones, et al., 2003).