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Executive function and future orientation moderate the relationship among substance use associations… Barkowsky, Deborah Suzanne 2013

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EXECUTIVE FUNCTION AND FUTURE ORIENTATION MODERATE THE RELATIONSHIP AMONG SUBSTANCE USE ASSOCIATIONS AND OUTCOME EXPECTANCIES WITH SUBSTANCE USE IN ADOLESCENTS: A PILOT STUDY  by   Deborah Suzanne Barkowsky B.A., Kwantlen Polytechnic University, 2009    A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF   MASTER OF ARTS  in   THE COLLEGE OF GRADUATE STUDIES  (Psychology)  THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) September 2013   ? Deborah Suzanne Barkowsky, 2013  ii Abstract The overall purpose of the present study was to investigate the moderating role of Executive Function and Future Orientation in the relationship between implicit associations/explicit outcome expectancies and substance use in grade 8 adolescent students. Participants from grade 8 (13-14 years old) completed cued association tasks that measure implicit substance use associations and explicit substance use outcome expectancies. They then completed substance use measures, as well as three measures of substance use problems: the CRAFFT, AUDIT, and CUDIT.  In a separate session, participants completed the Substance Use Risk Profile Scales (SURPS), and the Executive Function measures of response inhibition (Go/No-Go task), working memory (Self-Ordered Pointing Task,SOPT), and reward sensitivity ( the Donkey Task, a revised version of the Iowa Gambling Task). Lastly, they completed the Future Orientation Questionnaire. Moderation analyses were conducted using Generalized Linear Regression (GLzM). Results confirmed previous research showing that associations and outcome expectancies, as well as Impulsivity, predict substance use and abuse. The findings suggested moderation effects of response inhibition (Go/No-Go performance) and reward sensitivity (Donkey task performance).  Contrary to previous reports working memory (SOPT performance) did not moderate implicit memory association effects. In a novel demonstration, Future Orientation has moderating effect on associations and outcome expectancies with substance use in adolescents. Overall, the results suggest that the relationship between the development of executive functions and future orientation with associative learning is important in the development of adolescent substance use and abuse.  The intention is that these findings will provide insights useful for the development of prevention programs.   iii Preface The research presented in this thesis was conducted with the approval of the University of British Columbia Okanagan?s Behavioural Research Ethics Board, under Ethics Certificate H10-01963. The data was collected at Spring Valley middle school in Kelowna, British Columbia. I was responsible for collecting and uploading the EF tasks, revising the Future Orientation task from a pencil-and-paper task to an online task, recruiting participants, and administering measures to participants. The data analyses were conducted by Dr. Krank and me. iv TABLE OF CONTENTS Abstract  .......................................................................................................................... ii Preface  ......................................................................................................................... iii Table of Contents ............................................................................................................. iv List of Tables ..................................................................................................................... vi List of Figures ................................................................................................................. viii List of Abbreviations .......................................................................................................... x Acknowledgements .......................................................................................................... xi Dedication ....................................................................................................................... xii 1 Introduction ................................................................................................................ 1  1.1     Overview ........................................................................................................... 1  1.2 The Dual Process Model of Substance Use in Adolescents ................................... 5  1.2.1   Dual Process Models ........................................................................................................ 5  1.2.2   Assessing the Reflexive Processing System ...................................................................... 6  1.2.3   Reflective Processing System - Executive Function .......................................................... 8  1.3     Brain structures involved in EF processes ............................................................ 9  1.3.1   Dopamine in EF ............................................................................................................... 11  1.3.2   The Prefrontal Cortex and Impulsivity ............................................................................ 11  1.4     Reward and Future Orientation ........................................................................ 13  1.5     Processes Involved in Executive Function ......................................................... 15   1.5.1   Working Memory and Attention .................................................................................... 15   1.5.1.1  Substance Effects on WM ..................................................................................... 16  1.5.2   Task Shifting .................................................................................................................... 17  1.6     Executive Function Measures  .......................................................................... 18  1.6.1   Cued Go/No-Go Task ...................................................................................................... 19  1.6.2   Self-Ordered Pointing Task ............................................................................................. 20  1.6.3   Iowa Gambling Task (Donkey Task) ................................................................................ 21  1.6.4   Future Orientation Questionnaire .................................................................................. 24 1.7 Predictions ....................................................................................................... 25 2   METHODS ................................................................................................................... 30  2.1     Participants ...................................................................................................... 30  2.2     Procedures ......................................................................................................  30  2.2.1   Measures Used ............................................................................................................... 31   2.2.1.1   Substance Use Measures ....................................................................................... 31    2.2.1.1.1   Substance Use Problems ................................................................................ 32  v    2.2.1.1.1.1   CRAFFT  .................................................................................................. 32    2.2.1.1.1.2   AUDIT  ... ??????????????????????????????????.. 33    2.2.1.1.1.3   CUDIT ???? ....... ????????????????????????????.?. 33    2.2.1.1.2   Substance Use Associations  . ???????????????????????.??.  34    2.2.1.1.3   Self-coded emotion and situation associations ? .. ?????????????... 35    2.2.1.1.4   Substance use outcome expectancy liking ? .... ???????????????? 35     2.2.1.1.4.1   [Implicit] Word Associations, [Explicit] Expectancies, and     [Explicit] Expectancy Liking  ....................................................................................... 36    2.2.1.1.5   Impulsivity Scale - Substance Use Risk Profile Scale ...................................... 38   2.2.1.2   Executive Function Measures ................................................................................. 39    2.2.1.2.1   Go/No-Go Task ............................................................................................... 39    2.2.1.2.2   Self-Ordered Pointing Task (SOPT)  ................................................................ 40    2.2.1.2.3   Iowa Gambling Task - Donkey Version  .......................................................... 41    2.2.1.2.4   Future Orientation Questionnaire ................................................................. 42 3 RESULTS  ................................................................................................................... 44  3.1 Outliers ............................................................................................................ 44   3.2 Descriptive Statistics ........................................................................................ 44   3.2.1   Measures of Executive Function, Future Orientation, and Impulsivity .......................... 44  3.3 Measures of alcohol use, abuse, and associations ............................................. 46  3.4 Measures of cannabis use, abuse, and associations  .......................................... 46  3.5 Generalized Linear Regression Analyses  ........................................................... 50  3.5.1   Moderation of word association and outcome expectancy by Executive Function ...... 59  3.5.2   Moderation of word association and outcome expectancy by the Future Orientation   and Impulsivity ......................................................................................................................... 66 4 DISCUSSION  ............................................................................................................. 78 5 CONCLUSION AND CLINICAL IMPLICATIONS  .............................................................. 94 6 REFERENCES  ............................................................................................................. 96 7   APPENDIX  .............................................................................................................. 119    vi List Of Tables Table 1.   Cognitive and Substance Use Measures Used ................................................ 18 Table 2.   Means, standard deviations and correlations among cognitive and  behavioral control measures (independent variables)  ..................................... 45 Table 3.    Means, standard deviations and correlations among measures of alcohol use and risk, associations and outcome expectancies......................... 48 Table 4.    Means, standard deviations and correlations among measures of  cannabis use, cannabis abuse, and cannabis associations ............................. 49 Table 5.    Bivariate analyses of scores for each EF measure, FO subscale,  and SURPS Impulsivity on each outcome measure ......................................... 52 Table 6.    Main effects beta values for Total Associates scores and Outcome  Expectancy Liking scores associating each outcome measure using a generalized linear model ............................................................................... 53 Table 7.    Main effects and interactions of Go/No-Go scores with Total  Associates and Outcome Expectancy Liking scores for all outcome Measures ........................................................................................................ 61 Table 8.    Main effects and interactions of SOPT scores with Total  Associates and Outcome Expectancy Liking scores for all outcome Measures ........................................................................................................ 63    vii Table 9.    Main effects and interactions of DONKEY scores with Total Associates and Outcome Expectancy Liking scores for all outcome  measures ........................................................................................................ 64 Table 10.  Main effects and interactions of Planning Ahead scores with Total  Associates and Outcome Expectancy Liking scores for all outcome Measures ........................................................................................................ 67 Table 11.  Main effects and interactions of Anticipating Consequences scores with Total Associates and Outcome Expectancy Liking scores for all outcome measures .......................................................................................... 71 Table 12.  Main effects and interactions of Time Perspective scores with Total Associates and Outcome Expectancy Liking scores for all  outcome measures .......................................................................................... 75 Table 13.  Main effects and interactions of SURPS Impulsivity scores with  Total Associates and Outcome Expectancy Liking scores for  all outcome measures ..................................................................................... 77     viii List Of Figures Figure 1.     Theoretical relationship expected among implicit substance use   associations, EF and FO, and substance use ???????????? 27 Figure 2.  Theoretical relationship expected among explicit substance use   outcome expectancies, EF and FO, and substance use ??????? 29 Figure 3A.    Cannabis use scores for Cannabis use as a function of   associates and expectancies ??????????????????? 54 Figure 3B.    CUDIT scores for Cannabis use as a function of   associates and expectancies ??????????????????? 55 Figure 4A      Drank Alcohol\ and Drunkenness scores for Alcohol Use as a function of associates and expectancies ??????????????????? 56 Figure 4B. AUDIT scores for Alcohol Use as a function of associates and  expectancies ??????????????????????????.. 57 Figure 4C. CRAFFT scores for Alcohol Use as a function of associates and  expectancies ??????????????????????????. 58 Figure 5.     Trend towards an Interaction between GNG scores and Total   Associates scores on CUDIT scores ???????????????... 62 Figure 6.    Interaction between DONKEY scores and Total Associates scores  on CRAFFT scores ???????????????????????.. 65 Figure 7.      Interaction between Planning Ahead scores and Total Associates   scores on Alcohol Use scores ??????????????????? 68  ix Figure 8.      Interaction between Planning Ahead scores and Total Associates   scores on AUDIT scores ?????????????????????. 69 Figure 9.      Interaction between Planning Ahead scores and Cannabis Total   Associates scores on Cannabis Use scores  ????????????.. 70 Figure 10.    Interaction between Anticipating Consequences scores and Cannabis   Total Associates scores on Cannabis Use scores ??????????. 73 Figure 11.    Interaction between Anticipating Consequences scores and Outcome   Expectancy Liking scores on CUDIT score ??????????..??.. 74  x LIST OF ABBREVIATIONS ACC  Anterior Cingulate Cortex dACC  Dorsal Anterior Cingulate Cortex vACC  Ventral Anterior Cingulate Cortex AUDIT  Alcohol Use Disorders Identification Test CRAFFT Measure of alcohol and cannabis use CUDIT  Cannabis Use Disorders Identification Test DA  Dopamine DONKEY Donkey Task EF  Executive Function FO  Future Orientation GLzM  Generalized Linear Model GNG  Go/No-Go Task IGT  Iowa Gambling Task OEL  Outcome Expectancy Liking PFC  Prefrontal Cortex PHARM Profile of Healthy Activities and Risk Monitoring SOPT  Self-Ordered Pointing Task SURPS Substance Use Risk Profile Scale TA Total combined Word Associates+Situation Associates+Outcome Asssociates WM  Working Memory   xi ACKNOWLEDGEMENTS I would like to start by expressing my appreciation to my committee for their input and support throughout this thesis process. I thank Dr. Krank for taking me under your wing and introducing me to this field, providing me with extensive knowledge and research experience, and for allowing me creative freedom; Dr. Holtzman for your continuous encouragement and input, and for challenging me to maintain high levels of critical thought and writing; and Dr. Walsh for instructing me in methods of analysis and for your consistent encouragement throughout my program. I would like to thank IMPART for providing me with a fellowship and funding that enabled me to focus on studying, and for the opportunity to sharpen my research skills and expand my knowledge alongside fellow researchers. Thank you also to Jesus, for giving me a creative intellect and providing opportunities for me to develop and enjoy it; to every member of my extended families who have each supported me in your unique way; Kerry, for your mentorship; Sara, Penny, Sandra, Christina, Kirsten and Brenda for your spirit-lifting friendship; Max, Tash, and Hanie - patchos and psychobabble; Hank, for our sanctuary discussions about faith; and Liz, for convincing me that I could succeed in the field of psychology. Mostly, a zillion2 merci beaucoups and je t?aimes to my family Jana, Kadyn, and Kevin for your capacious love and support on this and all of our adventures. Code cabin.   xii DEDICATION   To Kadyn and Jana   1  INTRODUCTION 1.1 Overview   The transition from adolescence, the developmental period between 13 to 18 years old, to emerging adulthood is a particularly vulnerable time for individuals. Successful navigation through this developmental phase requires effective decision-making and goal-oriented behaviour to deal with increasing external influences and social demands while accomplishing important developmental tasks such as developing personal identity (Arnett, 2000; Erickson, 1980; Whitbourne, Sneed, & Sayer, 2009) and coping with emerging sexuality (Halpern, Udry, Campbell, & Suchindran, 1993). During this period, many adolescents encounter risky opportunities that could negatively affect all aspects of their future. One of the most prominent of these is substance use (Johnston, O?Malley, Bachman, & Schulenberg, 2010). Responses to opportunities for substance abuse vary among adolescents - some initiate substance use, while others do not (Keating, 2004).  Many choose to drink alcohol, but some do so responsibly and some dangerously.  Others choose to smoke cannabis, on its own or in conjunction with other substances, and still others choose to use one or more other illicit substance. Recent research and theoretical advances suggest that these potentially dangerous choices are impacted by learned associations from their social environment as well as the neurodevelopment of executive capacities in the frontal lobes (Conklin, Luciana, Hooper, & Yarger, 2007; Krank, 2010; Luna, Thulborn, Munoz et al., 2001; Monasterksy, 2007; Pharo, Sim, Graham, Gross, & Hayne, 2011).   Accumulating research on adolescent and adult substance use supports the important role of substance use associations (implicit memory associations and explicit outcome expectancies) on the initiation, escalation, and maintenance of substance use.  Research findings support a dual processing approach that includes both implicit or automatic  Page 2    influences and explicit or controlled influences of these associations.  The relevant associations contain specific information about when and where substance use may occur and about the effects of substance use. More information about substance use and more associations with positive outcomes contribute to earlier use, more frequent and heavy use, and more substance use problems in adolescents (Fulton, Krank, & Stewart, 2012; van der Vorst, Engels, Meeus, Dekovic, & Leeuwe, 2012).  Recent studies further show that such associations mediate the strong effects of social influence on adolescent use (van der Vorst et al., 2012).   In addition, recent advances in understanding the neurodevelopment of executive functions suggest that the relative immaturity of these late developing processing abilities contribute to the risk of adolescent substance use (Peeters, Wiers, Monshouwer et al., 2012).   Executive functions can have a moderating effect on the development of substance use.  Specifically, high levels of executive function may be protective.  Moreover, these functions appear to moderate the impact of substance use associations; high levels of executive function reduce the impact of implicit substance use associations (Peeters et al., 2012).  Nevertheless, this research is in the early stages of development with only two published studies documenting this moderation effect. These previous studies looking at this relationship have examined a single executive function, working memory. There is a need to explore the range of executive functions that may have important influences on substance use behavior. This thesis examines the moderating effect of a number of executive functions on substance use associations and their impact on substance use in adolescents.   In addition, this thesis extends the theoretical discussion and empirical domain of the role of control functions to adolescent future orientation (FO). FO refers to the motivation to plan action and analyze consequences. FO, as a motivational construct, differs from, but requires the use of, executive functions such as working memory, behavioral control, and delay of reward (Steinberg, Graham, O?Brien, et al., 2009).This thesis contributes to the field  Page 3    by extending previous research on associations and outcome expectancies, yet is unique because it is the first study to investigate the potential moderating role of FO on that relationship. The investigations of FO in particular are novel and lay the groundwork for future studies in this area.   Adolescence is a particularly important time to study substance use as it is the most common stage of first substance use experimentation and escalated use.  Correspondingly, it is also the time of great change in substance use associations with a rapid transition from negative associations to more positive associations in many youth (Fulton et al., 2012).  In addition, adolescents are also going through major neural changes in the development of executive functions located in the frontal cortex (Blakemore & Choudhury, 2006). For these reasons, the interaction between dual processing systems (which involve explicit processing systems and implicit processing systems) and substance use is particularly important in adolescents. These rapidly changing cognitive and behavioural conditions represent an ideal context to test the interactions expected by dual process theories.  The rapidly changing cognitions about substance use arise as adolescents are increasingly exposed to information about drug and alcohol use that affects their associations and perception of substances and their use.  An adolescent may learn positive associations with alcohol based on television programs that depict young adults having fun, laughing, hanging out with friends over a drink of alcohol. Alternately, an adolescent may develop negative associations from situations in which they see alcohol have a negative effect on a person or situations. For example, they may have parents who do not consume alcohol or who say that drinking alcohol is not a positive or beneficial behavior, they may have parents who become aggressive towards others after they have consumed alcohol, or they might witness a friend?s death due to drunk driving or alcohol poisoning. Such associations can be classified into two categories: 1) situation associations, which provide information about when and where drug use may occur, and 2) outcome expectancies,  Page 4    which provide information about anticipated, or expected, consequences of substance use.  It has been well-established that both situation associations and outcome expectancies are strongly associated with the initiation of substance use and continued use and abuse (Ames, Grenard, Thush, et al., 2007; Frigon & Krank, 2009; Goldman, Darks, & DelBoca, 1999; Wall, Hinson, McKee, & Goldstein, 2001; Wiers, Stacy, Ames et al., 2002). Theorists applying the principles of dual process approaches argue that substance use associations may provide impulses (automatic memory associations) or reasons (reflective outcome expectancies) to engage in substance use behaviors. From the dual processing perspective, it is important to note that these associations are learned along with the development of important cognitive processes; adolescents are developing executive functions that are controlled by the frontal cortex (Giedd, Blumenthal, Jeffries et al., 1999; Giedd, 2004; Luna et al., 2001; Blakemore & Choudhury, 2006). These executive functions are brain systems central to regulating behavioural control and consequently to moderating substance abuse (Giancola & Tarter, 1999; Keating, 2004). Planning, delaying reward, and inhibiting impulsive responses are some of these functions.  Adolescents may use these control processes to resist the temptations provided by drugs and alcohol (Wiers & Stacy, 2010; Wiers, Ames, Hofmann, Krank, & Stacy, 2010). In addition to their developmental changes in executive function (EF) capacity, adolescents are more susceptible than adults to the positive rewards of substance use because they are characteristically driven by immediate rewards (sensation-seeking) more than adults and less able to delay or control impulses (impulsivity) (Giedd et al.,1999; Luna et al., 2001; 2005; Krank, 2010; Krank, Stewart, O?Connor et al., 2011; Van Leijenhorst, Moor, OpdeMacks et al., 2010a).  Automatic impulses to use, induced by positive substance use associations, contrast with future oriented motivations and the control processes that serve these motivations.  These conflicting processes of automatic impulses and future orientation with executive  Page 5    control are captured in dual process models of substance use (Barrett, Tugade, & Engle, 2004; Carver, 2005; Chaiken & Trope, 1999; Evans, 2008; Tiffany, 1990).  These models suggest that an adolescent with a strong EF system and future orientation is better able to respond with reason rather than emotion in difficult situations and to put off the desire for immediate reward in order to obtain long-term goals. Consequently, recent research suggests that high levels of EF may suppress the effect of implicit associations on adolescent substance use.  These predictions have been confirmed in the interaction of working memory capacity with alcohol use associations; strong working memory reduces the impact of implicit alcohol associations on alcohol use and problems. (Grenard, Ames, Wiers et al., 2008; Thush, Wiers, Ames et al., 2008).  Although not yet tested, the dual process approach also predicts that strong FO will also moderate the impulsive effects of substance use associations.  Dual process theories predict that higher levels of executive function and higher levels of future orientation will reduce the positive relationships between substance use associations and substance use in adolescents (see Figures 1 and 2 for more specific details on the predictions). 1.2   The Dual Process Model of Substance Use in Adolescents 1.2.1 Dual Process Models The dual process model posits that impulsive behaviour in adolescents is the joint outcome of two developing, imbalanced systems: a hyperactive reflexive, reward-driven system and a limited reflective, harm-avoidant and regulatory/execution function (EF) system (Graf, 1994; Kahneman & Frederick, 2002; Smith & DeCoster, 2000; Strack & Deutsch, 2004; Wiers & Stacy, 2010). Reflexive processing is automatic, fast, and occurs without the intervention of EF (Greenwald, Nosek, & Banaji, 2003); whereas, reflective processing is slow, deliberate, and requires more cognitive effort than reflexive processing (Baddeley & Logie, 1999). These systems work together yet have differences worth noting.  Page 6    The reflexive system involves implicit or automatic processing based on associative memories, of which one is often unaware, such as when a child learns that alcohol consumption in social situations is acceptable because their parents drink alcohol when visiting with friends. Although the child does not intentionally activate these associations, they occur throughout their daily life and are retained in the child?s memories. The associations formed and strengthened during development motivate individuals and guide their behaviour quite automatically (Stacy, 1997).For example, implicit alcohol-related associations have been shown to predict current and potential alcohol use in adolescents (Krank & Goldstein, 2006; Krank & Wall, 2006; Stacy, 1997; Thush & Wiers, 2007; Wiers et al., 2002).   The reflective system, on the other hand, refers to insightful, explicit, or controlled processing involved in goal-directed behaviour. For example, an adolescent weighing the pros and cons regarding cannabis use is engaging in reflective processing, particularly if future goals at being kept in mind.  The reflective system?s effect on substance related choices and behaviour, particularly in relation to one?s substance use associations, is of interest in this study.  Reflective processing, also referred to as EF, takes longer, requires more effort, and is more deliberate than reflexive processing (Baddeley & Logie, 1999). Reflexive processing, often referred to as implicit cognition, is fast, requires fewer cognitive resources, and occurs without the intervention of deliberate EF (Kahneman & Frederick, 2002; Greenwald et al., 2003).  1.2.2 Assessing the Reflexive Processing System  Reflexive processing first depends on an individual?s learning history (Krank, 2010; Sillke, Siegle, Whalen et al., 2009). During early childhood development, implicit associations are formed and strengthened through one?s observation of substance use  Page 7    behaviours and opinions of individuals, such as relatives or friends, who have an influence on them. These associations motivate individuals and guide their behaviour quite automatically (Stacy, 1997).  To determine how these implicit, reflexive associations are influenced by an individual?s EF, it is first necessary to measure those associations. These are best assessed with indirect methods, such as rapid and indirect word association tasks, which minimize the effects of explicit or reflective processing on reflexive processing (Stacy, Ames, and Leigh, 2004). These tasks allow the investigator to study associations involving substance use behaviours in comparison to other possibilities, even though the other options are not presented explicitly. Stacy, Ames, Sussman, & Dent (1996) developed a cued-association method in which researchers present participants with ambiguous cue words beside which participants write down the first word that comes to mind. For example, fun:___; bud:___. Responses (eg. ?drunk? or ?high?) are then coded and totalled to form a scale that can be used to predict substance use behaviour.  Alcohol and cannabis-related associations have been shown to predict current and prospective alcohol use in adolescents (Krank & Wall, 2006; Stacy, 1997; Thush and Wiers, 2007; Wiers et al., 2002).  These effects have been interpreted as the result of impulsive behaviors resulting from automatic activation of positive substance use associations (Wiers et al., 2011).  Wiers and colleagues (2011) have further argued that executive function would moderate these effects. Specifically, strong executive control processes would be expected to reduce the impact of reflexive impulses (see Figure 1). Other methods of assessing substance use associations are more direct and less indicative of automatic processing.  In particular, substance use outcome expectancies are assessed by asking what the individual would expect or anticipate to happen if they used a substance. This type of question engages a more reflective process and thus may influence substance use behavior in a more deliberate or planned fashion.  For example, explicitly expecting that drinking alcohol would have positive social consequences might actually  Page 8    encourage reflective planning to drink.  Thus, outcome expectancies assessed directly are not as reflexive or automatic.  Indeed, Wiers and colleagues (2011) have suggested that these associations are more influential when control processes are strong.   1.2.3 Reflective Processing System - Executive Function Although these associative measures effectively predict substance use, proponents of the dual process models propose that the influence of implicit cognitions on substance use behaviour should also be dependent on an individual?s level of reflective processing, or EF (Stacy, Ames, & Knowlton, 2004; Wiers, Bartholow, van den Wildenberg et al., 2007). As mentioned, the EF system involves reflective, explicit, and controlled processing. This reflective system includes working memory (WM) (Baddeley & Logie, 1999), inhibitory attention (Hester & Garavan, 2005), and task-switching (Tanji, Shima, & Mushiake, 2007).  Executive control processes should have a moderating role in adolescent substance use initiation and disorders; they are required for an adolescent to facilitate behaviour control and manage new situations by anticipating, planning and adapting to change (Giancola and Tarter, 1999; Mezzacappa, Kindlon, & Earls, 2001). This suggests that individuals who have high levels of EF should show less impulsively driven behaviour because they are able to successfully inhibit or counteract the impact of associative (?reward?) processing on their behaviour. To continue the example of the adolescent from above, one with a high level of EF is likely to stay focused on long-term goals whether or not they choose to engage in cannabis use; whereas, an adolescent with a low level of EF may be less likely to remain focussed on long-term goals or control substance use behaviors, and use may escalate to risky use or abuse. In this thesis, we also examine the corollary hypothesis that strong FO motivations should similarly enhance long-term goals and use of control to reduce impulsive substance use.  Impulsivity is associated with the inability to stop initiated actions, intolerance to delay,  Page 9    reward sensitivity, and lack of consideration of further consequences of action; whereas, sensation-seeking is indicated by a strong tendency to seek exciting and possibly risky rewards (Whiteside & Lynam, 2009). Higher levels of impulsivity and sensation-seeking have been associated with substance use (Crews & Boettiger, 2009; Ferrett, Cuzen, Thomas, et al., 2011; Krank, Schoenfeld, & Frigon, 2010; Strack & Deutsch, 2004; Verdejo-Garcia, Lawrence, & Clark, 2008; Wiers et al., 2010). Impulsivity is a multifaceted construct and we choose not to include it as a moderating variable based on previous study findings that high levels of impulsivity are not predictive of strong impulses to engage in behavior unless they are motivated (Hofmann et al., 2008; Friese and Hofmann, 2009). Impulsivity is typically measured with questionnaires, with answers based on self-perception; the issue of validity may be problematic (Goldstein, Craig, Bechara et al., 2009). Situations involving stress, hunger, and fatigue promote impulsivity (Sher, Bartholow, Peuser et al., 2007; Nederkoorn, Baltus, Guerrieri et al., 2009; Baumeister, 2003) and these factors may have had an influence on performance in this study despite best efforts to control for them. Wiers et al. (2010) confirmed these findings and further revealed that the predictive power of trait impulsivity disappears once associations, EF, and their interactions are included in a regression equation. Finally, EF has been shown to control impulses (Crews & Boettiger, 2009; Wiers et al., 2010). For these reasons, we did not include impulsivity as a moderating variable. 1.3 Brain structures involved in EF processes EF occurs in the prefrontal cortex (PFC) area of the brain that continues to develop well into adulthood (Giedd, 2004; Giedd et al., 1999)  The PFC activates and inhibits attentional processing and conflict monitoring in the anterior cingulate cortex (ACC), located near the PFC (Bush, Lun & Posner, 2000). The ACC provides a critical pathway for attention (Posner, Petersen, Fox, & Raichle, 1988; Frith, Friston, Liddle, & Frackowiak, 1991),   Page 10    working memory (Petit, Courtney, Ungerleider, & Haxby, 1998), and motivation factors (Knutson, Westdorp, Kaiser & Hommer, 2000) to influence behaviour by evaluating stress or conflict in the environment and signalling other structures to further process and respond (Botvinick, Braver, Barch et al., 2001). The dorsal ACC (dACC) evaluates cognitive information and the ventral ACC (vACC) evaluates emotional or motivational information, such as reward (Bush et al., 2000; Devinsky, Morrell, & Vogt, 1995; Drevets & Raichle, 1998; Vogt, Finch, & Olson, 1992; Whalen, Bush, McNally et al., 1998). When the dACC and vACC are simultaneously presented with conflicting information, emotional or motivational processing is prioritized over cognitive performance (Dennis & Chen, 2007), resulting in decreased performance on tasks that require EF or behavioural control. Consequently, in a situation that involves immediate reward, adolescents with high EF may be more likely than those with low EF to maintain cognitive and behavioural control and less likely to exhibit risk-taking behaviour. The ACC is highly activated during situations involving anxiety and stress (Pruessner, Dedovic, Khalili-Mahani et al., 2008), rejection, and negative self-relevant trait words (Moran, Macrae, Heatherton et al., 2006). Furthermore, activation is greater in adolescents compared to adults during social situations involving high-risk decision-making, guilt, or embarrassment (Burnett, Bird, Moll, et al., 2009; vanLeijenhorst et al., 2010; vanLeijenhorst, Zolie, van Meel et al, 2010). George, Ketter, Parekh et al. (1995) found greater activation of the ACC in depressed females, and Blankstein et al. (2009) measured higher grey matter volumes near the vACC in females with high levels of neuroticism which may be due to less myelination in this area. Taken together, the findings of these studies suggest that the adolescent developing brain is highly sensitive to behavioural or emotional risks.   Page 11    1.3.1 Dopamine in EF  This sensitivity is partially due to changing dopamine (DA) levels, which influence thought patterns and behaviours decisions involving risk. During this time the number of DA receptors, particularly D1 and D2, reach a peak and then begin to decline, which may help to explain elevated levels of impulsivity and bias towards motivational stimuli  (Andersen, Thompson, Rutstein et al., 2000; Brenhouse, Sonntag, & Andersen, 2008). D1 receptors are associated with focusing behaviour and responding to motivational stimuli in the environment (Kalivas, Volkow, & Seamans, 2005); whereas, D2 receptors are associated with high-speed information processing, which is necessary in task-switching and reversal-learning (Ernst et al., 2009), and partly explains why adolescents can learn and perform tasks quickly yet may respond too quickly and fail to consider all perspectives before acting. For these reasons, maintaining goal-focused thoughts and behaviours can be very challenging during this stage of development.  1.3.2 The Prefrontal Cortex and Impulsivity The increase in DA levels during adolescence occurs in the PFC, the site of cognitive control. Cognitive control is an EF process critical for modifying behaviour after unfavourable outcomes such as response errors, or unpredicted reward or punishment (Holroyd & Coles, 2002). The PFC is also the site of emotional regulation. Research has shown that Individuals who have experienced damage to the PFC show decreased EF abilities, shown by altered emotional regulation, abnormal social functioning, and poor decision-making (Bechara, 2005; Bechara, Damasio, Damasio, & Anderson, 1994; Bechara, Damasio, & Damasio, 2000). A lack of healthy PFC development may underlie impaired cognitive control and emotional regulation, resulting in decreased resistance to, and increased consumption of, addictive substances. Therefore, low levels of EF lead to greater  Page 12    substance use and abuse, and greater substance use leads to lower levels of EF (Squeglia, Jacobus & Tapert, 2009). Other studies provide evidence for dysfunction of EF processes in chronic drug users due to neural damage in the orbitofrontal cortex of the PFC, an area involved in spatial planning, reward processing, and decision-making during working memory (WM) processes (Baker, Rogers, Own et al., 1996; Manes, Sahakian, Clerk et al., 2002; Ersche, Fletcher, Lewis et al., 2005; Paulus, Hozack, Zauscher et al., 2002; Thush et al., 2008). Chronic drug use has damaging effects on the fully developed PFC of an adult so it is likely that chronic drug use will have greater negative effects on the developing PFC of an adolescent. Ersche, Clark, London, Robbins, & Sahakian (2006) provided insight into the relationship of substance use and dysfunctional EF processes in adults by utilizing task-shifting measures. EF tasks with associate and outcome expectancy tasks were administered to adolescents in order to investigate whether adolescents who initiate substance use or display chronic substance use have lower initial EF compared to those who do not use substances, and/or if substance use decreases EF abilities over time. Tapert, Schweinsburg, Barlet et al. (2004) revealed a bidirectional relationship between alcohol and EF, such that low levels of EF can be a risk factor in the development of alcohol dependence; alternately, alcohol modifies processing on EF tasks. DeBellis, Clark, Beers et al. (2000) argue that alcohol negatively affects brain maturation, and several other studies posit that alcohol damages the PFC, decreasing inhibition and attentional control and leading to continued alcohol use (Crews, Braun, Hoplight et al., 2000; Wiers et al., 2007). Thush et al. (2008) propose that these adolescents may start with poorer EF and begin drinking at an early age. During adolescence, when brain regions associated with EF are still developing, alcohol induced damage in the PFC can lead to problems in inhibition and behavioural control which can then lead to increased alcohol use (Crews et al., 2000; Wiers et al., 2007).  Page 13    1.4 Reward and Future Orientation During adolescence, sensitivity to rewards and incentives increases dramatically, and motivational cues of potential reward are particularly salient. Therefore, adolescents are highly vulnerable to the effects of reward and anticipation of reward (incentive) due to the increases in DA in their brain, and these increases are related to the prediction, anticipation, and/or receipt of reward (Schultz et al., 2000; Lu, Grimm, Shahm & Hope, 2003). Moreover, most adolescents have a combination of high reward sensitivity and weak future orientation, increasing the tendency to make ineffective choices regarding long-term, goal-directed behaviour. Future orientation (FO) involves weighing the costs and benefits of future outcomes compared to immediate rewards and the extent to which an individual believes they have control over their future well-being based on their current decisions and planning (Cauffman & Steinberg, 2000; Green, Myerson, Lichtman et al.,1996; McCabe & Barnett 2000; Lessing, 1972; Nurmi, 1989). The Iowa Gambling Task is commonly used to measure reward sensitivity. Participants are required to make reward-based decisions even though they are not able to determine the future or unseen reward; lower performance indicates impaired EF processes (Bechara et al., 1994). In this task, participants can choose from four decks of cards that result in either gain or high loss. As the game progresses, participants with high levels of EF should be able to figure out which decks result in high gain and to choose from those decks in order to finish with the highest number of points possible. When performing this task, individuals with weak PFC abilities continue to make risky decisions even after significant losses; this may be due to lower EF levels of reversal learning and adaptation to changing stimulus-reward associations.  Cauffman, Shulman, Steinberg et al. (2010) investigated age differences in IGT performance. A modified version of the IGT was used (Peters & Slovic, 2000) in order to  Page 14    ensure that performance differences were not due to age differences but due to level of EF processing.  Participants (N=901) were between the ages of 10 and 30 years old. Data analyses examined the differences in developmental trajectories within an individual?s scores, and compared across seven age groups: 10-11 years, 12-13 years, 14-15, 16-17, 18-21, 22-25, and 26-30 years old. A linear age pattern related to performance revealed that adolescents were quicker to shift to advantageous decks but adults were quicker to shift away from less advantageous decks.  As well, adolescents made disproportionately more risky decisions than adults when reward feedback was immediately provided compared to when reward feedback was held until afterward.  This study provides further evidence that adolescents are more likely to make choices based on immediate rather than long-term rewards; therefore, when presented with an opportunity for risky substance use, high levels of future orientation and EF may overrule reward-driven decisions that are prevalent in this stage of development. Substantial increases in the ability to foresee long-term consequences occur between the ages of 11-13 years old and at 16 years old (Grisso, Steinberg, Woolard et al., 2003). Many adolescents exhibit low levels of EF by risky decision-making and impulsive actions but those who engage in high-risk behaviours score lower on FO than their peers (Cauffman, Steinberg, & Piquero, 2005; Steinberg et al., 2009).  In a recent pilot study by Steinberg et al. (2009), a 15-item self-report measure of FO along with a delay discounting task (the ability to withhold immediate action for a small reward in anticipation of a larger reward gained in the future) was administered to 935 high school and undergraduate students between the ages of 10 to 30 years old. Supporting an earlier study by Grisso et al. (2003), Steinberg et al. (2009) revealed that the preference for larger delayed rewards and the ability to anticipate future consequences both develop between 13 and 16 years old. Analyses also indicated that adolescents have lower FO scores than adults, and that adolescent females score higher than males on all FO subscales, suggesting that gender is  Page 15    partly responsible for FO differences. Steinberg et al. (2009) conclude that adolescents high in FO should be able to forego the immediate rewards of substance use for the future rewards involved in abstinence and reaching long-term goals. 1.5 Processes Involved in Executive Function The main processes involved in EF include Working Memory and Attention, Task Shifting, and Behavioral Control.  1.5.1 Working Memory and Attention A key process of EF is working memory (WM). WM involves storing and ongoing manipulation of information and retention of this information during distraction (Baddeley & Hitch, 1994), and is proposed to be a combination of the EF processes of inhibition and task switching (Miyake, Friedman, Emerson et al., 2000). According to Finn & Hall (2004), individuals with low WM exhibit decreased ability to ignore extraneous stimuli and focus on goal-relevant stimuli. In social or peer pressure situations, an adolescent with low WM is more likely to be distracted by salient, immediate-reward information and less likely to focus on the long-term effects of their substance use or abstinence (Stacey et al., 2004; Wiers et al., 2007). Consequently, when presented with the opportunity for substance use, more intentional and effortful processing may be required  to make an appropriate decision.  Similarly, Steinberg et al. (2009) posit that adolescents who are able to anticipate future outcomes and consequences are more likely to forego immediate reward and remain focused on the reward of working toward and attaining long-term goals. Other studies have found that individuals with damage to the orbitofrontal cortex in the PFC have lowered WM capacity and are unable to anticipate future outcomes (Fellows & Farah, 2005; McClure et al., 2004; Semendeferi, Armstrong, Schleicher et al., 2001).   Page 16    1.5.1.1 Substance Effects on WM Studies investigating the neurocognitive effects of adolescent cannabis use have revealed learning and memory impairments in cannabis users (Fried et al., 2005; Schwartz, Gruenewald, Klitzner, & Fedio, 1989), higher risk-taking and low sensitivity to punishment (Wesley, Hanlon, & Porrino, 2011), lower attention and working memory performance (Jacobsen, Mencl, Westerveld & Pugh, 2004; Jacobsen, Pugh, Constable et al., 2007; Schweinsburg, Nagel, Schweinsburg et al., 2008). Imaging research has revealed under-activation in the ACC during decision-making and working memory tasks (Wesley et al., 2011; Schweinsburg et al., 2008), increased ACC activation with slower conflict resolution (Abdullaev, Posner, Nunnally et al., 2010), increased PFC activation during a novel WM task (Jager, Block, Liujten, & Ramsey, 2010; Schweinsburg, Schweinsburg, Cheung, et al., 2005), and lower levels of myelination in the PFC (Bava, Frank, McQueeny et al., 2009; Bava, Jacobus, Thayer, & Tapert, 2013). Studies also reveal that earlier engagement in cannabis use is correlated with lower attention and verbal skills (Ehrenreich, Rinn, Kunert, et al., 1999; Fontes, Bolla, Cunha, et al. 2011; Gruber, Sagar, Dahlgren, et al., 2012; Pope, Gruber, Hudson, et al., 2001). Other studies have investigated the neurocognitive effects of alcohol use during adolescence. Bava et al. (2013) found a correlation between greater alcohol use during mid- to late adolescence and compromised myelination in the PFC fiber tract related to working memory (Brauer, Anwander, & Friederic, 2011).  Studies using fMRI have shown lowered ACC and PFC activation, resulting in decreased inhibition (Mahmood, Goldenberg, Thayer, et al., 2013; Norman, Pulido, Squeglia, et al., 2011). Grenard, Ames, Weirs et al. (2008) investigated the moderating role of WM in the relationship among outcome expectancies and alcohol use. Participants (M age = 16.71y) completed word associations that measure spontaneous (implicit) drug-related associations, and self-reports about perceived frequency of drug use in self, parents, and peers.  Page 17    Participants also completed the SOPT measure of WM. Stronger relationships between drug-relevant associations and alcohol use occurred among adolescents with low WM scores compared to those with high WM scores. As well, implicit positive outcome expectancies (OE) predicted alcohol use more strongly in adolescents with lower WM; whereas, explicit OE?s predicted alcohol use more strongly in adolescents with higher WM capacity. These findings support the dual-process perspective that the influence of implicit associations on substance users is stronger in adolescents with low WM than those with high WM (Thush et al., 2008). Therefore, adolescents who have low WM and positive alcohol associations and OE?s are more likely to drink alcohol because they perceive the effects as positive and rewarding, yet are not able to keep long-term goals in mind while distracted by possible immediate reward; whereas, those with high WM are less likely to be influenced by implicit associations or the rewarding effects of alcohol use. These adolescents can maintain their focus on future goals and consequences and perceive those rewards as more worthwhile than immediate rewards involved in substance use. 1.5.2 Task Shifting Task shifting is an EF process that involves shifting between operations or inhibiting previously activated mental states.  Individuals with high EF are able to learn new rules quickly and successfully switch between tasks. Task shifting improves with development, until early adolescence when it slows down while accuracy increases due to the developing EF system (Jones, Folk & Rapp, 2009). An important component of task shifting, called reversal learning, occurs when an individual alters their established responses as reward options change.  Using a confirmatory factor analyses to extract latent variables from EF tasks, Huizinga, Dolan,& van der Molen (2006) administered nine EF tasks to participants. Age differences in task shifting, WM, and inhibition, in participants aged 7 years, 11 years, 15  Page 18    years, and 21 years. Results showed that WM abilities increased between 7 to 15 years old, when they reached adult levels. Task shifting and inhibition decreased steadily between 7 to 15 years old, when they reached adult levels and remained at that level to 21 years old. For task shifting in particular, the perceived cost of shifting decreased with age: 7 year-olds perceived the cost to be larger than did 11 year-olds, and 11 year-olds considered the cost to be larger than 15 year-olds. The 15 year-olds did not differ from 21-year olds, which indicates that adult levels were reached at 15 years old. 1.6 Executive Function Measures When choosing the EF and FO measures, I considered both validity and entertainment. I chose the following EF measures because they are well-known and often used in studies, and they seemed like tasks that adolescents would enjoy playing. If the participants do not enjoy a game, it is likely that they will give up which will lead to outlier scores that have to be removed and thereby weaken the analysis; however, if participants get a sense of playfulness, they may be more likely to make an effort. In particular, the Donkey task (Crone and van der Molen, 2004) is a child?s version of the IGT; even though it is a bit too young for the participants, they did seem to enjoy playing it and most completed all 400 trials. I chose the FO task because it had not previously been administered in a study that included substance use measures, and therefore was a new direction of research.  Table 1. Cognitive and Substance Use Measures Used Measures Used  Association Measures  1. Word and Situation Associates Implicit/indirect associations 2. Expectancy Outcome Liking Explicit/direct associations Substance Use Measures  3.    SURPS Frequency and recency of substance use  Page 19    4.    CRAFFT Alcohol/Cannabis use, risk of abuse 5.    AUDIT Alcohol use and risk of abuse 6.    CUDIT Cannabis use and risk of abuse Executive Function Measures  7.   Cued Go/No-Go Inhibitory Control 8. Self-Ordered Pointing Task Working Memory 9. Donkey Task (Iowa Gambling Task) Reward sensitivity, Task Shifting 10.  Future Orientation         - Planning Ahead         - Anticipating Consequences         - Time Perspective  Planning future actions/goals Anticipating future consequences Ability to envision future self  1.6.1 Cued Go/No-Go Task The cued Go/No-Go Task measures the ability of an individual to focus on a task while ignoring irrelevant distracting stimuli (Rubenstein, Meyer & Evans, 2001). This ability is called inhibitory control, and requires an individual to continually change or shift goals and to activate new rules in order to monitor and update WM content. Goal-shifting requires the use of WM to decide between possible responses at the onset of a predictive cue (Bunge, Hazeltine, Scanlon et al., 2002); whereas, rule activation requires inhibition of WM to decide between a response or inhibition of response at the onset of a target (Aron, Robbins, & Poldrack, 2004). For example, in the Cued Go/No-Go task, the participant needs to determine which cue predicts the ?go? target and which cue predicts the ?no-go? target, and to be prepared to respond correctly to the changing cues. For each ?go? cue, the individual must respond to the target but for each ?no-go? cue, the individual must inhibit a response to the target. The challenge lies in changing rules quickly depending on the predictive cue. Archibald and Kerns (1999) found a correlation between WM and inhibition performance on the Cued Go/No-Go and SOPT tasks. In a later study, Cragg & Nation (2007) administered the SOPT task to children between 6 to 11 years old and to young adults (M age=19 years). Compared to younger children, older children had higher WM  Page 20    performance and a better ability to inhibit interference from previous memory traces. In both children and young adults, errors increased as matrix size increased. Further testing and analysis showed that error and span performance correlated highly, which suggests that both of these are involved in WM. 1.6.2 Self-Ordered Pointing Task  Success on the Self-Ordered Pointing Task (SOPT) requiring WM is determined by the sequence of responses correctly performed by the participant. Participants are shown a matrix of pictures and instructed to choose a picture. The pictures are then scrambled and the matrix is shown again to the participant who is required to choose a new picture each time the matrix is presented, while keeping track of the pictures already chosen. As participants work through the task, they are required to develop a strategy for future actions based on previous actions.  Fuster (1985) proposed three processes that are involved when using WM in a task such as this: retrospective memory that recalls previous information to guide goal-directed decisions; prospective memory that acts on anticipated information not yet concretely attained in order to provide anticipatory thinking to guide actions; and an interference control mechanism that blocks irrelevant external information which may interrupt the process of attaining the current goal. Prospective memory failures may be due to low levels of EF (West, 1996). The Fuster (1985) model may explain the relationship between EF and the WM construct, as we see evidenced by tasks that measure planning and strategizing. Studies have revealed age differences in WM development. Brocki and Bohlin (2004) revealed that each of the EF processes seem to have different developmental trajectories and occur in developmental stages: early childhood (6 to 8 years old), middle childhood (9 to 12 years old), and adolescence. Levin, Culhane, Hartmann et al. (1991) found that errors of omission and commission for both genders tend to decline between 7 to 12 years old,  Page 21    with no further decrease in 13 to 15 year-olds. Speed or reaction time tends to reach peak levels between 7.6 to 9.5 years and then again between 9.6 to 11.5 years old. Further, response inhibition and WM abilities mature at about 10 years old, a time when individuals change strategies from visual to phonological information storage, which improves recall  (Brocki & Bohlin, 2004; Hitch, Halliday, Shaafstal et al., 1988; Welsh, Pennington, & Groisser, 1991; Williams, Ponesse, Schachar et al., 1999). Lastly, gender differences are evident: females between 6 to 13 years old commit more errors of omission and display slower reaction times than males. Brock & Bohlin (2004) suggest that these response differences occur because females may be more cautious in answering, or perhaps due to differences in self-regulation development. 1.6.3 Iowa Gambling Task (Donkey Task) The Iowa Gambling Task (IGT) measures inhibition and task shifting. It was designed to resemble real-life decision-making under conditions of uncertainty, and examines the role of emotion in decision-making (Bechara et al., 1994; Bowman and Turnbull, 2004). Busemeyer & Townsend (1993) propose that standard decision-making tasks and the IGT differ in that standard decision-making tasks provide reward probabilities but in the IGT the probabilities have to be learned and inferred from past experience. Damasio (1996) posits that decision-making during uncertainty is an interaction that occurs in WM; the process is guided by a somatic marker, an association between a reinforcing stimuli and an affect or psychological state. The mechanism allows an individual to evaluate his/her emotional experience of a previous outcome, and then to use this memory to perceive and evaluate future situations and exhibit advantageous goal-directed behaviour. For example, Crone and vanderMolen (2004) and Hooper, Luciana, Conklin, & Yarger (2004) revealed that IGT scores improve significantly and steadily from early adolescence to adulthood, suggesting  Page 22    that individuals make increasingly more advantageous decisions as they experience and learn from more life situations over time. The IGT differentiates between sensitivity to future consequences versus sensitivity to reward or insensitivity to punishment (Bechara, Tranel, & Damasio, 2000), which requires inductive reasoning, tracking one?s performance on the task via gains and losses, and integrating gain/loss feedback into a decision-making rule (Crone & vanderMolen, 2004). Bechara et al. (1994) designed the IGT task in order to measure PFC deficits in patients with brain damage; performance on the IGT was lower in patients with PFC damage and also lower in individuals with low levels of EF compared to individuals without PFC damage or high levels of EF. Bechara & Martin (2004) later revealed that individuals with PFC damage have difficulties with real-life decision-making tasks but not WM tasks, indicating reduced sensitivity to future punishment or rewards. Similarly, Businelle, Apperson, Kendzor et al. (2008) have found that individuals who abuse substances actually tend to prefer the disadvantageous decks, demonstrating that they are similar to PFC-damaged patients who are unable to switch choices to the advantageous deck even after they are shown that they are making disadvantageous choices and should switch decks. Bechara & Martin (2004) later used the IGT to conduct a further study on individuals without PFC damage. They administered the IGT and a separate card task, both of which measure decision-making and WM, to two groups of individuals 18 years and older: one group of individuals with substance dependence and a control group who were not substance users. Compared to the control group, the substance dependent group performed below normal levels on decision-making and WM. The only gender differences occurred in the control group, where males performed better than females on decision-making only. Bechara & Martin (2004) concluded that impaired decision-making is not due to an inability to remember information for a short period of time; rather, it is due to the process of WM operating on stored information. When WM activates a goal-oriented action,  Page 23    other actions are automatically inhibited from entering WM but when those other actions interact and accumulate, inhibition is compromised due to excess WM demands. Evidence of a taxed WM is displayed by perseverative errors or errors of commission (Jones et al., 2009).  Since WM is a process involved in EF, it is desirable for adolescents to have as high EF as possible because they need to be able to develop and stay focused on long-term goals as they experience possible distractions, and to over-rule positive associations and outcome expectances and the immediate rewards involved in peer and social acceptance and substance use.  While some studies find support for an effect of nicotine on IGT performance, some researchers such as Businelle et al. (2008) argue that nicotine use should be controlled for when investigating the relationship among IGT scores and substance use because some individuals who engage in nicotine use also engage in drug or alcohol use. Therefore, one cannot be sure that nicotine influences IGT performance because performance may be dually influenced by both the nicotine and drugs/alcohol or independently influenced by the drugs or alcohol and not the nicotine. Businelle et al. (2008) argue that nicotine use does not have a negative effect on IGT scores; rather, it is the other substances that have the negative effect. Businelle et al. (2008) examined the IGT performance of individuals aged 25 years and older who either smoke and have a substance use dependency, smoke but do not have a substance use dependency, never smoked but have a substance use dependency, or never smoked and do not have a substance use dependency. Results showed equal performance levels between individuals with a substance use disorder who smoke and those with a substance use disorder who have never smoked; a participant?s smoking status did not have an effect on IGT performance, suggesting that nicotine did not have an effect on EF. A limitation to this study is that it was conducted on adults and may not be generalizable to an adolescent sample who are in a stage of PFC development.  Page 24    Nonetheless, the findings of this study support the rationale to focus the investigation of this study on alcohol and cannabis use. 1.6.4 Future Orientation Questionnaire Very few studies have been conducted on Future Orientation (FO). Most of the research has focused on the relationship among age differences and future orientation development: future orientation tends to increase throughout adolescence (Cauffman & Steinberg, 2000). Nurmi (1992) found that older adolescents are more likely than younger adolescents to consider and plan developmental tasks such as post-secondary education and career choices, and to discuss future-oriented emotions such as hope and fear. However, Steinberg et al. (2009) revealed that on the future orientation questionnaire, adolescents between 12 to 15 years old display a negative slope in planning ahead and positive slopes for both time perspective and anticipation of consequences. These findings indicate that the ability to plan ahead decreases between the ages of 12 to 15 years, whereas the ability to see oneself in future years and to anticipate consequences increases during the ages of 12 to 15 years old.  It is important to acknowledge that the design of my study does not test the direction of causal effects between FO and substance use.   I focussed on investigation of the moderating role of FO rather than viewing and measuring FO as changeable due to substance use. Although we measure correlations rather than causality, the theory tested here proposes that FO influences decision-making (Steinberg et al., 2009) and therefore would play a predictive role in substance use. Further research should involve studies that provide insights into the causal relationship among FO and substance use in adolescents.   Page 25    1.7 Predictions To date, the majority of research regarding dual processing, substance use, and EF has been conducted on adults; there is a paucity of research measuring EF and FO in adolescents and/or measuring the moderating role of EF and FO on associations and outcome expectances and substance use in adolescents. The uniqueness of this study is twofold: we investigated EF and FO together in one study, and we investigated the moderating roles of EF and FO in the relationship among associations and expectancies and alcohol and cannabis use in adolescents. To do this, we administered tasks that measure implicit associations and outcome expectancies, alcohol and cannabis use and abuse, impulsivity, and tasks that measures EF and FO to Grade 8 students at a middle school in Kelowna, British Columbia (see Table 1).  We chose to measure alcohol and cannabis, as opposed to other substances, because these are the most commonly used substances in this age group and so are the best substances to measure change in use.  Dual processing theories of substance use suggest that substance use depends on an interaction between automatic associations of substance use with positive or negative outcomes and the moderating effects of reflective processing (see Figure 1).  Thus, an individual with less positive associations (low associations) would not be likely to engage in substance use.  There is simply no impulse or incentive to act.  However, an individual with high associations with substance use and more positive impressions of substance use outcomes would be more inclined to use.  Here is where reflection can exert a moderating influence.  An individual with strong motivation and ability to use their reflective system (Control processes high) is more likely to intervene to counteract these impulses with planning and critical analysis than an individual with less reflective abilities (Control processes low).  Thus, we expect that substance use should reveal an interaction between level of positive substance use associations and the level or motivation to plan and analyze  Page 26    (Figure 1).  We predict that this same relationship will hold for future orientation with higher future orientation providing a protective influence against the automatic effects of substance use associations. Page 27     *Substance use units are arbitrary and intended only to indicate the direction of predicted effects across a range of possible substance use and abuse measures. Figure 1:  Theoretical relationship expected among implicit substance use associations, EF      and FO, and substance use   Page 28    According to Wiers et al. (2011) this interactive relationship is reversed for explicit outcome expectancies.  Greater control processes would, in fact, enhance the implementation of reflective expectations.  Figure 2 shows the predicted interaction.  There is a main effect of outcome expectancies with stronger associations associated with more substance use (Association high), but this effect is stronger in individuals with higher levels of executive function (Control processes high). Hypotheses: 1. Higher levels of alcohol and cannabis use will be positively associated with higher levels of implicit associations with each drug respectively. (Main effect shown in Figure 1) 2. Higher levels of alcohol and cannabis use will be positively associated with higher levels of explicit outcome expectancies with each drug respectively. (Main effect shown in Figure 2) 3. High EF will reduce the effect of high positive associations (Control processes high in Figure 1). 4. High  EF will increase the effect of high positive outcome expectancies (Control processes high in Figure 2). 5. Higher future orientation will reduce the effect of high positive associations (Control processes high in Figure 1). 6. Higher future orientation will increase the effect of high positive outcome expectancies (Control processes high in Figure 2). 7. Greater impulsivity is associated with higher substance use but does not moderate the relationship between associations or outcome expectancies and substance use (cf Krank et al., 2011 and Wiers et al., 2011). 8. Higher levels of Future Orientation will have a main effect to reduce alcohol and marijuana use, especially problem use.  Page 29       *Substance use units are arbitrary and intended only to indicate the direction of predicted effects across a range of possible substance use and abuse measures. Figure 2:  Theoretical relationship expected among explicit substance use outcome      expectancies, EF and FO, and substance use   Page 30    2 METHODS 2.1 Participants Participants for this thesis study were grade eight students who attended Spring Valley middle school located in a neighbourhood with low- to mid-socioeconomic status. This sample was taken from a larger group of students who participated in the Profile of Healthy Activities and Risk Monitoring (PHARM), assessing substance use (tobacco, alcohol, marijuana, polysubstance) and substance use associations of students in grades seven to nine in several middle schools in School District #23. In the PHARM portion of this study, participants completed the substance use measures: last time used; frequency; quantity; the CRAFFT, a measure of alcohol and cannabis abuse; Alcohol Use Disorders Identification Test (AUDIT); Cannabis Use Disorders Identification Test (CUDIT); substance use associations; substance use outcome expectancies; and the SURPS Impulsivity scale. In the thesis portion of the study, participants completed the Go/No-Go, SOPT, DONKEY Task, and the Future Orientation questionnaire. Included participants for the analysis were those who completed the thesis portion of the study and completed the PHARM assessment (N = 92; Males = 47).  This sample excluded 13 students who completed the EF and FO tasks, but did not complete the PHARM assessment.  All sampling methods and procedures were reviewed and approved by the Research Ethics Board of the University of British Columbia under the Canadian Research Ethics Guidelines of the Tri-councils. 2.2 Procedures PHARM participants who wished to participate in this study were required to return signed student consent and parental assent forms. Each participant independently completed all tasks on a personal computer while seated with their classmates in the classroom or library, supervised by the class teacher and the student researcher.  Page 31    Maintaining the regular atmosphere and schedule for students should keep stress levels from increasing, and therefore not influence performance. We used a web-based program that presented questions, and tracked participant data that could later be put into a report. This has been shown to be a reliable delivery method that makes data collection and maintenance more manageable (Ames, Gallaher, Sun et al., 2005). Participants were each identified by an identification code that allowed the data to be linked across surveys but preserved the participants? anonymity.  At the start of class, the student researcher distributed identification codes and read the EF measure instructions out loud to the participants who were also instructed to carefully read through the instructions displayed on the screen at the start of each task, and to press the spacebar to begin. At the completion of a task, the score was displayed on the computer. The participant could then press the space bar to continue, bringing up the instructions for the next task, until all four tasks were completed. Each task was timed, and total time for completion was one hour, after which participants were debriefed and dismissed from the classroom. The tasks were developed using Inquisit software, with the data saved in SPSS format for statistical analysis.  SPSS version 19.0 was used to run all analyses.  2.2.1 Measures Used The following substance use, abuse, and cognitive association measures were administered in the PHARM, a 3-year longitudinal study in which this thesis study was nested. The data from these measures were used in the analyses together with the EF  data collected for this sample. 2.2.1.1 Substance Use Measures Alcohol and cannabis use were assessed using standardized questions administered in a conditional hierarchy (Frigon & Krank, 2009). Each student was first asked if they had  Page 32    ever used a drug. Multiple drugs were assessed in the study but only the data for alcohol and cannabis use was considered. If the participant answered yes, they were then asked how recently they had used a substance (i.e., ?When was the last time you used [each drug]?) with the response options: never, more than a year ago, in the past year, in the past month, and in the past week. In addition, participants were asked to indicate when they had last been ?drunk? (self-defined). The scores were tabulated as never = 0, more than a year ago = 1, in the past year = 2, in the past month = 3, and in the past week = 4. In addition, students who answered yes to the ever-used question were asked how many days in the past 30 did they use the substance (frequency) and how many drinks they would normally have when they drank alcohol (quantity). 2.2.1.1.1 Substance Use Problems The study used three measures of substance use problems.  The first was the CRAFFT test, which tests general substance use problems including alcohol, cannabis, and other illicit drugs.  The second was the AUDIT, which tests for problem alcohol use.  The third is the CUDIT, which tests for problem cannabis use.  All tests were administered conditionally on endorsing use of alcohol for the CRAFFT and AUDIT and on endorsing use of cannabis for the CUDIT. 2.2.1.1.1.1 CRAFFT  The CRAFFT was developed to measure alcohol and drug use problems in adolescents (Knight, Shrier, Bravender, Farrell, Bilt, & Shaffer, 1999) and has been shown to have adequate sensitivity and specificity for identifying adolescents with substance-related problems (Knight, Sherritt, Harris et al., 2003). This measure consists of six ?yes? or ?no? items designed to indicate problems experienced with alcohol and other drugs in the past year. CRAFFT is an acronym of the first letters of key words in the test? s six questions:  Page 33    ?Car?, ?Relax?, ?Alone?, ?Forget?, ?Friends?, and ?Trouble?. The CRAFFT is the only screening test that includes an item on drinking and driving (or riding with an intoxicated driver) which is designed to screen for risk of alcohol-related car crashes based on having ridden in a car with an intoxicated family member or peer (Knight et al., 2003). A total score of 2 or higher indicates that further assessment is needed. 2.2.1.1.1.2 AUDIT The Alcohol Use Disorders Identification Test (AUDIT) was developed for the World Health Organization as a screening measure for health workers to administer to clients in order to identify hazardous alcohol use (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). This measure was designed to detect problem drinking at the low end of the spectrum, and because it was worded in such a way as to reduce under-reporting, the AUDIT can be administered along with health and lifestyle questionnaires, such as in this study, and not stand out as different or extreme and so participants are more likely to answer honestly. Although originally designed for adults, it has been found suitable for adolescents (Chung, Colby, Barnett et al., 2000; Reinert & Allan, 2007). The 10-item questionnaire consists of three questions about the amount and frequency of alcohol use, three questions about alcohol dependence, two questions about adverse psychological reactions, and two questions about problems caused by alcohol use (Saunders, Aasland, Babor et al., 1993). Each response has a score ranging from 0 to 4. All scores are summed together to produce a total score between 0 - 40, of which a score of 8 or more is indicative of hazardous or harmful alcohol use and dependence. 2.2.1.1.1.3 CUDIT Similar to the AUDIT, the Cannabis Use Disorders Identification Test (CUDIT) is a 10-item questionnaire used for identifying current cannabis use and abuse (Adamson &  Page 34    Sellman, 2003). The philosophy of the CUDIT is the same as the diagnostic philosophy of the DSM-IV, that the phenomenology of substance disorders is equivalent across different substances. The scoring range for the CUDIT is from 0 to 40, with 8 being the cut-off score indicative of problem cannabis use and abuse.  2.2.1.1.2 Substance use associations Two substance use association measures were used in this study.  Each of these measures was obtained for both alcohol and cannabis. First an indirect measure of association was obtained using a cued word/phrase association task followed by self-coding of the response (Frigon & Krank 2009; Krank et al., 2010).  Second, direct measures of substance use outcome expectancy were obtained using an open-ended question with a rating of liking (Fulton et al., 2012). Both measures have been shown to strongly predict substance use in adolescents. This thesis included a version of the Cued Association Task developed by Krank and colleagues (Frigon & Krank, 2009; Krank et al., 2011).  This method retains the top of mind, automatic, association feature of the cued response task, but also allows the participant to clarify and disambiguate common answers such as ?drink? or ?party? and improves the prediction of substance use (Frigon & Krank, 2009; Krank et al., 2011).  The task begins with an open-ended response to a potential substance use cue.  In a later self-coding phase, after responses are given, the participant categorizes their own responses according to a variety of options including substance use. In addition, a direct method of assessing expected alcohol and cannabis use outcomes was used.  In this procedure, the participant is asked to identify four outcomes of alcohol and cannabis use respectively.  By cuing open-ended responses, this technique assesses the top of mind outcome expectancies that should be generated by automatic associations.  Again participants code their responses; in this case they identify how much they would like or not like the outcome.  This liking  Page 35    measure also strongly predicts adolescent substance use initiation, levels, and rate of escalation (Fulton, Krank, & Stewart, 2012).  2.2.1.1.3 Self-coded emotion and situation associations (Frigon & Krank, 2009; Krank et al., 2011)  The first associative measure was an indirect assessment of substance use associations. These cued and self-coded measures have two stages.  First, participants are asked to write down the first thing they think of when they see a word (Word Associates) or behaviour  (Behaviour Associates) that comes to mind when they see a situational or emotional phrase.  These associates are generated at the beginning of the survey prior to any questions about substance use.  Later in the survey, participants are shown these same stems with their response and asked about the meaning of their responses by checking all categories from a list of options that might apply.  The list includes a number of options including alcohol and cannabis use.  For example, a participant might have written down ?party? in response to the first behaviour that comes to mind on ?a typical Friday or Saturday night.?  Later when asked they could check a number of relevant options such as family, friends, or recreation.  They might also check ?alcohol? and ?cannabis.?  In this study we used a combined score that summed all ?alcohol? responses and all ?cannabis? responses to relevant stem items. 2.2.1.1.4 Substance use outcome expectancy liking (Krank & Goldstein, 2006; Fulton et al., 2012) This measure provided a direct open-ended assessment of substance specific outcome expectancies.  The question asked:  ?What would you expect to happen if you  Page 36    ______??  For alcohol use the stem was ?drank a moderate amount of alcohol.?  For cannabis use the stem was ?used cannabis.?  Alcohol wording  This question asks you to tell us about the anticipated effects of using a moderate amount of alcohol. We do not assume that you have used alcohol. Please answer this question even if you have never had a drink of alcohol. We are interested in what you think would happen.  Please enter the four most important things that you would expect or anticipate to happen if you drank a moderate amount of alcohol. Then indicate how much you would like or not like this outcome.  Cannabis wording This question asks you to tell us about the anticipated effects of using cannabis.  We do not assume that you have used cannabis.  Please answer this question even if you have never used cannabis.  We are interested in what you think would happen.       Please enter the four most important things that you would expect or anticipate to happen if you used cannabis.  Then indicate how much you would like or not like this outcome.    After each of four responses the participants were asked to rate how much they would like the outcome on a five point likert scale (Like a lot, Like, Neither, Not like, Not like a lot). 2.2.1.1.4.1 [Implicit] Word Associations, [Explicit] Expectancies, and [Explicit] Expectancy Liking.  These scales reliably measure cognitions and expectations regarding alcohol and cannabis use. The use of associative memory tasks is based on research that has shown that the decision to initiate substance use is influenced by cognitions based on both positive and negative, and direct and indirect memories and associations about substance use and its outcomes (Ames et al, 2007; Christensen, Goldman, & Inn, 1982; Krank and Goldstein, 2006; Wiers et al., 2007). Indirect word association tests are better able than direct method tests to capture implicit memories that are relevant to substance use. Rather than direct questions, these tests enable participants to provide open-ended responses that are then coded as being related or not related to substance use. Direct (explicit) and indirect (implicit) measures of substance use associations correlate with levels of use but also predict  Page 37    substance use initiation and increased substance use (Kelly, Maserman, & Marlatt, 2005; Krank & Wall, 2006).  Indirect measures typically require laborious and subjective coding of at least two raters, and often results in a degree of irresolvable ambiguity. A possible issue is that the coder, often an adult, may interpret something differently from the adolescent participant and therefore score answers differently than would the participant. Errors in coding increase measurement error which decreases predictability and variable correlation and ultimately, power (Cohen, Cohen, West, & Aiken, 2003). To address measurement error and increase coding accuracy, we used the self-coding method developed by Krank (Frigon & Krank; 2009; Krank et al., 2010), in which the indirect associative responses are fed back and coded by the participants. These self-coded measures reveal a strong predictive relationship between associations and expectancies and substance use which captures the predictive value of standard researcher scored procedures and improves the prediction (Frigon & Krank, 2009; Krank et al., 2010).  Self-coding has been shown to identify more responses related to substance use than does inter-rater coding (Krank et al., 2010). This is because self-coding provides the opportunity for a participant to clarify or refine their response, resulting in disambiguity and decreased measurement error (Bouton & Nelson, 1998; Krank & Wall, 2006). Another benefit is that new information can be added through memory retrieval that is prompted through the additional memory probe when the participant is asked to clarify their answer (Krank & Wall, 2006), which results in increased predictability (Frigon & Krank, 2009). Krank et al. (2010) argue that self-coding may be more predictive because it includes an explicit component along with the strongly predictive implicit component; it increases the predictability of substance use.  Page 38    2.2.1.1.5 Impulsivity Scale - Substance Use Risk Profile Scale  This study used the impulsivity measure from the Substance Use Risk Profile Scale (SURPS+) (see Appendix). Impulsivity was measured to confirm previous research showing that Impulsivity is a predictive factor in adolescent alcohol use (Krank et al., 2011; Wiers et al., 2010; 2011) and to investigate whether it shares variance with, or suppresses, EF and FO. If Impulsivity does suppress EF and FO, then EF and FO are possibly personality traits that cannot be ?unlearned? or changed. However, if Impulsivity does not suppress, then it is likely that EF and FO are cognitive abilities or orientation that can be learned, developed, and incorporated into prevention programs.   The SURPS (Krank et al., 2011; Woicik, Sherry, Pihl & Conrod, 2009) is a brief 23-item scale was developed for use with adults and adolescents to measure personality dimensions that are particularly relevant to substance use vulnerability. It has four subscales: AS (anxiety sensitivity), H (hopelessness/negative thinking), IMP (impulsivity), and SS (sensation-seeking). AS has been shown to be positively related to alcohol use and negatively related to cannabis use (see review by Stewart, Samoluk, and MacDonald (1999). However, Samoluk, Stewart, Sweet, & MacDonald (1999) found AS negatively related to both alcohol and cannabis use, suggesting that individuals scoring higher on the AS scale are less likely to initiate substance use in early adolescence. Furthermore, DeMartini and Carey (2011) found that drinking motives (eg. drinking to dull awareness of stress) mediate the relationship between AS and alcohol use frequency.  Krank et al. (2011) evaluated the validity of the SURPS+ personality subscales for substance use in adolescents in grades 8 to 10. Analyses revealed that the personality subscales H, SS and IMP are strong predictors of substance use and abuse. Higher levels of IMP are associated with greater alcohol and cannabis use and abuse, and higher levels  Page 39    of both IMP and SS predict future alcohol and cannabis use and abuse. SS levels predict past year alcohol use and drunkenness, a finding which supports previous research findings that SS substance abusers are particularly at risk of heavy drinking and alcohol problems (Conrod et al., 2000). As well, levels of H predict past year alcohol use and drunkenness and future cannabis use, and SS and H predict CRAFFT scores. 2.2.1.2 Executive Function Measures 2.2.1.2.1 Go/No-Go Task  The cued Go/No-Go task is a measure of inhibitory control. We used the version and procedure as described in Fillmore, Rush & Hays (2006). A trial involved the presentation of a fixation point for 800ms, and then a white screen was displayed for 500ms after which either a horizontal or vertical oriented rectangular cue was displayed in the centre of the screen against the white background. Either a Go or No-Go target was presented after the cue. The Go target was a solid green color and the No-Go target was solid blue. Participants were to press the spacebar as soon as they saw a green target (Go) appear on the screen but to suppress a response when they saw a blue target (No-Go) on the screen. The target remained on the screen until the participant pressed the space bar or 100ms had elapsed. The Go target was preceded by the horizontal cue on 20% of the trials and by the vertical cue on 80% of the trials; alternately, the No-Go target was preceded by the horizontal cue on 80% of the trials and preceded by the vertical cue on 20% of the trials. Over the 250 trials, an equal number of vertical and horizontal cues were randomly presented before an equal amount of Go and No-Go targets. The version used is described by Fillmore et al. (2006) in which cues provide information about the target stimulus to follow (stop or go). Cue-dependence is developed quickly and participants anticipate the need for an inhibitory response or an active response to each cue (Posner, 1980; Miller, Schaffer, &  Page 40    Hackley, 1991). The mean score for this task was 239.8 (SD = 9.61) (see Table 2). Nosek and Banaji (2001) reported a split-half reliability of r = 20; whereas, findings of test-retest reliability range from r = 51 to r >.80 (Rudolph, Shroder-Abe, Schutz et al., 2008; Williams & Kaufmann, 2012).  The number of correct choices and the number of errors were summed across all sections to give a total of correct and a total of error. The Mean correct score was used as the dependent variable on this task.  2.2.1.2.2 Self-Ordered Pointing Task (SOPT) The SOPT is a measure of WM. In this study, I used a version of the representational drawings subtask of the SOPT as developed by Petrides & Milner (1982).  The task was divided into four blocks of 6, 8, 10, and 12 pages of pictures displayed in a changing matrix on the screen. Participants completed the 6-picture block as a practice set, then proceeded to the 8-picture block to start the testing, after which a new set of pictures was presented for the 10-picture block, and lastly a new set of pictures was presented for the 12-picture block.  At the start of a block, a participant chose a picture in the matrix by pointing and clicking with the mouse. The matrix was then scrambled and shown to the participant again, the participant was to choose a new picture. This continued until all screens in the block were completed, the goal being to point to each picture only once during a block. Each time a participant pointed to a picture more than once it was counted as an error. The number of correct choices and the number of errors were summed across all sections to give a total of correct and a total of error. Archibald and Kerns (1999) reported a test?retest reliability coefficient of r= .76 in a sample of 18 children (M age = 10.03; S.D. = 1.72). The Mean correct score was used as the dependent variable on this task. The mean for this measure was 92.8 (SD = 10.1) (See Table 2).   Page 41    2.2.1.2.3 Iowa Gambling Task ? Donkey Version The Iowa Gambling Task (Bechara et al., 1994), was developed to measure impulsivity, task-switching, reward sensitivity, and the ability to consider future consequences. We used a revised, pro-social version, as developed and described by Crone and van der Molen (2004) (see Appendix). Studies have reported concerns about low reliability (Buelow & Suhr, 2009; Gansler, Jerram, Vannorsdall, & Schretlen, 2011; Lin, Song, Chen, Lee, & Chiu, 2013). In this task, participants viewed a donkey and four doors (A, B, C, and D) on the screen. They were instructed to assist the hungry donkey to get as many apples as possible by selecting one of four doors, which would result in winning or losing apples. The participants continued to select doors, switching doors whenever they would like, with the goal to win as many apples as possible for the donkey. Each time the participant chose a door, a small box would appear on the screen indicating the ratio of apples won to apples lost for that door (see bottom (see Appendix).  Two performance panels were displayed for the duration of the task; a small horizontal bar above each door provided information on average wins and losses on that particular door, changing each time the door was chosen, and one large bar displayed under the doors provided the same information but averaged across all four doors.  This task had two blocks: a standard block and a reversed block. In the standard block, doors A and B were high paying, and doors C and D were low paying. A switch happened during the reverse block; doors A and B were now low paying, and doors C and D were high paying. Each block consisted of 100 trials, and each participant completed both blocks, in counterbalanced order. Although there are several scores that can be computed for this task, we used the Overall score (gain-loss) across blocks in our analysis. The mean score for this task was -26.0 (SD = 11.5) (See Table 2).   Page 42    2.2.1.2.4 Future Orientation Questionnaire  The Future Orientation (FO) scale is a 15-item self-report measure developed by Steinberg et al. (2009).  Items are grouped into three, 5-item subscales that are related but not identical: time perspective (?some people often think what their life would be like 10 years from now? vs ?other people try to imagine what their life will be like in 10 years?), anticipation of future consequences (?some people usually think about the consequences before they do something? vs ?other people just act-they don?t waste time thinking about the consequences?), and planning ahead  (?some people like to plan things out one step at a time? vs ?other people like to jump right into things without planning them beforehand?. Multivariate analyses revealed that FO scores are significantly correlated with responses concerning planning ahead (r = ?.40) on the Zuckerman Sensation-Seeking Scale (Zuckerman, Kolin, Price & Zoob, 1964) but not correlated with responses indicating thrill seeking (r = ?.01), which suggests that the FO scale measures EF but not personality (Steinberg et al., 2009). FO scores are also correlated with items from the Barratt Impulsivity Scale (Barratt, 1959) regarding planning and thinking about the future (r =.41) but not with items that indicate inattentiveness (r =.00) or lack of loyalty toward others (r = ? .05) (Steinberg et al., 2009).  This suggests that the FO scale measures an individual?s ability to anticipate consequences and work toward delayed rewards, which is an aspect of EF. Furthermore, FO scores are negatively correlated with risk-taking (r = ?.22) (Steinberg et al., 2009). Steinberg et al. (2009) administered this scale to 935 individuals between the ages of 10 and 30 years old in order to examine age differences in future orientation. Results showed that the adolescents between 10 to 15 years old display lower levels of future orientation than individuals 16 to 30 years old, evidenced by their preference for a smaller but immediate reward over a larger but delayed reward (Steinberg et al., 2009).  Page 43    In this study, the Steinberg et al. (2009) questionnaire, which is usually presented in paper-and-pencil format, was revised so as to be presented on a computer: rather than showing the entire questionnaire on the screen, questions were presented one at a time and the participant clicked on the appropriate radio button answer. The participant was required to answer the current question before the next question was presented, until all 15 questions were completed. Each question was presented as a statement set, and the participant chose the radio-button option that better described how true each statement was for them. For example, the statement set for Question 1 was ?Some people like to plan things out one step at a time?   BUT   ?Other people like to jump right into things without planning them out beforehand?. The participant was required to choose either ?Really True for Me? or ?Sort of True for Me? for each statement. Only after both statements were answered was set 1 complete and set 2 appeared on the screen. This continued until all 15 statement sets were completed. Scoring the FO questionnaire. Each of the 15 questions required an answer for the first half of the question (?Sort of true for me? or ?Really true for me?) and an answer for the second half (?Sort of true for me? or ?Really true for me?). On the data output, there are 30 scores; these can be regrouped such that the scores for 1 and 2 are the two scores for the first question, the scores for 3 and 4 are the two scores for the second question, and so on for all 15 questions. All items on the scale (see Appendix) are scored left to right on a scale of 1-4. Each question, therefore, can have a lowest score of 4 and a highest score of 6. Scoring: All items are scored left to right on a scale of 1-4. Score items 2, 5, 7, 9, 12, 13 and 15 adding from left to right. Reverse score items 1, 3, 4, 6, 8, 11, and 14, so that higher scores indicate a stronger future orientation. Future Orientation total score is the unweighted average of all 15 items. To calculate the subscales: Planning Ahead is the unweighted average of items 1, 6, 7, 12, and 13. Time Perspective is the unweighted average of items  Page 44    2, 5, 8, 11, 14. Anticipation of Future Consequences is the unweighted average of items 3, 4, 9, 10, 15. The mean scores and standard deviations are listed in Table 2. 3 RESULTS 3.1 Outliers Four participants were excluded from analysis because their cognitive scores exceeded the mean correct score in at least one EF task by more than three standard deviations. The final analytic sample was N=88. This method of exclusion was the same as that used by Thush et al. (2008) whose study also included analyses regarding associations, WM, and substance use in adolescents. Missing data for individual variables was minimal (<5%).  The main analyses used Generalized Linear Models, which employs robust full data maximum likelihood estimations, which effectively deals with missing values. 3.2 Descriptive Statistics 3.2.1 Measures of Executive Function, Future Orientation, and Impulsivity Table 2 shows the means and standard deviations of cognitive tasks that measure aspects of EF.  In addition, subscale means and standard deviations of FO scale and the Impulsivity scale taken from the SURPS are shown.  The bivariate correlations (Pearson-r) among these measures are also shown. Significant positive correlations occurred between scores on the SOPT and Go/No-Go scales, and among scores on the Future Orientation subscales. The Donkey measure and the SURPS Impulsivity were not correlated with Future Orientation nor any other cognitive tasks. Time Perspective and Anticipation of Consequences subscales of FO were both significantly correlated with higher scores on the FO Planning Ahead subscale. The small correlation between SOPT and GNG suggests that  Page 45    these two scales measure some common aspects of EF, and provides support to Archibald and Kerns (1999) who revealed a correlation between WM and inhibition on the Go/No-Go and SOPT. No other correlations between any of the other EF scores were revealed, which suggests that the EF tasks are measuring independent control processes. The SURPS Impulsivity scores were not correlated with any other measure, indicating that EF scales are not directly related to this trait measure of impulsivity.  The general absence of correlations among the measures suggest that the self-reported FO and Trait level impulsivity measures are not a direct consequence of the EF cognitive processes measured in this study. Tests of goodness of fit indicate that all models show a good fit with the data.  Table 2.   Means, standard deviations and correlations among cognitive and behavioral control measures (independent variables)  Mean SD  1 2 3 4 5 6  1. GNG 239.8 8.61  -       2. SOPT 92.8 10.1  .22*       3. Donkey -26.0 11.5  .02 .11      4. Planning Ahead 5.05 .35  -.12 -.13 .09     5. Time Perspective 4.96 .35  .13 .13 .11 .40***    6. Anticipating Consequences 4.92 .40  -.02 -.08 .03 .39*** .18   7. SURPS Impulsivity 2.98 .67  -.02 -.11 -.05 .11 -.11 .09  *p < .05, **p < .01, ***p< .001   Page 46    3.3 Measures of alcohol use, abuse, and associations The descriptive statistics for the alcohol outcome measures and their correlations are shown in Table 3.  Bivariate analyses were used to measure the correlations between each dependent variable (alcohol use, abuse, and risk of abuse) with the cognitive measures of self-generated implicit alcohol associations and explicit outcome liking measures.  As expected all alcohol-related measures are strongly or moderately correlated, indicating that they are associated and measure overlapping processes involved in alcohol use and risk.  As expected, high correlations occur between the AUDIT and alcohol use and drunkenness measures. There is also a moderate correlation between scores on the alcohol use and drunkenness measures, and AUDIT with the CRAFFT.   The mean scores for alcohol use indicate that the majority of students have either not consumed alcohol or last drank over a year ago. 16% of participants had AUDIT scores that indicate hazardous drinking, and 13% of participants had CRAFFT scores that indicate increased risk.  3.4 Measures of cannabis use, abuse, and associations Descriptive statistics for the cannabis outcome measures and their correlations are shown in Table 4. Bivariate analyses1 were used to measure the correlations between each dependent variable (cannabis use scores, CUDIT scores) with implicit association measures, as shown in Table 3. The CUDIT is highly correlated with the cannabis use measure, and the implicit associates and explicit liking measures are moderately correlated with the cannabis use measure. Furthermore, explicit outcome liking and cannabis use have a higher correlation than explicit outcome liking and the CUDIT. The implicit associate and                                                           1 Initial analyses reported in Tables 1-3 were conducted using Pearson?s correlations.  Given that many of the variables were highly skewed and zero-inflated, each relationship was confirmed using GLzM analysis (see below).  Page 47    explicit liking measures are moderately correlated. Some participants had CUDIT scores indicating medium to high risk (4.4%). In summary, each independent measures is correlated with the other independent measures and with cannabis use and is consistent with research showing that each of these scales reliably correlate with cannabis use (Frigon and Krank, 2009; Fulton et al., 2012).     Page 48    Table 3.   Means, standard deviations and correlations among measures of alcohol use and risk, associations and outcome expectancies  Mean SD 1 2 3 4 5 1. Last time drank alcohol1  1.84 2.49 -     2. Last time drank alc1  .80 1.72 .67***     3. CRAFFT score2    .64 1.26 .27** .42***    4. AUDIT score3 1.16  .38 .75*** .67*** .33***   5. Alcohol associates4   .98 1.61 .37*** .28*** .34*** .42*** - 6. Alcohol outcome liking5  -.96 1.06 .39*** .37*** .32*** .37*** .51*** *p < .05, **p < .01, ***p< .001; 1range (0 ? 4), where 0 ? never, 1 ? more than a year ago, 2 ? in the past year, 3 ? in the past  month, 4 ? in the past week; 2number of items endorsed (0 ? 6); 3 AUDIT score range (0 - 4), where 0 ? never, 1 ? less than monthly, 2 ? monthly, 3 ? weekly, 4 ? daily or almost daily;  4score from 0 to 10; 5score ranging from -2 to 2. See text for further information.  Page 49    Table 4.   Means, standard deviations and correlations among measures of cannabis use, cannabis abuse, and cannabis associations   *p < .05, **p < .01, ***p< .001; 1range (0 ? 4), where 0 ? never, 1 ? more than a year ago, 2 ? in the past year, 3 ? in the past month, 4 ? in the past week; 2 CUDIT score range (1 - 4),  where 1 - low risk, 2 ? medium risk,  3 ? high risk, 4 ? dependence risk; 3score from 0 to 5; 4score ranging from -2 to 2. See text for further information.   Mean  SD 1 2 3 1. Last time used cannabis1   .76 1.67 -   2. CUDIT score2 1.07  .38 .66*** -  3. Cannabis associates3 1.93 2.02 .51*** .35*** - 4. Cannabis outcome liking4 -.93 1.16 .47*** .30*** .40***  Page 50    3.5 Generalized Linear Regression Analyses Analyses for main effects were conducted first, followed by moderation analyses. The main effect of each independent variable on each dependent variable was analyzed. These analyses were done to explore the pattern of relationships with substance use recognizing that the substance use measures are zero inflated, highly skewed and non-normal. We performed multiple generalized linear regression analyses (GLzMs) to examine the relationships of Executive Function task scores, Future Orientation subscales, Impulsivity, cued associations, and outcome liking expectancies with alcohol and cannabis use and abuse. The independent variables: scores for the GNG, SOPT, and DONKEY, and the FO subscales of Time Perspective, Planning Ahead, and Anticipating Consequences, SURPS Impulsivity, and Word Associates and Outcome Expectancy Liking. The dependent variables: scores for ?last time drank?, ?last time drunk?, and ?last time used cannabis?, and total scores for the AUDIT, CRAFFT, and CUDIT questionnaires.  The first order analyses were conducted to determine main effects. First, each independent variable was entered into the regression analyses on its own, to determine its independent contribution to the variance of the dependent variable. Scores for DONKEY, GNG, and SOPT measures were standardized; scores for the Future Orientation subscales Time Perspective, Planning Ahead, and Anticipation of Consequences were centered. As well, scores for Associates and Outcome Expectancy Liking scores were centered. The standardized betas of the first order analyses are shown in Table 5. The standardized beta refers to the change of the dependent variable as a function of one standard deviation of change in the independent variable.  This measure provides an indication of the magnitude of effect sizes. As is shown, the Executive Function task yielded main effects of SOPT for cannabis use and CUDIT scores.  Higher SOPT scores of working memory were associated with  Page 51    lower levels of cannabis use and abuse.  Participant scores on the GNG task were also associated significantly with lower CUDIT scores. The survey measures had main effects on substance use.  Higher Time Perspective scores were associated with lower substance use across all measures: alcohol use, drunkenness, AUDIT, CRAFFT, cannabis use, and CUDIT scores. As was found in previous studies, higher SURPS Impulsivity was associated with higher substance use including alcohol use, drunkenness, CRAFFT scores, cannabis use scores, and CUDIT scores.   Page 52    Table 5.  Bivariate analyses of scores for each EF measure, FO subscale, and SURPS Impulsivity on each outcome measure   Alcohol  Cannabis  Last time used  (b) Last time drunk  (b)  AUDIT (b)  CRAFFT (b)  Last time used (b)  CUDIT (b) 1. GNG - .92     .93 - .59     .96    - .85 - 2.71 2. SOPT   .07   - .39 - .18  - .37     - 1.20**    - 1.47*** 3. Donkey  - .07   - .07 - .06     .11  - .13   - .35 4. FO - Time Perspective  - 1.11**  - 1.00*   - 1.32** - 1.33*   - 1.15*    - 2.26** 5. FO - Planning Ahead .10   .13 - .45 -. 96     .06 - 1.50 6. FO- Anticipating Consequences .11 - .19  - .24 - .06   - .13  - .69 7. SURPS Impulsivity   .72***  1.04** .37     1.17***       .91**    .66*  *p < .05, **p < .01, ***p< .001.  Frequency assumed a negative binomial distribution with a log link function. Last time used variables assumed a multinomial distribution    with a cumulative log link function. AUDIT and CUDIT scores assumed a negative binomial with a log link function. ((b) refers to the change of the dependent variable as a function of one standard deviation of change in the independent variable.   Page 53    Table 6 shows the main effects of Total Associates scores (total combined word+situational+outcome associate scores) and Outcome Expectancy Liking scores occurred for all outcome measures. These main effects were revealed throughout the moderation analyses. Figure 3A and Figure 3B shows the main effects of Associations and Expectancies on cannabis use and CUDIT scores, respectively. Figure 4A, Figure 4B, and Figure 4C show the main effects of Associations Outcome Expectancies on scores for alcohol use, the AUDIT, and CRAFFT. These results indicate that these cognitive variables are strong associates of substance use.   Table 6.   Main effects beta values for Total Associates scores and Outcome Expectancy scores associating each outcome measure using a generalized linear model  Drank Alcohol Drunkenness AUDIT CRAFFT Cannabis Use CUDIT Total Associates .37*** .33** .45*** .38** .47*** .53*** Outcome Expectancy Liking .72***  .79*** .66*** .50** .77*** 1.09*** *p < .05, **p < .01, ***p< .001. Total Associates = total associates score (total combined word+situational+outcome associates scores), OEL=outcome expectancy liking. *p < .05, **p < .02, ***p< .001.  Frequency assumed a negative binomial distribution with a log link function. Last time used variables assumed a multinomial distribution with a cumulative log link function.      Page 54      Low = low levels of associations and outcome expectancies. High = high levels of associations and outcome expectancies Figure 3A:    Cannabis use scores for Cannabis use as a function of associates and expectancies.  High and low categories were created using a median split for both word associates and outcome expectancies.   Page 55      Low = low levels of associations and outcome expectancies. High = high levels of associations and outcome expectancies Figure 3B:    CUDIT scores for Cannabis use as a function of associates and expectancies.  High and low categories were created using a median split for both word associates and outcome expectancies.  Page 56      Low = low levels of associations and outcome expectancies. High = high levels of associations and outcome expectancies Figure 4A:    Drank Alcohol and  Drunkenness scores for Alcohol Use as a function of associates and expectancies.  High and          low categories were created using a median split for both word associates and outcome expectancies.  Page 57     Low = low levels of associations and outcome expectancies. High = high levels of associations and outcome expectancies Figure 4B:    AUDIT scores for Alcohol Use as a function of associates and expectancies.  High and low categories were created          using a median split for both word associates and outcome expectancies.   Page 58       Low = low levels of associations and outcome expectancies. High = high levels of associations and outcome expectancies Figure 4C:    CRAFFT scores for Alcohol Use as a function of associates and expectancies.  High and low categories were          created using a median split for both word associates and outcome expectancies.  Page 59    3.5.1 Moderation of word association and outcome expectancy by Executive Function  To assess moderation effects, we conducted generalized linear analyses for main effects and interaction terms for Executive Function scores, FO scores, and Impulsivity scores with a) Total Associate scores and b) Outcome Expectancy Liking scores. These tests were performed for each substance use variable. Using the centered scores for each predictor variable, the interaction term provides a test for moderation. The results are shown in Table 7 to Table 13, respectively. Table 7 shows the moderation analyses for standardized GNG scores with Total Associates scores, and for standardized GNG scores with Outcome Expectancy Liking scores for all outcome measures. A trend toward a negative interaction is revealed between GNG and Total Associate scores on CUDIT scores suggests that higher levels of response control (as indicated by GNG scores) is protective against substance use associations effects on risky cannabis use (see interaction plotted in Figure 5).  These observations should be treated carefully as the main effect does not reach significance in the initial main effects analysis. In fact, Figure 5 suggests that the weak main effect is explained by the interaction with word associates. Table 8 shows the moderation analyses for standardized SOPT scores and Total Associates scores, and for standardized SOPT scores and Outcome Expectancy Liking scores for all outcome measures. When combined with Total Associates scores in the analyses, a negative main effect of SOPT is revealed for cannabis use scores, as well as a trend toward a negative main effect for CUDIT scores. When combined with Outcome Expectancy Liking scores in the analyses, negative main effects are also revealed for cannabis use scores and CUDIT scores. The large effect sizes indicate high predictive  Page 60    ability of the SOPT on cannabis use and risk scores, and the negative relationship indicates that higher SOPT scores are predictive of lower cannabis use and abuse. Page 61    Table 7.   Main effects and interactions of Go/No-Go scores with Total Associates and Outcome Expectancy Liking scores for all outcome measures  Alcohol   Cannabis  Last time used (b) Last time drunk (b)  AUDIT      (b)   CRAFFT      (b)  Last time used (b)  CUDIT (b) GNG score  -.10  .50    -1.07     .47      -1.99 -1.98 TA score      .23***    .54**     .32**  .31**   .43**    .83** GNG X TA  .20         1.42   .44 .88          .23 -2.52(.10) GNG score      -1.33   .66    -1.07 .90       -1.34 -2.84*  OEL score    .71***     .91***     .61** .31    .78**    .67* GNG X OEL  .30        1.74 1.37 .67         -.27  -.25 *p < .05, **p < .01, ***p< .001. GNG = Go/No-Go, TA= total associates score (total combined word+situational+outcome associates scores), OEL=outcome expectancy liking. GNG scores were standardized, and TA and OEL scores were centered for the analysis. Frequency assumed a negative binomial distribution with a log link function.  Last time used variables assumed a multinomial distribution with a cumulative log link function.   Page 62     Figure 5:    Trend towards an Interaction between GNG scores and Total Associates scores on CUDIT scores. High and low categories were created using a median split for both word associates and GNG score.  Page 63    Table 8.   Main effects of SOPT scores on Total Associates scores and Outcome Expectancy Liking scores for all outcome   measures   Alcohol   Cannabis  Last time used (b) Last time drunk (b)  AUDIT (b)  CRAFFT (b)  Last time used (b)  CUDIT (b) SOPT score .20        - .28     - .16 - .29  - 1.06** - 1.32 TA score   .39**   .60**   .25**   .23     .40**      .50* SOPT X TA       - .28 .05     - .01 - .13  - .08  - .05 SOPT Score .11        - .45     - .14 - .32  - 1.27**     - 1.77*** OEL score   .67**     .81***    .43**  .46      .73**    1.23*** SOPT X OEL        - .80         - .77     - .09 - .34     .04 - .04 *p < .05, **p < .01, ***p< .001. TA= total associates score (total combined word+situational+outcome associates scores), OEL=outcome expectancy liking. SOPT scores were standardized, and TA and OEL scores were centered for the analysis. Frequency assumed a negative binomial distribution with a log link function.  Last time used variables assumed a multinomial distribution  with a cumulative log link function Page 64    Table 9. Main effects and interactions of DONKEY scores with Total Associates scores and Outcome Expectancy Liking scores for all outcome measures  Alcohol   Cannabis  Last time used (b) Last time drunk (b) AUDIT (b) CRAFFT (b)   Last time used (b) CUDIT (b) DONKEY score - .13 - .15 - .01  .19  - .24 - .25 TA score    .34*     .61**    .23*  .29*      .39**     .69** DONKEY X TA - .09 - .21 - .21 - .50**   .13 - .12 DONKEY score - .10 - .07 - .01  .13  - .06 - .29 OEL score     .63**      .85***     .41**  .47*      .68**    .73* DONKEY X OEL  .09 - .12 - .08  .03     .34   .37 *p < .05, **p < .01, ***p< .001. TA= total associates score (total combined word+situational+outcome associates scores), OEL=outcome expectancy  liking. DONKEY scores were standardized, and TA and OEL scores were centered for the analysis. Frequency assumed a negative binomial distribution with a log link function.  Last time used variables assumed a multinomial distribution  with a cumulative log link function.  Table 9 shows the moderation analyses for standardized DONKEY scores and Total Associates scores, and for standardized DONKEY scores and Outcome Expectancy Liking scores for all outcome measures. A medium-sized negative interaction was revealed for Donkey scores and Total Associates scores on CRAFFT scores, which indicates that implicit associations predict risk of alcohol use in adolescents who are sensitive to reward and choose immediate smaller rewards over delayed, larger rewards (indicated by low DONKEY scores). Whereas, adolescents who do not have a sensitivity to reward but have a strong ability to switch tasks (learn new rules) and consider consequences (indicated by high DONKEY scores) are better able to overcome implicit associations and not abuse alcohol. The interaction is shown in Figure 6.   Page 65      Figure 6:  Interaction between DONKEY scores and Total Associates scores on CRAFFT scores.  High and low categories      were created using a median split for both word associates and donkey task scores.                  Page 66    3.5.2 Moderation of word association and outcome expectancy by the Future Orientation and Impulsivity  The Planning Ahead subscales of the Future Orientation scale revealed several interactions with word associates (see Table 10).   As shown in Figure 7, an interaction was revealed between Planning Ahead scores and Total Associates scores on alcohol use, indicating that low Planning Ahead scores and high Total Associates scores predict high alcohol use scores.  Figure 8 shows the interaction between Planning Ahead scores and Total Associates scores on AUDIT scores, indicating that low Planning Ahead scores and high Total Associates scores negatively predict AUDIT scores.  Figure 9 reveals the negative interaction between Planning Ahead scores and Total Associates scores on cannabis use scores, indicating that low scores on Planning Ahead and Total Associates negatively predict higher cannabis use scores.  Page 67    Table 10.   Main effects and interactions of Planning Ahead scores with Total Associates scores and Outcome Expectancy Liking scores for all outcome measures  Alcohol   Cannabis  Last time used (b) Last time drunk (b) AUDIT (b) CRAFFT (b)  Last time used (b) CUDIT (b) Planning Ahead score    .06    .06 - .21 - .80  - .30 - 1.79 TA score      .38**      .55**    .22*   .23     .39*     .73*** Planning Ahead X TA  - 1.63** - 1.01 - 1.13* - .15  - 1.78*     .03 Planning Ahead score         - .19   - .16 - .37 - .99    .09 - 1.69 OEL score     .65**        .87***      .45**    .46*      .71**     .79** Planning Ahead X OEL  - .24   - .22 - .15  .34    .55   1.07 *p < .05, **p < .02, ***p< .001. TA= total associates score (total combined word+situational+outcome associates scores), OEL=outcome expectancy liking. All Planning Ahead, TA, and OEL scores were centered for the analysis. Frequency assumed a negative binomial distribution with a log link function.  Last time used variables assumed a multinomial distribution  with a cumulative log link function.    Page 68     Figure 7:     Interaction between Planning Ahead scores and Total Associates scores on Alcohol Use scores.  High and low categories were created using a median split for both word associates and Planning Ahead scores.    Page 69       Figure 8:    Interaction between Planning Ahead scores and Total Associates scores on AUDIT scores. High and low categories were created using a median split for both word associates and Planning Ahead scores.  Page 70      Figure 9:   Interaction for Planning Ahead scores and Total Associates scores on Cannabis Use scores.  High and low categories were created using a median split for both word associates and Planning Ahead scores.  Page 71    Table 11.  Main effects and interactions of Anticipating Consequences scores with Total Associates scores and Outcome  Expectancy Liking scores for all outcome measures  Alcohol   Cannabis  Last time used (b) Last time drunk (b) AUDIT (b) CRAFFT (b)  Last time used (b) CUDIT (b) FO - Anticipating Consequences .35 .37 -.01  .15    .09   .33 TA    .24**    .58**   .23*  .27      .38*      .71** Anticipating Cons. X TA .15        - .13  .05  .04  - 1.17**  - .49 FO - Anticipating Consequences .27 .19     - .04     - .11    .49 - 1.06 OEL score    .45**     .88***    .45**    .46*      .74**       .81** Anticipating Cons. X OEL  .10 .31     - .18  .76    .91     1.93** *p < .05, **p < .01, **p< .001. TA= total associates score (total combined word+situational+outcome associates scores), OEL=outcome expectancy liking. All Anticipating Consequences, TA, and OEL scores were centered for the analysis. Frequency assumed a negative binomial distribution with a log link function.  Last time used variables assumed a multinomial distribution with a cumulative log link function.   Page 72    Table 11 shows the moderation analyses for Anticipating Consequences scores for all outcome measures. A negative interaction was revealed for Anticipating Consequences scores and Total Word Associates scores on cannabis use scores, which indicates that high cannabis use scores are negatively predicted by low Anticipating Consequences scores in participants with high levels of positive associations.  Figure 10 shows the interaction between Anticipating Consequences scores and Total Associates scores on Cannabis Use scores, indicating that high levels of the ability to anticipate consequences is a protective factor against implicit associations regarding cannabis use. This suggests that explicit associations are more likely to influence the decision to use cannabis in participants with low ability to anticipate consequences. Figure 11 shows the positive interaction between Anticipating Consequences scores and Outcome Expectancy Liking scores on CUDIT scores, which indicates that high CUDIT scores are predicted by higher Anticipating Consequences scores in participants with high Outcome Expectancy Liking scores. This suggests that the ability to anticipate consequences enhances the use of positive explicit associations in the abuse of cannabis.   Page 73            Figure 10:  Interaction between Anticipating Consequences scores and Cannabis Total Associates scores on Cannabis Use scores.  High and low categories were created using a median split for both word associates and Anticipating consequences scores.  Page 74            Figure 11:  Interaction between Anticipating Consequences scores and Outcome Expectancy Liking scores on CUDIT scores.  High and low categories were created using a median split for both word associates and Anticipating Consequences scores.  Page 75  Table 12.   Main effects and interactions of Time Perspective scores with Total Associates scores and Outcome Expectancy Liking scores for all outcome measures  Alcohol   Cannabis  Last time used (b) Last time drunk (b) AUDIT (b)   CRAFFT   (b)  Last time used (b) CUDIT (b) Time Perspective score     - 1.78**      - 1.35   - 1.44**      - 1.43        - 1.15    - 2.08** TA score        .46**          .61**       .26**          .29*            .41***       .83*** Time Perspective X TA     - .37         .47     - .06        - .13            .04     - .16 Time Perspective score   - 2.03**     - 1.30*   - 1.39**       - 1.60**          - .85    - 2.31** OEL score       .80*         .76***      .50**          .55**            .71**        .56** Time Perspective X OEL      - .42        .25    - .14          .46            .63        .44 *p < .05, **p < .01, **p< .001. TA= total associates score (total combined word+situational+outcome associates scores), OEL=outcome expectancy liking. All Time Perspective, TA, and OEL scores were centered for the analysis. Frequency assumed a negative binomial distribution with a log link function.  Last time used variables assumed a multinomial distribution with a cumulative log link function.  Table 12 shows the moderation analyses for Time Perspective for all outcome measures. When Time Perspective scores and Total Associates scores are combined in the moderation analyses, Time Perspective scores are shown to predict alcohol use scores, AUDIT scores, and CUDIT scores. When Time Perspective scores are combined with Outcome Expectancy Liking scores in the moderation analyses, main effects are revealed for Time Perspective scores for alcohol use scores, drunkenness scores, AUDIT scores, CRAFFT scores, and CUDIT scores.  The large effect sizes of Time Perspective indicate a robust negative relationship between Time Perspective scores and substance use; Time Perspective is a protective factor against both implicit and explicit associations in the decision to use alcohol and cannabis.  Page 76    Table 13.   Main effects and interactions of SURPS Impulsivity scores with Total Associates scores and Outcome Expectancy Liking use scores for all outcome measures  Alcohol   Cannabis  Last time used (b) Last time drunk (b) AUDIT (b) CRAFFT (b)  Last time used (b) CUDIT (b) SURPS Impulsivity score     .60**     .97** .16 1.09***    .79* .55 TA score      .33***     .30**    .44*** .34**            .45***   .53*** SURPS Impulsivity X TA - .13 - .13    - .27*   - .08  - .21 .04 SURPS Impulsivity score     .63**     .97** .17 1.09***     .73* .44 OEL score      .67***      .77***    .65** .41**        .89*** 1.06*** SURPS Impulsivity X OEL  - .02 - .10     - .13   - .02           - .63 (p = .07)   - .55 *p < .05, **p < .01, ***p< .001. TA= total associates score (total combined word+situational+outcome associates scores), OEL=outcome expectancy liking. All SURPS Impulsivity, TA, and OEL scores were centered for the analysis. Frequency assumed a negative binomial distribution with a log link function.  Last time used variables assumed a multinomial distribution with a cumulative log link function.  Table 13 shows the moderation analyses for centered SURPS Impulsivity scores for all outcome measures. When Total Associates scores were included in the moderation analyses, main effects were shown for SURPS Impulsivity scores for alcohol use, drunkenness, CRAFFT scores, and cannabis use. When SURPS Impulsivity scores and Total Associates scores were combined in the moderation analyses, a negative interaction was revealed for SURPS and Total Associates scores on AUDIT scores (b = -.272, p = .03), which means that lower SURPS scores and implicit association scores negatively predictive AUDIT scores. When Outcome Expectancy Liking scores were included in the moderation analysis, main effects were shown for SURPS  Page 77    Impulsivity scores for alcohol use, drunkenness, CRAFFT scores, and cannabis use. A trend toward a negative interaction between SURPS Impulsivity scores and Outcome Expectancy Liking scores on cannabis use scores was also revealed.   Page 78  4 DISCUSSION   This study replicated the robust predictive relationship of substance use associations and outcome expectancies with alcohol and cannabis use and abuse (Stacy et al. 2009).  In the context of this replication, the study explored whether higher levels of executive function (EF) cognitive abilities required for behavioural control and planning (e.g. working memory, behavioural control, and task-shifting with risk-taking and reward motivation) predict lower levels of substance use and if these abilities moderate the impact of substance use associations and outcome expectancies (Friese, Bargas-Avila, Hofmann & Wiers, 2010; Grenard et al., 2008; Hofmann, Gschwendner, Friese et al., 2008; Thush et al., 2008; Wiers, Beckers, Houben & Hofmann, 2009).  In addition, the study explored the novel hypotheses that higher levels of future orientation (FO) predicts less substance use and that FO moderates the impact of substance use associations and expectances.  Finally, the study also tested whether Impulsivity predicts substance use and/or moderates the impact of substance use associations and outcome expectancies.    The present study supported several of our proposed hypotheses based on the dual process theories: (1) Higher levels of alcohol and cannabis use will be positively associated with higher levels of implicit associations with each drug respectively (Table 6 and Figures 3 and 4); (2) Higher levels of alcohol and cannabis use will be positively associated with higher levels of explicit outcome expectancies with each drug respectively (Table 6 and Figures 3 and 4); and (3) Higher EF will reduce the effect of high positive associations (Tables 7 and 9; Figures 5 and 6), but only for some substance abuse measures and only for the GNG and Donkey tasks.  Our prediction that higher EF will increase the effect of high positive outcome expectancies was not supported. Several findings supported our novel prediction that higher future orientation will reduce the effect of high positive associations (Planning Ahead: Table 10; Figures 5 through 9).  In addition, as predicted higher  Page 79    Anticipating Consequences scores increased the effect of high positive outcome expectancies.  Finally, greater impulsivity is associated with higher substance use but does not moderate the relationship between associations or outcomes expectancies and substance use confirming our prediction and previous findings (cf Krank et al., 2011 and Wiers et al., 2011); In addition we found that possessing a longer Time Perspective had a main effect to reduce alcohol and marijuana use, especially problem use.    In summary, the present findings replicate previous studies that posit that automatic associative processes contribute to substance use and abuse (Ames et al., 2007; Christiansen, Goldman et al., 1982; Friese & Hofman, 2009; Grenard et al., 2008; Krank & Wall, 2006; Krank & Goldstein, 2006; Thush et al., 2008; Wiers et al., 2007). The study results also replicate the predictive relationship between the OEL measure of expectancies and substance use in this population (Fulton et al., 2012). In contrast, the study of moderation effects by control processes yielded mixed results.  The effects of greater FO were promising revealing a pattern of protection and a moderating effect on substance use.  The effects of the Impulsivity trait measure replicated previous findings showing that higher levels of impulsivity was associated with higher levels of alcohol  and cannabis use (Krank et al., 2011).  Moreover, the present moderation analysis revealed one interaction.  In contrast with previous findings and our hypotheses, however, the predictions that higher EF would have a protective relationship with substance use was only weakly supported with only one interaction reaching significance. These findings are discussed in detail below. Substance use associations, outcome expectancies and substance use  Associations and outcome expectancy liking scores are correlated with each other and with the outcome measures, consistent with findings of previous studies. These associations with substance use were confirmed by the GLzM analyses which cnfirmed main effects for associations and for outcome expectancy liking on the substance use measures.   Page 80     This study replicates recent findings that support the use of indirect measures with open-ended questions to obtain information about memory associations with substance use. This method generates spontaneous, implicit, and accessible responses (Ames et al., 2007; Frigon and Krank, 2009; Stacy, Ames & Grenard, 2006). Although direct measures such as outcome expectancies predict current and future substance use in adolescents (Callas, Flynn, & Worden, 2004; Christiansen & Goldman, 1983; Goldman, Reich, & Darkes, 2006; Leigh & Stacy, 2004), indirect measures such as the association measure have been shown to predict additional variance not accounted for by expectancy measures (Ames et al, 2007; Krank & Goldstein, 2006; Stacy et al., 2004). Indirect measures may capture implicit memories relevant to substance use that may not be captured by direct methods (Fulton et al., 2012). Direct methods typically contain pre-determined options that may be irrelevant, confusing, and even potentially harmful to adolescents (Fulton et al., 2012). For example, they may contain belief options that are typically not held by most adolescents who do not engage in substance use. The options may encourage substance use in this age group, as mere exposure to information can increase expectancy outcomes (cf. Ames et al., 2007; Bargh, 2002; Krank et al., 2010; Tom, Nelson, Srzentic, & King, 2007; Zajonc, 1968).   The study also replicated the self-coding method for scoring indirect memory associations (Frigon & Krank, 2009; Krank et al., 2010).  One issue with indirect measures is that they typically require laborious and subjective coding of at least two raters and often results in a degree of irresolvable ambiguity. Another issue is that the coder, often an adult, may interpret something differently from the adolescent participant and therefore score answers differently than would the participant. Errors in coding increase measurement error which decreases predictability and variable correlation and ultimately, power (Cohen, Cohen, West, & Aiken, 2003). To address measurement error and increase coding accuracy, we used the self-coding method developed by Krank (Frigon & Krank; 2009; Krank et al., 2010), in which the indirect associative responses are fed back and coded by  Page 81    the participants. These self-coded measures reveal a strong predictive relationship between associations and expectancies and substance use which captures the predictive value of standard researcher scored procedures and improves the prediction (Frigon & Krank, 2009; Krank, Schoenfeld, & Frigon, 2010).  Self-coding has been shown to identify more responses related to substance use than does inter-rater coding (Krank et al., 2010). This is because self-coding provides the opportunity for a participant to clarify or refine their response, resulting in disambiguity and decreased measurement error (Bouton & Nelson, 1998; Krank & Wall, 2006). Another benefit is that new information can be added through memory retrieval that is prompted through the additional memory probe when the participant is asked to clarify their answer (Krank & Wall, 2006), which results in increased predictability (Frigon & Krank, 2009). Krank et al. (2010) argue that self-coding may be more predictive because it includes an explicit component along with the strongly predictive implicit component; it increases the predictability of substance use.  The associations and OEL measures were correlated with each other and with all substance use measures, confirming previous studies that have demonstrated the validity of self-coded direct and indirect measures (Frigon & Krank, 2009; Krank et al., 2010; Fulton et al., 2012).The  effect sizes for associative and expectancy measures were maintained in the moderation analyses. Both Associations and Expectancies show better predictive value for cannabis use than for CUDIT scores; the only measure in which the effects were not maintained was the CRAFFT, which is a non-specific measure of substance abuse risk and with a high amount of variability. In summary, both Expectancies and Associations are highly predictive of alcohol use and risk of abuse.  Page 82    Executive Function and Substance Use  The small correlation between SOPT and GNG suggests that these two scales measure some common aspects of EF, and provides support to Archibald and Kerns (1999) who revealed a correlation between WM and inhibition on the Go/No-Go and SOPT. The absence of other significant correlations between EF measures suggests that the current tests of moderation by each function are independent of each other.     Of the EF functions, main effects of SOPT on cannabis use and CUDIT scores indicates that working memory is associated with cannabis use and abuse.  As well, a trend towards an interaction between GNG and associations on CUDIT scores suggests that higher levels of response inhibition may provide a protective function against marijuana abuse.  Finally, an interaction between Donkey scores and associations on CRAFFT scores indicates a protective factor of high task switching levels and low reward sensitivity on alcohol abuse.  Overall the results provide support, albeit weak, for the hypothesized protective role of higher EF associative influences protecting against substance use.  GO/NO-GO: Response inhibition Analyses revealed a trend toward a main effect for GNG scores on CUDIT scores. This trend occurred when GNG was considered on its own in the analysis and when OEL was included in the analysis with GNG, but not when TA was included. These trends while not conclusive are consistent with behavioural inhibition (as indicated by GNG scores) reducing cannabis use. The moderation analysis of GNG with implicit associations showed trend toward a negative interaction.  These trends are consistent with dual process theory predictions that higher levels of executive function should protect from the automatic effects of substance associations (Wiers et al., 2010).   Page 83    SOPT: Working memory A main effect of SOPT scores for cannabis use and CUDIT scores was revealed in both bivariate and multivariate analyses. The SOPT task is a measure of working memory (WM). Thush et al. (2008) posit that implicit associations predict alcohol use in individuals with low WM; whereas, explicit associations predict alcohol use in individuals with high WM. This expectation is based on Barrett et al. (2004) who suggest that implicit associations are more likely to influence decision-making in individuals with lower levels of WM compared to those with higher WM, and that those with higher WM are more likely to use reflective processes to over-ride the effects of implicit associations on their decision-making regarding substance use. Those with higher levels of WM are more likely to be able to hold and consider conflicting goals in WM and utilize more than one method of conflict resolution.  In this study, associations and outcome expectancies strongly predicted substance use and abuse, as does WM as measured by the SOPT. Our findings did not, however, reveal any significant interactions and do not support previous findings. Given the significance of the main effects for SOPT and associations, higher power may have revealed a significant interaction similar to previous findings (Grenard et al., 2008; Thush et al., 2008).  Grenard et al. (2008) revealed interactions, albeit small, for WM and associations for alcohol use. Although our analyses did not reveal interactions, it did show that both WM and associations account for a significant portion of the variance, which is consistent with the findings of Grenard et al. (2008) and with the dual-process perspective.  However, it is important to note that the interaction values for our analyses are small.  In addition, it is important to note that SOPT also may measure planning and organization to some degree, so we cannot definitively say that WM is the only moderating influence of decisions to use and abuse cannabis (Ross, Hanouskova, Giarla et al., 2007).  Page 84    In this population, however, SOPT did not correlate with FO or the sub-scales including Planning Ahead.     DONKEY TASK: Reward sensitivity and task switching  Donkey task scores do not predict substance use on their own but moderation analyses revealed a modest interaction among Donkey scores and associations on CRAFFT scores. This indicates that positive associations predict greater risk of alcohol abuse in individuals who have high levels of reward sensitivity and low abilities in task switching (indicated by low Donkey scores). A significant negative interaction was revealed for Donkey X TA for CRAFFT scores, which suggests that implicit associations predict risk of substance use in adolescents who are sensitive to reward and therefore choose immediate smaller rewards over delayed, larger rewards. Whereas, adolescents who are not sensitive to reward and are better at task-switching and considering consequences are better able to overcome implicit associations and not abuse cannabis.   Our findings for the Donkey are consistent with previous research that has shown that individuals who do not have a history of substance abuse tend to choose from the advantageous decks, while those with a history of use are not able to learn this tactic (Bechara and Damasio, 2002; Bechara and Martin, 2004; Monterosso, Ehrman, Napier et al., 2001; Petry, Bickel, & Arnett, 1998).  In our study, low scores on the Donkey task are predictive of CRAFFT scores, indicating that individuals who are showing signs of substance abuse were less likely either to choose from the advantageous decks or to figure out the strategy to do this. However, the participants in the previous studies were not adolescents so the findings of those studies may not be generalizable to our sample.  The isolated moderation effect on the CRAFFT measure requires replication, but may suggest a selective effect based on the risk assessment focus of the CRAFFT.  The CRAFFT is less focused on levels of use than the other measures of substance abuse used  Page 85    here.  As a consequence, a measure of risk tolerance (Donkey task) might be expected to selectively impact this measure. Moderating/Protective Effects of EF  Although the direction of many interaction effects favoured the moderation hypothesis, only one interaction effect between Donkey scores and total association scores reached statistical significance. This weak support contrasts with the findings in other studies of these interaction effects (Grenard et al., 2008; Hofmann et al., 2008; Thush et al., 2008; Wiers et al., 2009; Friese et al., 2010).  Future Orientation  To our knowledge, this is the first study to investigate the role of future orientation in adolescent substance use.  More future orientation appears to exert a protective effect on CUDIT scores.  There is also a trend toward significance in the main effect analysis of the AUDIT scores.  Of the subscales, Time Perspective revealed a broad protective relationship with both cannabis and alcohol use and abuse. Neither Planning Ahead nor Anticipating Consequences revealed any main effects.  Moderation analyses revealed several significant interactions.  Overall, there was a negative interaction for FO X TA on cannabis use, indicating that a high level of FO is protective against implicit associations during decisions to use cannabis. This suggests that adolescents who have a strong ability to consider the future and stay focussed on goals, are able to resist cannabis use despite a high level of positive associations. There are very few studies that have investigated the ability of adolescents to consider future consequences in decision-making processes, and those studies have compared between age groups, and have not included the use of implicit associations in the analyses. Of the studies that have been conducted in this area, Grisso et al. (2003) showed that adolescents between 11 to 13 years old are less likely to consider consequences of decisions than older adolescents, and  Page 86    Lessing (1972) found no age differences in this ability among adolescents between 9 to 15 years old. In summary, past research and our findings indicate variability in future orientation abilities between individuals and at each developmental stage.  Planning Ahead.  Our findings showed that the scores on the Planning Ahead subscale did not predict alcohol and drug use problems alone (with the exception of a trend identified by the CRAFFT.  Nevertheless, this future orientation subscale shows that more endorsement of Planning Ahead significantly moderates implicit associations during decisions regarding alcohol use and abuse and regarding cannabis use.  Importantly, in each case the direction of the interaction is consistent with the novel prediction that more future orientation protects from the risk imposed by automatic substance use associations.  .  One surprising finding is that in each of these three interactions high Planning Ahead scores and high Total Associate scores predict lower alcohol and cannabis use scores. Previous research has indicated that there is a positive relationship between intelligence and Planning Ahead (Conway, Kane, & Engel, 2003); these graphs may suggest that adolescents who have a strong ability to plan ahead have high levels of intelligence.  In these individuals, high association scores may reflect more ?general knowledge? and are therefore less impulsive associations than for other adolescents. Alternatively, high planners may have conflicting outcome expectancies that lead to reflective non-use.  Anticipating Consequences.  The Anticipating Consequences subscale revealed very few significant effects.  Notably, however, there were two interactions, one with substance use associations and the other with outcome expectancies.  These are noteworthy because they are consistent with dual process theories that postulate a protective effect of control on implicit associations and facilitative effects on explicit expectations.  Moderation analyses showed that the tendency to anticipate consequences is protective against implicit associations during the decision to use cannabis. The findings for this subscale are similar  Page 87    to those for Planning Ahead and the GNG task that involves behavioural inhibition. Although these scales did not correlate with the GNG, it appears that response inhibition and FO processes involved in these two tasks may be working in concert during decision-making.   In addition and importantly, Anticipating Consequences interacted positively with explicit outcome expectancies in the prediction of cannabis abuse.  Thus, the ability to anticipate consequences seems to facilitate the effects of explicit associations during the decision to abuse cannabis.  As discussed above, these findings support those of Wiers et al. (2002; 2010) who posit that control processes support the use of explicit associations which involve a choice whether to use a substance. Our finding is that the FO ability of anticipating consequences can be added to the argument of Wiers et al. (2002; 2010).  Time Perspective.  Main effect and moderation analyses revealed main effects of Time Perspective for most outcome measures. The Time Perspective subscale is a strong predictor of substance use and abuse. By contrast with Planning Ahead, Time Perspective did not interact with substance use associations.  This indicates a separate impact of having a long term view on substance use that does not depend on controlling automatic associations. General implications of Future Orientation on Substance Use in Adolescents  Steinberg et al. (2009) showed that the slope for Time Perspective is more stable than those for Planning Ahead and Anticipating Consequences during this stage of adolescence. Planning Ahead and Anticipating Consequences both have a decline around this age and then a sharp increase; whereas Time Perspective has a steady and less steep increase. This suggests that Time Perspective may be a consistent predictor of drug and alcohol use throughout all stages of adolescence. Likewise, in this study Time Perspective was the only subscale for which main effects were revealed in the bivariate analysis. When analyzed with associations and expectancies in the moderation analysis, there were no interactions but  Page 88    the main effects were maintained. These findings may be due to the stability of this aspect of future orientation. The interactions revealed in our study for Planning Ahead and Anticipating Consequences provide support to Steinberg et al.?s (2009) argument that both levels of planning ahead and anticipating consequences increase during this stage of adolescence, providing a protective influence on associations and expectancies. Impulsivity (SURPS)   Scores on SURPS were associated with alcohol and marijuana use. This finding replicates previous work (Krank et al., 2011; Woicik et al., 2010) using this brief scale. The findings of the SURPS moderation analyses are consistent with previous research in that those with higher impulsivity report higher levels of substance use, abuse, and risk.  As previously suggested, these data suggest that trait Impulsivity does not moderate the effects of implicit substance use associations (Wiers et al., 2010).   Page 89    Association between Future Orientation and Impulsivity  Our findings regarding Future Orientation and its subscales support Steinberg et al.?s (2009) findings that future orientation mediates an adolescent?s ability to hold off immediate gratification in order to strive for delayed rewards or long-term goals. Furthermore, Steinberg et al. (2009) posit that Future Orientation is unrelated to impulsivity, as evidenced by their findings that showed that Future Orientation strongly mediated differences in delayed gratification levels but impulsivity had only trivial effects on delayed gratification levels. Similar to Steinberg et al.?s (2009) findings, our study revealed that Future Orientation and the SURPS Impulsivity scale were uncorrelated, and that the effect sizes for association between Future Orientation subscales and alcohol and marijuana use and abuse were larger than the effect sizes for the SURPS Impulsivity subscale and alcohol and cannabis use and abuse; however, we also found that both Future Orientation scores and SURPS+ Impulsivity scores predict outcome scores.  Using confirmatory factory analysis, Steinberg et al. (2009) found that Time Perspective is related but not identical to Planning Ahead (r = .44) and Anticipating Consequences (r = .44). Our analysis revealed similar findings: Time Perspective is related to Planning Ahead (r =.40) and Anticipating Consequences  (r= .39).  However, Steinberg et al. (2009) also found that the subscales have relatively low alpha coefficients (time perspective, ? = .55; anticipation of future consequences, ? = .62; and planning ahead, ? = .70), which may result in low power issues. To avoid low power, Steinberg et al (2009) suggest that these subscales  should not be used individually as separate measures but be used together to form one scale.  Our results, however, suggest the opposite. Bivariate analyses indicated that the subscales are correlated but our moderation analyses revealed that each subscale is independently predictive of substance use. Main effects for Time Perspective were maintained throughout the analyses, suggesting that this subscale is a strong predictor of substance use. Similarly, the inclusion of Planning Ahead and  Page 90    Anticipating Consequences in the analyses equation with associations and expectancies lessens the predictive power of associations and expectancies; this may be due low sample power. Taken together, our findings indicate that the subscales are related yet not identical, and independently predict substance use. Further research may consider expanding or revising the subscales in order to increase alpha levels.  Limitations  Despite few significant interactions for EF and associations/expectancies, the direction of interaction coefficients and graphs of marginal effects suggest that higher power would have revealed significant interactions. Future studies that investigate the moderating effects of EF should use larger sample sizes. In addition, there may be problems inherent in administering cognitive tasks with younger participants.   For example, during testing some of the students became bored and/or frustrated due to the length of the tasks and because they could not relate to the task.  In particular, the Donkey task included 400 trials, which was too long for many of the participants, and although it was revised to appeal to children, it was likely not appropriate for this age group. A version of this task that includes a more age-appropriate reward, rather than feeding apples to a donkey, may yield significant interactions that indicate that task shifting is predictive of substance use and abuse, providing support to previous research. As well, the inter-trial time on the GNG task should be made shorter for adolescents than the inter-trial time that is used for adults. In summary, more age-appropriate tasks that measures the same Executive functions may prove to reveal more significant results. These observations suggest that the setting and tasks may have contributed to higher levels of error variance in the measures obtained in this population.    In addition to age related interactions with the tasks, increased variance may have been associated with conduction of the EF tasks in groups within the school setting.  Page 91    Students may have performed differently in the group setting than they would have if tested individually. This setting may have increased levels of competitiveness, nervousness, and/or stress in some students who would have performed more consistently if tested alone. Moreover, the group setting may have increased potential noise and interference from the activities of other students.  These potential issues may have added variability to the EF scores and contributed to the low levels of interaction coefficients with the Working Memory task (SOPT), which would have been affected by increased error. Nevertheless, the absence of even trends towards an interaction in these observations suggest that we should consider whether the dual process approach with respect to working memory as applied in older or high risk populations works in the same way in the same way for the general middle school population. The failures to replicate suggest possible concerns with 1) the applicability of the theory to the early teen sample, 2) the power of the sample size to test moderation, and 3) the reliability of the EF measures taken in the middle school setting.  Another limitation is the lack of cultural diversity in the sample.  Future studies should consider cultural diversity among participants, as culture may be a contributing factor in the development of executive functions and future orientation and may therefore have an effect on decision-making, risk-taking and reward motivation.   Finally, it is important to note that this was an exploratory study. To date, no other studies have been conducted in which Executive Function and Future Orientation were included in the analyses with substance use outcomes in adolescents. The purpose of this study was to identify any patterns that may exist, with the intention to conduct follow-up studies that more rigorously examine the patterns. For this reason, we included many measures of control functions, and substance use and abuse; thus, many tests of the hypotheses were performed.  We did not use a Bonferroni correction specifically to avoid further reductions in power.  This approach means that our data analysis is not conclusive and the findings require further study.  One solution would be to combine measures into  Page 92    single scores (e.g. an overall alcohol or cannabis abuse score).  Nevertheless, the pattern of findings across these measures does confirm previous findings and will be informative to future studies.  For example, most of the significant findings with control functions point to measures of cannabis use and measures of substance abuse. Unfortunately, due to the small sample size, we were not able to compare gender effects.  Future directions of study may involve investigating the role of stress or anxiety. Adolescents are in a time of change and pressure academically, developmentally, and socially. The researcher attempted to use an environment that was comfortable to the student and did not have any outside or unusual distractions, so as to maintain consistency throughout testing and to not introduce extraneous variables. Although a few of the tasks did produce frustration due to lengthy inter-trial times or bored, or the inability to figure out the strategy for the Donkey task, the participants did not experience a great deal of stress and therefore the findings may not be generalizable to adolescents in real-world situations.  Real-life decision-making can be stressful for adolescents, due to many factors such as emotion, reward sensitivity, and social pressure which may result in the use of brain processes that were not utilized during this study  High cognitive load, stress, or alcohol consumption tax the reflective processing system and result in depletion of resources. These conditions can impair one?s ability to maintain behavioural control and result in increased impact of impulses on behaviour, even for individuals with high levels of EF (Wiers et al., 2009). Recent research supports the argument that an environment that involves neglect or violence increases the risk of substance use (Hyman, Garcia, & Sinha, 2006; Krank, 2010; Rees, 2008). Increased risk is due to chronic stress that decreases EF abilities and modifies prefrontal neural networks, resulting in heightened response to motivational or emotional stimuli, such as rewards, and decreased ability to control impulses based on unconscious associations (Radley, Rocher, Miller et al., 2006). Adolescents naturally have a strong tendency to seek immediate  Page 93    gratification and have high levels of impulsivity and sensation-seeking; these are increased in individuals who experience lengthy periods of emotional distress at home or school (Tice, Bratslavsky, & Baumeister, 2001).  Lastly, the novel findings in this study regarding the moderating role of FO warrant further investigation regarding FO, as well as the development of other FO measurement scales that can detect adolescents with low EF and FO levels and programs that can help to increase FO in those children.   Page 94    5 CONCLUSION AND CLINICAL IMPLICATIONS  Our findings provide support for the need of continued development of adolescent substance use initiatives. Programs should include resources and activities to help strengthen EF and FO and lower Impulsivity levels. Appropriate training in problem-solving and critical thinking would be beneficial, with teachers and trainers always keeping in mind that adolescents are in a potentially stressful transitional stage of development.  Steinberg et al. (2009) showed that adolescents between the ages of 10 and 15 years old tend to plan ahead less than those younger and older in age. This age-related difference may be due to several factors such as the dramatic increase and pruning of DA receptors which is related to increases in impulsivity, and therefore lack of planning, during this time of development. Compared to childhood and adulthood, adolescence is a more transitional stage of life in which individuals may still feel childlike in some ways yet are developing physically and socially into adults. Adults around them may have expectations that these adolescents are planning for their future goals and careers, yet neurological development is still at a stage that is transitioning from childhood to a more mature state. Therefore, it is plausible that adolescents are not developmentally prepared to plan ahead because they are in the middle of great change. Going through tumultuous transition requires great mental, physical and emotional energy. Considering that an individual who is just now transitioning out of childhood, which is typically a time of great imagination and play, and is dealing with many changes and perhaps stressful new social situations, it would stand to reason that the ability to plan ahead is still developing and maturing.   These findings suggest that prevention programs should be sensitive enough to detect students with weak skills in planning ahead, while taking into account the developmental stage and previous research such as that of Steinberg et al., (2009), and so aim to encourage and develop strong skills in planning ahead in adolescents. Activities that  Page 95    develop the EF processes of task-switching and anticipation of consequences may help to lessen reward sensitivity and help individuals overcome the influence of implicit associations on decisions to use alcohol or cannabis in a risky manner. Therefore, prevention programs should consider the use of the Future Orientation task, or a version thereof, because it appears to be sensitive enough to detect those who are at risk of abusing alcohol and cannabis. They should also assess the length of time the individual has been using alcohol. Given the evidently strong predictive power of Future Orientation in drug and alcohol use problems, this is a process on which prevention programs may wish to focus.  In summary, this pilot study provides evidence that high levels of Impulsivity are correlated with high levels substance use and abuse, but that high levels of EF and FO suppress the effects of associations and outcome expectancies on substance use and abuse in adolescents. Strengthening skills in EF may concurrently increase FO levels, yet EF and FO should be strengthened separately. In addition, the findings support the use of EF, FO, and Impulsivity measures both as an indicator for level of substance use risk and as a primary target for prevention interventions.   Page 96    REFERENCES Abdullaev, Y., Posner, M.I., Nunnally, R., & Dishion, T.J. (2010). Function MRI evidence for inefficient attentional control in adolescent chronic cannabis abuse. Behavioral Brain Research, 215, 45-57. Adamson, S. J., & Sellman, J. D. (2003). A prototype screening instrument for cannabis use disorder: the Cannabis Use Disorders Identification Test (CUDIT) in an alcohol  dependent clinical sample. Drug Alcohol Review (22), 309-315.  Adriani, W., Spijker, S., Deroche-Gamonet, V. et al. (2003). Evidence for enhanced neurobehavioral vulnerability to nicotine during periadolescence in rats. Journal of Neuroscience, 23, 4712-4716.  Ames, S. L., Gallaher, P.E., Sun, P., Pearce, S., Zogg, J.B., Houska, B.R. et al. (2005). A Web-based program for coding open-ended response protocols. Behavior Research Methods, 37, 470-479. Ames, S. L., Grenard, J., Thush, C., Sussman, S., Wiers, R. W., & Stacy, A. W.(2007). Comparison of indirect assessments of association as predictors of cannabis use among at-risk adolescents. Experimental and Clinical Psychopharmacology, 15, 204?218. Andersen, S.L., Thompson, A.T., Rutstein, M., Hostetter, J.C., & Teicher, M.H. (2000). Dopamine receptor pruning in prefrontal cortex during the periadolescent period in rats. Synapse, 37(2),167-169.  Archibald, S. J., & Kerns, K. A. (1999). Identification and description of new tests of executive functioning in children. Child Neuropsychology, 5, 115?129. Arnett, J.J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55, 469-480.  Page 97    Aron, A.R., Robbins, T.W., & Poldrack, R.A. (2004). Inhibition and the right inferior frontal cortex. Trends in Cognitive Science, 8, 170-177. Babor, T.F., Higgins-Biddle, J.C., Saunders, J.B., & Monteiro, M.G. (2001). AUDIT. The Alcohol Use Disorders  Identification Test Guidelines for Use in Primary Care, Second Edition. The World Health Organization. Geneva, Switzerland. Baddeley, A.D. & Hitch, G.J. (1994). Developments in the concept of working memory. Neuropsychology, 8, 485-493. Baddeley, A.D., & Logie, R.H. (1999). Working memory: The multiple-component model. In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and EC. New York: Cambridge University Press. Baker, S.C.  Rogers, R.D., Owen, A.M., Frith, C.D., Dolan, R.J., Frackowiak, R.S.J. et al.  (1996). Neural systems engaged by planning: a PET study of the Tower of London Task. Neuropsychologia, 34, 515-526.  Barratt, E.S. (1959). Anxiety and impulsiveness related to psychomotor efficiency. Perceptual and Motor Skills, 9, 191-198. Barrett, L. F., Tugade, M. M., & Engle, R. W. (2004). Individual differences in working memory capacity and dual-process theories of the mind. Psychological Bulletin, 130(4), 553?573. Baumeister, E.F. (2003). Ego depletion and self-regulation failure: a resource model of self-control. Alcoholism, Clinical and Experimental Research, 27, 281-284. Bava, S., Frank, L.R., McQueeny, T., Schweinsburg, B.C., Schweinsburg, A.D., & Tapert, S.F. (2009). Altered white matter microstructure in adolescent substance use. Psychiatry Research, 173, 228-237. Bava, S., Jacobus, J., Thayer, R.E., & Tapert, S.F. (2013). Longitudinal changes in white matter integrity among adolescent  substance users. Alcoholism: Clinical and Experimental Research, 37(S1), 181-189.  Page 98    Bechara, A. (2005). Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nature Neuroscience, 8 (11), 1458-1463. Bechara, A., & Damasio, H. (2002). Decision-making and addiction (part I): impaired activation of somatic states in substance dependent individuals when pondering decisions with negative future consequences. Neuropsychologia, 40(10), 1675-1689. Bechara, A., Damasio, H., & Damasio, A.R. (2000). Emotion, decision-making and the orbitofrontal cortex. Cerebral Cortex, 10 (3), 295-307. Bechara, A., Damasio, A.R., Damasio, H., & Anderson, S.W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 5, 7-15. Bechara, A. & Martin, E.M. (2004). Impaired decision-making related to working memory deficits in individuals with substance addictions. Neuropsychology, 18, 152-162. Bechara, A., Tranel, D., & Damasio, H. (2000). Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain, 123 (11), 2189-2202. Blakemore, S-J, & Choudhury, S. (2006). Development of the adolescent brain: implications for executive function and social cognition. Journal of Child Psychology and Psychiatry, 47(3), 296-312. Blankstein, U., Chen, J.Y.W., Mincic, A.M., McGrath, P.A., & Davis, K.D. (2009). The complex minds of teenagers: neuroanatomy of personality differs between sexes. Neuropsychologia, 47, 599-603.  Botvinick, M.M., Braver, T.S., Barch, D.M., Carter, C.S., & Cohen, J.D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624-652. Bouton, M.B., & Nelson, J.B. (1998). The role of context in classical conditioning: Some implications for cognitive behavior therapy. In W. O?Donohue (Ed.), Learning and behavior therapy (pp. 59-84). Boston, MA: Allyn and Bacon.  Page 99    Bowman, C.H. & Turnbull, O.H. (2004). Emotion-based learning on a simplified card game: The Iowa and Bangor Gambling Tasks. Brain and Cognition, 55, 277-282. Brauer, J., Anwander, A., & Friederici, A.D. (2011). Neuroanatomical prerequisites for language functions in the maturing brain. Cerebral Cortex, 21, 459-466. Brenhouse, H.C., Sonntag, K.C., & Andersen, S.L. (2008). Transient D1 dopamine receptor expression on prefrontal cortex projection neurons: relationship to enhanced motivational salience of drug cues in adolescence. Journal of Neuroscience, 28(1), 2375-2378.  Brocki, K.C. & Bohlin, G. (2004). Executive functions in children aged 6 to 13: a dimensional and developmental study. Developmental Neuropsychology, 26 (2), 571-593. Buelow, M.T., & Suhr, J. (2009). Construct validity of the Iowa Gambling Task. Neuropsychology Review, 19, 102-114. Bunge, S.A., Hazeltine, E., Scanlon, M.D., Rosen, A.C, & Gabrieli, J.D.E. (2002). Dissociable contributions of prefrontal and parietal cortices to response selection. Neuroimage, 17, 1562-1571. Burnett, S., Bird, G., Moll, J., Frith, C., & Blakemore, S.J. (2009). Development during adolescence of the neural processing of social emotion. Journal of Cognitive Neuroscience, 21 (9), 1736-1750. Busemeyer, J.R. & Townsend, J.T. (1993). Decision field theory ? A dynamic cognitive approach to decision-making in an uncertain environment. Psychological Review, 100 (3), 432-459. Bush, G., Lun, P., & Posner, M.I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Cerebral Cortex, 18 (1), 2505-2522. Businelle, M. S., Apperson, M. R., Kendzor, D. E., Terlecki, M. A., & Copeland, A. L. (2008). The  relative impact of Nicotine Dependence, other substance dependence, and  Page 100    gender on Bechara Gambling Task performance. Experimental & Clinical Psychopharmacology, 16, 513-520. Carver, C.S. (2005). Impulse and constraint: Perspectives from personality psychology, convergence with theory in other areas, and potential for integration. Personality and Social Psychology Review, 9, 312-333. Cauffman, E. & Steinberg, L. (2000). (Im)maturity of judgment in adolescence: Why adolescents may be less culpable than adults. Behavioral Sciences and the Law, 18, 741-760. Cauffman, E., Shulman, E.P., Steinberg, L., Claus, E., Banish, M.T., Graham, S., & Woolard, J. (2010). Age differences in affective decision making as indexed by performance on the Iowa Gambling Task. Developmental Psychology, 46 (1), 193-207. Cauffman, E., Steinberg, L., & Piquero, A. (2005). Psychological, neuropsychological, and psychophysiological correlates of serious antisocial behaviour in adolescence: The role of self-control. Criminology, 43, 133-176. Chaiken, S., & Trope, Y. (Eds.) (1999). Dual-process theories in social psychology. New York: Guilford Press. Christiansen, B.A,  & Goldman, M.S. (1983). Alcohol-related expectancies versus demographics/background variables in the prediction of adolescent drinking. Journal of Consulting and Clinical Psychology, 51, 249-257. Christiansen,  B.  A.,  Goldman,  M.  S.,  &  Inn,  A. (1982).  Development  of alcohol-related  expectancies  in  adolescents:  Separating  pharmacological from social learning influences. Journal of Consulting and Clinical  Psychology, 50, 336-344. Chung, T., Colby, S., Barnett, N., Rohsenow, D., Spirito, A., & Monti, P. (2000). Screening adolescents for problem drinking: performance of brief screens against DSM-IV alcohol diagnoses. Journal of Studies on Alcohol, 61, 579?587.  Page 101    Cohen, J., Cohen, P., West, S.G., & Aiken, L.S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates Publishers. Conklin, H.M., Luciana, M., Hooper, C.J., & Yarger, R.S. (2007). Working memory performance in typically developing children and adolescents: behavioral evidence of protracted frontal lobe development. Developmental Neuropsychology, 31, 103-128. Conway, A. R., Kane, M. J., & Engle, R. W. (2003). Working memory capacity and its relation to general intelligence. Trends in Cognitive Science, 7, 547?552. Cragg, L. & Nation, K. (2007). Self-ordered pointing as a test of working memory in typically developing children. Memory, 15 (5), 526-535. Crews, F.T. & Boettiger, C.A. (2009). Impulsivity, frontal lobes and risk for addiction. Pharmacology, Biochemistry and Behavior, 93, 237-247. Crews, F.T., Braun, C.J., Hoplight, B., Switzer III, R.C., & Knapp, D.J. (2000). Binge ethanol consumption causes differential brain damage in young adolescent rats compared with adult rats. Alcoholism: Clinical and Experimental Research, 24, 1712-1723. Crone, E.A. & van der Molen, M.W. (2004). Developmental changes in real life decision-making: performance on a gambling task previously shown to depend on the ventromedial prefrontal cortex. Developmental Neuropsychology, 25I(3), 251-279. Damasio, A.R. (1996). The somatic marker  hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society, 351, 1413-1420. DeMartini, K.S. & Carey, K.B. (2011). The role of anxiety sensitivity and drinking motives in predicting alcohol use: a critical review. Clinical Psychological Review, 31(1), 169-177. De Bellis, M.D., Clark, D.B., Beers, S.R., Coloff, P.H., Boring, A.M., Hall, J., Kersh, A., & Keshavan, M.S. (2000). Hippocampal volume in adolescent-onset alcohol use disorders. American Journal of Psychiatry, 157, 737-744.  Page 102    Dennis, T.A. & Chen, C.C. (2007). Emotional face processing and attention performance in three domains: Neurophysiological mechanisms and moderating effects of trait anxiety. International Journal of Psychophysiology, 65 (1), 10-19. Devinsky, O., Morrell, M.J., & Vogt, B.A. (1995). Contributions of anterior cingulate cortex to behaviour. Brain, 11, 8279-8306. DiChiara, G. (2002). Nucleus accumbens shell and core dopamine: differential role in behavior and addiction. 137(1-2), 75-114. Drevets, W.C. & Raichle, M.E. (1998). Reciprocal suppression of regional cerebral blood flow during emotional versus higher cognitive processes: Implications for interactions between emotion and cognition. Cognition &  Emotion, 12, 353-385. Ehrenreich, H., Rinn, T., Kunert, H.J., Moeller, M.R., Poser, W., Schilling, L., Gigerenzer, G., & Hoehe, M.R. (1999). Specific attentional dysfunction in adults following early start of cannabis use. Psychopharmacology, 142, 295-301. Ernst, M., Romeo, R.D., & Andersen, S.L. (2009). Neurobiology of the development of motivated behaviors in adolescence: a window into a neural systems model. Pharmacology, Biochemistry and Behaviour, 93, 199-211. Ersche, K.D., Clark, L., London, M., Robbins, T.W., & Sahakian, B.J. (2006). Profile of executive and memory function associated with amphetamine and opiate dependence. Neuropsychopharmacology, 31, 1036-1047. Ersche, K.D., Fletcher, P.C., Lewis, S.J.G., Clark, L., Stocks-Gee., G., & London, M. et al. (2005). Abnormal frontal activations related to decision-making in current and former amphetamine and opiate dependent individuals. Psychopharmacology, 180, 612-635. Evans, J.S.B.T. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255-278.  Page 103    Fellows, L.K. & Farah, M.J. (2005). Dissociable elements of human foresight: A role for the ventromedial frontal lobes in framing the future, but not in discounting future rewards. Neuropsychologia, 43, 1214-1221. Ferrett, H. L., Cuzen, N.L., Thomas, K.G.F., Carey, P.D., Stein, D.J., Finn, P.R., Tapert, S.F., & Fein, G. (2011). Characterization of South African adolescents with alcohol use disorders but without psychiatric or polysubstance comorbidity. Alcohols: Clinical and Experimental Research, 35(9), 1705-1715. Fillmore, M.T., Rush, C.R., & Hays, L. (2006). Acute effects of cocaine in two models of inhibitory control: implications of non-linear dose effects. Addiction, 101, 1323-1332. Finn, P.R. & Hall, J. (2004). Cognitive ability and risk for alcoholism: short-term memory capacity and intelligence moderate personality risk for alcohol problems. Journal of Abnormal Psychology, 113, 569-581. Fontes, M.A., Bolla, K.A., Cunha, P.J., Almeida, P.P., Jungerman, F., Laranjeira, R.R., Bressan, R.A., & Lacera, A.L.T. (2011). Cannabis use before age 15 and subsequent executive functioning. The British Journal of Psychiatry, 198, 442-447. Fried, P.A., Watkinson, B., & Gray, R. (2005). Neurocognitive consequences of marihuana ? a comparison with pre-drug performance. Neurotoxicology and Teratology, 27, 231-239. Friese, M. & Hofmann, W. (2009). Control me or I will control you: Impulses, trait self-control, and the guidance of behavior. Journal of Research in Personality, 43(5), 795-805. Friese, M., Bargas-Avila, J., Hofmann, W., & Wiers, R. W. (2010). Here?s  looking at you, Bud: alcohol-related  memory structures predict eye movements for social drinkers with low executive control. Social Psychological and Personal Science, 1, 143?151.  Page 104    Frigon, A.P. & Krank, M.D. (2009). Self-coded indirect memory associations in a brief school-based intervention for substance use suspensions. Psychology of Addictive Behaviors, 23(4), 736-742. Frith, C.D., Friston, K.J., Liddle, P.F., & Frackowiak, R.S.J., (1991). Willed action and the prefrontal cortex in man: A study with PET. Proceedings of the Royal Society of Biological Sciences, 244, 241-264. Fulton, H.G., Krank, M., & Stewart, S.H. (2012). Outcome expectancy liking: a self-generated, self-coded measure predicts adolescent substance use trajectories. Psychology of Addictive Behaviors, 26(4), 870-879. Fuster, J.M. (1985). The prefrontal cortex, mediator of cross-temporal contingencies. Human Neurobiology, 4, 169-179. Gansler, D.A., Jerram, M.W., Vannorsdall, T.D., & Schretien, D.J. (2011). Comparing alternative metrics to assess performance on the Iowa Gambling Task. Journal of Clinical and Experimental Neuropsychology, 33(9), 1040-1048. George, M.S., Ketter, T.A., Parekh, P.I., Horwitz, B., Herscovitch, P., & Post, R.M. (1995). Brain activity during transient sadness and happiness in healthy women. American Journal of Psychiatry, 152, 341-351. Giancola, P.R. & Tarter, E. (1999). Executive cognitive functioning and risk for substance abuse. Psychological Science, 10(3), 203-205. Giedd, J.N. (2004). Structural magnetic resonance imaging of the adolescent brain. Annals of the New York Academy of Sciences, 1021(1), 77-85. Giedd, J., Blumenthal, J., Jeffries, N., Castellanos, F., Liu, H., Zijdenbox, A., Paus, T., Evans, A., & Ropport, J. (1999). Brain development during childhood and adolescence: a longitudinal MRI study. Nature Neuroscience, 2, 861-863. Goldman MS, Darkes J, Del Boca FK. Expectancy mediation of biopsychosocial risk for alcohol use and alcoholism. In: Kirsch I, editor. How expectancies shape  Page 105    experience. Washington, DC: American Psychological Association; 1999. pp. 233?262. Goldstein, R.Z., Craig, A.D., Bechara, A., Garavan, H., Childress, A.R., Paulus, M.P., & Volkow, N.D. (2009). The neurocircuitry of impaired insight in drug addiction. Trends in Cognitive Science, 13, 372-380. Graf, P. (1994). Explicit and implicit memory: A decade of research. In C. Umilta & M. Moscovitch (Eds.), Attention and performance 15: Conscious and nonconscious information processing. Cambridge: MIT Press. Green, L., Myerson, J., Lichtman, D., Rosen, S., & Fry, A. (1996). Temporal discounting in choice between delayed rewards: The role of age and income. Psychology and Aging, 11, 79-84. Greenwald, A.G., Nosek, B.A., & Banaji, M.R. (2003). Understanding and using the implicit association test: An improved scoring algorithm. Journal of Personality and Social Psychology, 47 (6), 197-216. Grenard, J.L., Ames, S.L., Wiers, R.W. Thush, C., Sussman, S., & Stacy, A.W. (2008). Working memory capacity moderates the predictive effects of drug-related associations on substance use. Psychology of Addictive Behaviors, 22(3), 426-432. Grisso, T., Steinberg, L., Woolard, J., Cauffman, E., Scott, E., Graham, S., Lexcen, F., Reppucci, N.D., & Schwartz, R. (2003). Juveniles? competence to stand trial: a comparison of adolescents? and adults? capacities as trial defendants. Law and Human Behaviour, 27 (4), 333-363. Gruber, S.A., Sagar, K.A., Dahlgren, M.K., Racine, M., & Lukas, S.E. (2012). Age of onset of marijuana use and executive function. Psychology of Addictive Behaviors, 26(3), 496-506.  Page 106    Halpern, C. T., Udry, J.R., Campbell, B., & Suchindran, C.M. (1993). Testosterone and  Pubertal Development as Predictors of Sexual Activity: A Panel Analysis of Adolescent Males. Psychosomatic Medicine, 55, 536-447. Hester, R. & Garavan, H. (2005). Working memory and EC: The influence of content and load on the control of attention. Memory & Cognition, 33(2), 221-233. Hitch, G.J., Halliday, S., Schaafstal, A.MN., & Schraagen, J.M. (1988). Visual working memory in young children. Memory and Cognition, 16, 120-132. Hofmann, W., Gschwendner, T., Friese, M., Wiers, R.W., & Schmitt, M. (2008). Working memory capacity and self-regulatory behavior: toward an individual differences perspective on behavior determination by automatic versus controlled processes. Journal of Personal and Social Psychology, 95, 962-977.  Holroyd, C.B. & Coles, M.G.H. (2002). The Neural Basis of Human Error Processing: Reinforcement Learning, Dopamine, and the Error-Related Negativity. Psychological Review,109(4), 679?709. Hooper, C.J., Luciana, M., Conklin, H.M., & Yarger, R.S. (2004). Adolescents? performance on the Iowa Gambling Task: Implications for the development of decision-making and ventromedial prefrontal cortex. Developmental Psychology, 40, 1148-1158.  Horvitz, J.C. (2000). Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events. Neuroscience, 96(4), 651-656. Huizinga, M., Dolan, C.V., & van der Molen, M.W. (2006). Age-related change in executive function: Developmental trends and a latent variable analysis. Neuropsychologia, 44, 2017-2036. Hyman, S.M., Garcia, M., & Sinha, R. (2006). Gender specific associations between types of childhood maltreatment and the onset, escalation and severity of substance use in cocaine dependent adults. American Journal of Drug and Alcohol Abuse, 32(4), 655-664.  Page 107    Jacobsen, L.K., Mencl, W.E., Westerveld, M., & Pugh, K.R. (2004). Impact of cannabis use on brain function in adolescents. Annals of the New York Academy of Sciences, 1021, 384-390. Jacobsen, L.K., Pugh, K.R., Constable, R.T., Westervels, M., & Mencl, W.W. (2007). Functional correlates of verbal memory deficits emerging during nicotine withdrawal in abstinent adolescent cannabis users. Biological Psychiatry, 61, 31-40. Jager, G., Block, R.I., Liujten, M., & Ramsey, N.F. (2010). Cannabis use and memory brain function in adolescent boys: a cross-sectional multicenter functional magnetic resonance imaging study. Journal of the American Academy of Child & Adolescent Psychiatry, 49(6), 561-572. Johnston, L.D., O?Malley, P.M., Bachman, J.G., & Schulenberg, J.E. (2010). Monitoring The Future National Survey Results on Drug Use: 1975-2009. Volume I: Secondary school students. Bethesda, MD: National Institute on Drug Abuse. Jones, A. C., Folk, J. R., & Rapp, B. (2009). All letters are not equal: Subgraphemic texture in orthographic working memory. Journal of Experimental Psychology: Learning, Memory and Cognition, 35, 1389?1402. Kahneman, D., & Frederick, S. (2002). Representativeness revisited: attribute substitution in intuitive judgment. In T. Gilovich, D. Griffin, and D. Kahneman (Eds.). Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge: Cambridge Univ. Press. Kalivas, P.W., Volkow, N., & Seamans, J. (2005). Unmanageable motivation in addiction: a pathology in prefrontal-accumbens glutamate transmission. Neuron, 45, 647-650. Keating, D.P. (2004). Cognitive and brain development. In R.M. Lerner & L. Steinberg (Eds.) Handbook of adolescent psychology (pp. 45-84). Hoboken, NJ: John Wiley & Sons, Inc.  Page 108    Kelly, A.B., Masterman, P.W., & Marlatt, G.A. (2005). Alcohol-related associative strength and drinking behaviours: concurrent and prospective relationships. Drug and Alcohol Review, 24(6), 489-498. Knight, J.R., Shrier, L.A., Bravender, T.D., Farrell, M., VanderBilt, J., & Shaffer, H.J. (1999). A new brief screen for adolescent substance use. Archives of Pediatric and Adolescent Medicine, 153(6), 591-596. Knight, J.R., Sherritt, L., Harris, S.K., Gates, E.C., & Chang, G. (2003). Validity of brief alcohol screening tests among adolescents: a comparison of the AUDIT, POSIT, CAGE, and CRAFFT. Alcohol: Clinical and Experimental Research, 27(1), 67-73. Knutson, B., Westdrop, A., Kaiser, E., & Hommer, D. (2000). FMRI visualization of brain activity during a monetary incentive delay task. Neuroimage, 12, 20-27. Krank, M. (2010). Dual cognitive processes and alcohol and drug misuse in transitioning adolescence. In T.W. Miller (Eds.) Transitions across the lifespan. New York: Springer Press. Krank, M., Schoenfeld, T., & Frigon, A. (2010). Self-coded indirect memory associations and alcohol and cannabis use in college students. Behavior Research Methods, 42(3), 733-738. Krank, M. D., & Goldstein, A.L. (2006). Adolescent changes in implicit cognition and prevention of substance abuse. In R. W. Wiers & A. W. Stacy (Eds.), Handbook of Implicit Cognition and Addiction (pp. 439- 453).Thousand Oaks, CA: Sage Publications. Krank, M., Stewart, S.H., O?Connor, R., Woicik, P.B., Wall, A.M., & Conrod, P.J. (2011). Structural, concurrent, and predictive validity of the substance use risk profile scale in early adolescence. Addictive and Behavior, 36(1-2), 37-46.  Page 109    Krank, M. & Wall, A. (2006). Context and retrieval effects on implicit cognition for substance use. In R.W. Wiers, & A.W. Stacy (Eds.) Handbook of implicit cognition and addiction (pp. 281-292). Thousand Oaks, CA: Sage Publications, Inc. Leigh, B.C., & Stacy, A.W. (2004). Alcohol expectancies and drinking in different age groups. Addiction, 99, 215-227. Lessing, E. (1972). Extension of personal future time perspective, age and life satisfaction of children and adolescents. Developmental Psychology, 6, 457-468. Levin, H.S., Culhane, K.A. Hartmann, J., Evankovich, K., Mattson, A.J., Harward, H. et al. (1991). Developmental changes in performance on tests of purported frontal lobe function. Developmental Neuropsychology, 7, 377-395. Lin, C-H., Song, T-J., Chen, Y-Y., Lee, W-K., & Chiu, Y-C. (2013). Reexamining the validity and reliability of the clinical version of the Iowa Gambling Task: evident from a normal subject group, Frontiers in Psychology, 4(220), 1-12. Lu, L., Grimm, J.W., Shaham, Y., & Hope, B.T. (2003). Molecular neuroadaptations in the accumbens and ventral tegmental area during the first 90 days of forced abstinence from cocaine self-administration in rats. Journal of Neurochemistry, 85(6), 1604-1613. Luna, B., Thulborn, K.R., Munoz, D.P., Merriam, E.P., Garver, K.E., Minshew, N.J. et al. (2001). Maturation of widely distributed brain function subserves cognitive development. NeuroImage, 13(5), 786-793. Madras, B.K., Fahey, M.A., Bergman, J., Canfield, D.R., & Spealman, R.D. (1989). Effects of cocaine and related drugs in nonhuman primates. [3H] cocaine binding sites in caudate-putamen. Journal of Pharmacology and Experimental Therapeutics, 251(1), 131-141. Mahmood, O.M., Goldenberg, D., Thayer, R., Migliorini, R., Simmons, A.N., & Tapert, S.F. (2013). Adolescents? fMRI activation to a response inhibition task predicts future substance use.  Addictive Behaviors, 38, 1435-1441.  Page 110    Manes, F., Sahakian, B., Clark, L., Rogers, R., Antoun, N., Aitken, M., & Robbins, T. (2002). Decision-making processes following damage to the prefrontal cortex. Brain, 125, 624-639. McCabe, K. & Barnett, D. (2000). The relation between familial factors and the future orientation of urban, African American sixth graders. Journal of Child and Family Studies, 9, 491-508. McClure, S.M., Laibson, D.I., Loewenstein, G., & Cohen, J.D. (2004). Separate neural systems value immediate and delayed monetary rewards. Science, 15, 306(5695), 503-507. McClure, S.M., Daw, N.D., & Montague, P.R. (2003). A computational substrate for incentive salience. Trends in Neuroscience, 26, 423?428. Mezzacappa, E., Kindlon, D., & Earls, F. (2001). Child abuse and performance task assessments of executive functions in boys. Journal of Child Psychological Psychiatry, 42, 1041?1048. Miller, J., Schaffer, R., & Hackley, S.A. (1991). Effects of preliminary information in a go versus no-go task. Acta Psychologia, 76, 241-292. Miyake, A., Friedman, N.P., Emerson, M.J., Witzki, A.H., Howerter, A., & Wager, T.D. (2000). The united and diversity of executive functions and their contributions to complex ?frontal lobe? tasks: a latent variable analysis. Cognitive Psychology, 41, 49-100. Monastersky, R., (2007). Who?s minding the teenage brain? Chronicle of Higher Education, 53(19), A14-A18. Monterosso, J.R., Ehrman, R., Napier, K.L., O?Brian, C.P. & Childress, A.R. (2001). Three decision-making tasks in cocaine-dependent patients: do they measure the same construct? Addiction, 96, 1825-1837.  Page 111    Moran, J.M., Macrae, C.N., Heatherton, T.F., Wyland, C.L., & Kelley, W.M. (2006). Neuroanatomical evidence for distinct cognitive and affective components of self. Journal of Cognitive Neuroscience, 18, 1586-1594. Nederkoorn, C., Guerrieri, R., Havermans, R.C., Roefs, A., & Jansen, A. (2009). The interactive effect of hunger and impulsivity on food intake and purchase in a virtual supermarket. International Journal of Obesity (Lond.), 33, 905-912. Norman, A.L., Pulido, C., Squeglia, L.M., Spadoni, A.D., Paulus, M.P., & Tapert, S.F. (2011). Neural activation during inhibition predicts initiation of substance use in adolescence. Drug and Alcohol Dependence, 119(3), 216-223. Nosek, B.A., & Banaji, M.R. (2001). The Go/No-Go association task. Social Cognition, 19(6), 625-664. Nurmi, J. (1989). Planning, motivation, and evaluation in review of the development of future orientation to the future: A latent structure analysis. Scandinavian Journal of Psychology, 30, 64-71.  Nurmi, J. (1992). Age differences in adult life goals, concerns, and their temporal extension: A life course approach to future-oriented motivation. International Journal of Behavioral Development, 15, 487-508. Paulus, M.P., Hozack, N.E., Zauscher, B.E., Frank, L., Brown, G.G., Braff, D.L. et al. (2002). Behavioral and functional neuroimaging evidence for prefrontal dysfunction in methamphetamine-dependent subjects. Neuropsychopharmacology, 26, 53-63. Peeters, M., Wiers, R.W., Monshouwer, K., et al. (2012). Automatic processes in at-risk adolescents: the role of alcohol-approach tendencies and response inhibition in drinking behavior. Addiction, 107(11), 1939-1946.  Peters, E. & Slovic, P. (2000). The springs of action: Affective and analytical information processing in choice. Personality and Social Psychology Bulletin, 26 (12), 1465-1475.  Page 112    Petit, L., Courtney, S.M., Ungerleider, L.M., & Haxby, J.V. (1998). Sustained activity in the medial wall during working memory delays. Journal of Neuroscience, 18, 9429-9437. Petrides, M., & Milner, B. (1982). Deficits on subject-ordered tasks after frontal- and temporal-lobe lesions in man. Neuropsychologica, 20 (3), 249-262. Petry, N.M., Bickel, W.K., Arnett, M. (1998). Shortened time horizons and insensitivity to future consequences in heroine addicts. Addiction, 93(5), 729-738. Pharo, H., Sim, C., Graham, M., Gross, J., & Hayne, H. (2011). Risky business: executive function, personality, and reckless behavior during adolescence and emerging adulthood. Behavioral Neuroscience, 125(6), 970-978. Pope, H.G., Gruber, A.J., Hudson, J.I., Huestis, M.A. Yurgelun-Todd, D. (2001). Neuropsychological performance in long-term cannabis users. Archives of General Psychiatry, 58, 909-915. Posner, M.I. (1980) Orienting of attention. Quarterly Journal of Experimental Psychology, 32, 3-25. Posner, M.I., Petersen, S.E., Fox, P.T., & Raichel, M.E. (1988). Localization of cognitive operations in the human brain. Science, 240, 1627-1631. Pruessner, J.C., Dedovic, K., Khalili-Mahani, N., Engert, V., Pruessner, M., Buss, C., Renwick, R., Dagher, A., Meaney, M.J., & Lupien, S. (2008). Deactivation of the limbic system during acute psychosocial stress: evidence from positron emission tomography and functional magnetic resonance imaging studies. Biological Psychiatry, 63, 234-240. Radley, J.J., Rocher, A.B., Miller, M., Janssen, W.G., Liston, C. et al. (2006). Repeated stress induces dendritic spine loss in the rat prefrontal cortex. Cerebral Cortex, 16, 313-320. Rees, C. (2008). The influence of emotional neglect on development. Paediatrics and Child Health, 18 (12), 527-534.  Page 113    Reinert, D.F. & Allen, J.P. (2007). The alcohol use disorders identification test: an update of research findings. Alcohol: Clinical and Experimental Research, 31(2), 185-199. Ross, T.P., Hanouskova, E., Giarla, K., Calhoun, E., & Tucker, M. (2007). The reliability and validity of the self-ordered pointing task. Archives of Clinical Neuropsychology, 22 (4), 449-458. Rubenstein, J.S., Meyer, D.E., & Evans, E. (2001). Executive control of cognitive processes in task switching. Journal of Experimental Psychology and Human Perception and Performance, 27, 763-797. Rudolph, M. Shr?der-Ab?, A. Sch?tz, A.P. Gregg, C. Sedikides. (2008).Through a glass, less darkly? Reassessing convergent and discriminant validity in measures of implicit self-esteem. European Journal of Psychological Assessment, 24(4), 273?281. Sabbagh, L. (2006). The teen brain, hard at work. Scientific American Mind 17(4), 20-25. Samoluk, S. B., Stewart, S. H., Sweet, S. D., & MacDonald, A. B. (1999). Anxiety sensitivity and social affiliation as determinants of alcohol consumption. Behavior Therapy, 30, 285-303. Saunders, J.B., Aasland, O.G., Babor, T.F., delaFuente, J.R., & Grant, M. (1993). Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption. Addiction, 88(6), 791-804.  Schultz, W. (2006). Behavioral theories and the neurophysiology of reward. Annual Review of Psychology, 57, 87-115. Schultz, W., Tremblay, L., & Hollerman, J.R. (2000). Reward processing in primate orbitofrontal cortex and basal ganglia. Cerebral Cortex, 10, 272-284.  Schwartz, R.H., Gruenewald, P.J., Klitzner, M., & Fedio, P. (1989). Short-term memory impairment in cannabis-dependent adolescents. American Journal of Diseases in Children, 143, 1214-1219.  Page 114    Schweinsburg, A.D., Nagel, B.J., Schweinsburg, B.C., Park, a., Theilmann, R.J., & Tapert, S.F. (2008). Abstinent adolescent marijuana users show altered fMRI response during spatial working memory. Psychiatry Research: Neuroimaging, 163912), 40-51. Schweinsburg, A.D., Schweinsburg, B.C., Cheung, E.H., Brown, G.G., Brown, S.A., & Tapert, S.F. (2005). fMRI response to spatial WM in adolescents with comorbid marijuana and alcohol use disorders. Drug Alcohol Dependence, 79, 201-210. Semendeferi, K. Armstrong, E., Schleicher, A., Zilles, K., & Van Hoesen, G.W. (2001). Prefrontal cortex in humans and apes: a comparative study of area 10. American Journal of Physical Anthropology, 114(3), 224-241. Sher, K.J., Bartholow, B.D., Peuser, K., Erickson, D.J., & Wood, M.D. (2007). Stress-response-dampening effects of alcohol: attention as a mediator and moderator. Journal of Abnormal Psychology, 116, 362-377. Sillke, J.S., Siegle, G.J., Whalen, D.J., Ostapenko, L.J., Ladouceur, C.D., & Dahl, R.E. (2009). Pubertal changes in emotional information processing: papillary, behavioral, and subjective evidence during emotional word identification. Developmental Psychopathology, 21, 7-26. Smith, E. R., & DeCoster, J. (2000). Dual process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108-131. Squeglia, L.M., Jacobus, J., & Tapert, S.F. (2009). The influence of substance use on adolescent brain development. Clinical EEG and Neuroscience, 40(1), 31-38. Stacy, A.W. (1997). Memory activation and expectancy as prospective predictors of alcohol and cannabis use. Journal of Abnormal Psychology, 106, 61-73. Stacy, A.W., Ames, S.L., & Grenard, J.L. (2006). Word association tests of associative memory and implicit processes: Theoretical and assessment issues. In R.W. Wiers &  Page 115    A.W. Stacy (Eds.), Handbook of implicit cognition and addiction (pp. 75-90). Thousand Oaks, CA: Sage. Stacy, A.W., Ames, S.L., & Knowlton, B.J. (2004). Neurologically plausible distinctions in cognition relevant to drug use etiology and prevention. Substance Use and Misuse, 39, 1571-1623. Stacy, A.W., Ames, S.L., & Leigh, B.C. (2004). An implicit cognition assessment approach to relapse, secondary prevention, and media effects. Cognitive and Behavioral Practice, 11, 139-149. Stacy, A.W., Ames, S.L., Sussman, S., & Dent, C.W. (1996). Implicit cognition in adolescent drug use. Psychology of Addictive Behaviors, 10(3), 190-203. Steinberg, L., Graham, S., O?Brien, L., Woolard, J., Cauffman, E., & Banich, M. (2009). Age differences in future orientation and delay discounting. Child Development, 80 (1), 28-44. Stewart, S. H., Samoluk, S. B., & MacDonald, A. B. (1999b). Anxiety sensitivity and substance use and abuse. In S. Taylor (Ed.), Anxiety sensitivity: Theory, research, and treatment of the fear of anxiety (pp. 287-319). Mahwah, NJ: Erlbaum. Strack, F. & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review 2004, 8 (3), 220?24. Tanji, J., Shima, K., & Mushiake, H. (2007). Concept-based behavioral planning and the lateral prefrontal cortex. Trends in Cognitive Sciences, 11 (12), 528-534. Tapert, S.F., Schweinsburg, A.D., Barlet, V.C., Brown, S.A., Frank, L., R., Brown, G.G., & Meloy, M.J. (2004). Blood oxygen level dependent response and spatial working memory in adolescents with alcohol use disorders. Alcoholism: Clinical and Experimental Research, 28, 1577-1586.  Thush, C. & Wiers, R.W. (2007). Explicit and implicit alcohol-related cognitions and the prediction of future drinking in adolescents. Addictive Behaviors, 32, 1367-1383.  Page 116    Thush, C., Wiers, R.W., Ames, S.L., Grenard, J.L., Sussman, S., & Stacy, A.W. (2008). Interactions between implicit and explicit cognition and working memory capacity in the prediction of alcohol use in at-risk adolescents. Drug and Alcohol Dependence, 94, 116-124. Tice, D.M., Bratslavsky E., & Baumeister, R.F. (2001). Emotional distress regulation takes precedence over impulse control: if you feel bad, do it! Journal of Personality and Social Psychology, 80,153?167. Tiffany, S.T. (1990). A cognitive model of drug urges and drug-use behavior: Role of automatic and nonautomatic processes. Psychological Review, 97(2), 147-168. van der Vorst, H., Engels, R.C.M.E., Meeus, W., Dekovic, M., & Leeuwe, J.V. (2005). The role of alcohol-specific socialization in adolescents? drinking behavior. Addiction, 100, 1464-1476. van Leijenhorst, L. Moor, B.G., OpdeMacks, Z.A., Rombouts, S., Westenberg, P.M., & Crone, E.A. (2010a). Adolescent risky decision-making: Neurocognitive development of reward and control regions. NeuroImage, 51, 345-355. van Leijenhorst, L., Zanolie, K., van Meel, C.S., Westenberg, P.M., Rombouts, S.A., & Crone, E.A. (2010b). What motivates the adolescent? Brain regions mediating reward sensitivity across adolescence. Cerebral Cortex, 10 (20), 61-69. Verdejo-Garcia, A., Lawrence, A.J., & Clark, L. (2008). Impulsivity as a vulnerability marker for substance-use disorders: review of the findings from high-risk research, problem gamblers and genetic association studies. Neuroscience and Biobehavioral Reviews, 32(4), 777-810. Vogt, B. A., Finch, D. M., & Olson, C. R. (1992). Functional heterogeneity in cingulate cortex: The anterior executive and posterior evaluative regions. Cerebral Cortex, 2, 435?443.  Page 117    Wall, A. M., Hinson, R. E., McKee, S. A., & Goldstein, A. (2001). Examining alcohol outcome expectancies in laboratory and naturalistic bar settings: A within-subject experimental analysis. Psychology of Addictive Behaviors, 15, 219?226. Welsh, M.C., Pennington, B.F., & Groisser, D.B. (1991). A normative-developmental study of executive function: A window on prefrontal function in children. Developmental Neuropsychology, 7, 131-149. Wesley, M.J., Hanlon, C.A., Porrino, L.J. (2011). Poor decision-making by chronic marijuana users is associated with decreased functional responsiveness to negative consequences. Psychiatry Research, 51-59. West, R.L. (1996). An application of prefrontal cortex function theory to cognitive aging. Psychological Bulletin, 120, 272-292.  Whalen, P.J., Bush, G., McNally, R.J., Wilhelm, S., McInerney, S.C., Jenike, M.A., & Rauch, S.C. (1998). The Emotional Counting Stroop Paradigm: A functional magnetic resonance imaging probe of the anterior cingulate affective division. Biological Psychiatry, 44,1219?1228. Whitbourne, S.K., Sneed, J.R., & Sayer, A. (2009). Psychosocial development from college through middle: A 34-year sequential study. Developmental Psychology, 45, 1328-1340. Whiteside, S.P. & Lynam, R. (2009). Understanding the role of impulsivity and externalizing psychopathology in alcohol abuse: Application of the UPPS Impulsive Behaviour Scale. Personality Disorders: Theory, Research, and Treatment, S(1), 69-79. Wiers, R.W., Ames, S., Hofmann, W., Krank, M., & Stacy, A.W. (2010). Impulsivity, impulsive and reflective processes and the development of alcohol use and misuse in adolescents and young adults. Frontiers in Psychology, 1, 144. Wiers, R.W., Bartholow, B.C, vandenWildenberg, E., Thush, C., Engels, R.C.M.E., Sher, K.J., Grenard, J., Ames, S.L.,  and Stacy, A.W. (2007). Automatic and controlled  Page 118    processes and the development of addictive behaviors in adolescents: A review and a model. Pharmacology, Biochemistry and Behavior, 86, 263-283.  Wiers,  R.W., Beckers, L., Houben, K., & Hofmann, W. (2009). A short fuse after alcohol: Implicit power associations predict aggressiveness after alcohol consumption in young heavy drinkers with limited EC. Pharmacology, Biochemistry and Behaviour, 93(3), 300-305. Wiers, R.W., Eberl, C., Rinck, M., Becker, E.S., & Lindenmeyer, J. (2011). Retraining automatic action tendencies changes alcohol patients? approach bias for alcohol and improves treatment outcome. Psychological Science, 22(4), 490-497. Wiers, R.W. & Stacy, A.W. (2010). Are alcohol expectancies associations? Comment on Moss & Albery (2009). Psychological Bulletin, 136(1), 12-16. Wiers, R.W., Stacy, A.W., Ames, S.L., Noll, J.A., Sayette, M.A., Zack, M., & Krank, M. (2002). Implicit and explicit alcohol-related cognitions. Alcoholism: Clinical and Experimental Research, 26, 129-137. Williams, B.J., & Kaufmann, L.M.(2012). Reliability of the Go/No-Go association task. Journal of Experimental Social Psychology, 48(4), 879-891. Williams, B.R., Ponesse, J.S., Schachar, R.J., Logan, G.D., & Tannock, R. (1999). Development of inhibitory control across the life span. Developmental Psychology, 35, 205-213. Woicik, P.A., Sherry, H.S., Pihl, R.O., and Conrod, P.J. (2009). The substance use risk profile scale: A scale measuring traits linked to reinforcement-specific substance use profiles.  Zajonc, R.B. (1968). Attitudinal effects of mere exposure. Journal of Personality and Social Psychology, 9(2), 1-27. Zuckerman, M., Kolin, E. A., Price, L. & Zoob, I. (1964). Development of a sensation-seeking scale. Journal of Consulting and Clinical Psychology, 28, 477-82.  Page 119    Appendix 1. Substance use risk profile scale  Shows the exploratory factor structure with varimax rotation: Four-factor solution for SURPS items (Wave 2 of PATH, Krank et al., 2011).     Factors  h2 Hopelessness Impulsivity Sensation Seeking  Anxiety Sensitivity Hopelessness      1.  I am content (R) .478 0.685 0.025 0.082 0.040 4.  I am happy (R) .624 0.787 -0.032 0.040 0.032 7.  I have faith that my future holds great promise (R) .468 0.640 0.162 -0.139 -0.116 13.  I feel proud of my accomplishments (R) .504 0.685 0.106 -0.107 -0.106 17.  I feel that I'm a failure  .459 0.595 0.242 0.101 0.192 20.  I feel pleasant (R) .549 0.738 0.049 -0.039 0.006 23.  I am very enthusiastic about my future (R) .527 0.702 0.122 -0.082 -0.113 Impulsivity      2.  I often don't think things through before I speak .475 0.039 0.685 -0.032 -0.057 5.  I often involve myself in situations that I later regret being involved in .433 0.162 0.612 0.163 0.075 11.  I usually act without stopping to think .738 0.111 0.845 0.108 -0.019 15.  Generally, I am an impulsive person .293 0.008 0.471 0.263 0.037 22.  I feel I have to be manipulative to get what I want .216 0.116 0.367 0.106 0.238 Sensation Seeking      3.  I would like to skydive .362 -0.088 0.140 0.576 -0.054 6.  I enjoy new and exciting experiences even if they are unusual .307 -0.152 0.213 0.488 -0.016 9. I like doing things that frighten me a little .379 -0.021 0.288 0.532 -0.114 12. I would like to learn how to drive a motorcycle .240 -0.026 0.141 0.469 -0.001 16. I am interested in experience for its own sake even if it is illegal .413 0.195 .456 0.399 -0.082 19.  I would enjoy hiking long distances in wild and uninhabited territory .121 -0.151 -0.078 0.296 0.064 Anxiety Sensitivity      8.  It's frightening to feel dizzy or faint .294 -0.126 -0.094 -0.049 0.517 10.  It frightens me when I feel my heart beat change .315 -0.038 -0.049 -0.020 0.557 14.  I get scared when I'm too nervous .370 0.024 0.065 -0.031 0.603 18.  I get scared when I experience unusual body sensations .308 0.039 -0.002 -0.101 0.545 21.  It scares me when I'm unable to focus on a task .366 -0.068 -0.016 -0.006 0.601 Eigenvalue  4.46 3.22 2.44 1.46 Cronbach?s Alpha  0.861 0.743 (0.752)* 0.627  (0.637)* 0.702 Notes: Standardized factor loadings presented. (R) = Reversed scored. h2 = Communality. Rotated factor loadings greater than .45 shown in bold.  The factor analyses used maximum likelihood estimator with robust standard errors (MLR) and varimax rotation. Items are grouped according to intended subscale and factors are labeled according to the content of the highest loading factors. *Cronbach?s alpha for subscales with problem items deleted (i.e., item 22 deleted from Impulsivity subscale; items 16 and 19 deleted from Sensation Seeking subscale).  Page 120    2.     Word Associates Note this section shows the paper based version.  This measure has been converted to a web-based delivery including self-coding of responses (Krank et al., 2010; Frigon and Krank, 2009). Write the first word you think of next to each word. For example, if the word is ?doctor? you might write ?nurse? (doctor: NURSE). You want to work as quickly as possible, write the first thing that you think of.  Ring:  Closet:   Mug:  Rock:  Bud:  Draft:  Hot:  Weed:  Speed:  Ice:  Scrap:  Screw:  Control:  Rave:  Smack:  Score:  Pot:  Bottle:  Shot:  Trap:  Blunt:  Fling:  Hit:  Joint:  Pipe:  Slam:  Date:  Cooler:  Rubber:  Lay:  Roach:  Blow:    Page 121    3. Situational Associates Note this section shows the paper based version.  This measure has been converted to a web-based delivery including self-coding of responses (Krank et al., 2010; Frigon & Krank, 2009). In this section we want you to think of the first action or behaviour that comes to mind when you think of?.. Hanging out with friends   Going to a party   After school   At home without parents   Going to the mall   Staying out really late   A typical  Friday or Saturday night   Sneaking out   At home with family   At a friend?s house    Page 122    4. Outcome Associates Note this section shows the paper based version.  This measure has been converted to a web-based delivery including self-coding of responses (Krank et al., 2010; Frigon & Krank, 2009). In this section we want you to think of the first action or behaviour that comes to mind when you think of?.. Forgetting problems    Feeling angry    Having fun    Feeling loved    Feeling really relaxed    Feeling upset    Feeling dreamy    Feeling hot    Feeling bored    Laughing    Feeling lonely      Page 123    5. Substance use outcome expectancy liking. This measure provided a direct open-ended assessment of substance specific outcome expectancies (Krank and Goldstein, 2006, Fulton et al., in press).  The question asked:  ?What would you expect to happen if you ______??  For alcohol use the stem was ?drank a moderate amount of alcohol.?  For cannabis use the stem was ?used cannabis.?   Alcohol wording  This question asks you to tell us about the anticipated effects of using a moderate amount of alcohol. We do not assume that you have used alcohol. Please answer this question even if you have never had a drink of alcohol. We are interested in what you think would happen. Please enter the four most important things that you would expect or anticipate to happen if you drank a moderate amount of alcohol. Then indicate how much you would like or not like this outcome. Cannabis wording This question asks you to tell us about the anticipated effects of using cannabis.  We do not assume that you have used cannabis.  Please answer this question even if you have never used cannabis.  We are interested in what you think would happen.    Please enter the four most important things that you would expect or anticipate to happen if you used cannabis.  Then indicate how much you would like or not like this outcome.    Page 124    6.       The CRAFFT questionnaire (Knight, Shrier, Bravender, Farrell, Bilt, & Shaffer, 1999) 1.  Have you ever ridden in a car driven by someone (including yourself) who was ? high? or had been using alcohol or drugs?  2.  Do you ever use alcohol or drugs to relax, feel better about yourself, or fit in?  3.  Do you ever use alcohol or drugs while you are by yourself, alone? 4.  Do you ever forget things you did while using alcohol or drugs? 5.  Do your family or friends ever tell you that you should cut down on your drinking or drug use?  6. Have you ever gotten into trouble while you were using alcohol or drugs?   7.      The original AUDIT questionnaire as printed in the WHO Report, 2001 (Babor, Higgins-Biddle, Saunders, & Monteiro, 1989)    Page 125    8.    The Cannabis Use Disorders Identification Test (CUDIT) (Adamson & Sellman, 2003) Have you used any cannabis over the past 6 months?  Yes  No  If YES, please answer the following questions about your cannabis use.    Please tick the box that is most correct for you in relation to your cannabis use over the past 6 months    1. How often do you used cannabis?     never  monthly or less  2 ? 4 times a month  2 ? 3 times a week  4 or more times a week   Y Y Y Y Y    2. How many hours were you ??stoned?? on a typical day when you had been using cannabis?    1 or 2          3 or 4    5 or 6       7 to 9         10 or more   Y Y Y Y Y   3. How often were you ??stoned?? for 6 or more hours?      never  less than monthly  monthly  weekly  daily or almost daily   Y  Y   Y   Y  Y   4. How often during the past 6 months did you find that you were not able to stop using cannabis once you had started?   never  less than monthly  monthly  weekly   daily or almost daily   Y  Y   Y   Y  Y     5. How often during the past 6 months did you fail to do what was normally expected from you because of using cannabis?   never  less than monthly  monthly  weekly   daily or almost daily   Y  Y   Y   Y  Y   6. How often during the past 6 months did you needed to use cannabis in the morning to get yourself going after a heavy session      of using cannabis?  never  less than monthly  monthly  weekly  daily or almost daily   Y  Y   Y   Y  Y   7. How often during the past 6 months did you have a feeling of guilt or remorse after using cannabis?    never  less than monthly  monthly  weekly   daily or almost daily   Y  Y   Y   Y  Y   8. How often in the past 6 months have you had a problem with your memory or concentration after using cannabis?   never  less than monthly  monthly  weekly   daily or almost daily   Y  Y   Y   Y  Y   9. Have you or someone else been injured as a result of your use of cannabis over the past 6 months?     no   yes      Y       Y    10. Has a relative, friend or a doctor or other health worker been concerned about your use of cannabis or suggested you cut down       over the past 6 months?   no   yes      Y    Y      Page 126    9.    Iowa Gambling Task ? Donkey Version (Crone & vanderMolen, 2004)   Participants view the stimulus display of a donkey and four doors, as shown in the top panel. Above each door is a performance panel that shows green(win):red(loss), the ratio changes for each door each time it is opened, depending on the amount of win/loss. When a participant chooses a door with greater loss than win, the amount of red in the bar increases and the green decreases. When a participant chooses a door with greater win than loss, the amount of green in the bar increases and the red decreases; the ratio changes with each choice made. The bottom panel shows the outcome display that appears above each performance panel for each door. When a door is chosen, the outcome display indicates how many apples have been won and how many apples lost in that turn. In this example, the higher pile on the indicates number of apples lost (colored red) and the pile on the right indicates number of apples won (colored green). Therefore, with each turn the participant can view the number of apples won and lost for each door, while also viewing the ratio of wins:losses for each door.   Page 127  10.    Future Orientation Questionnaire (Steinberg et al., 2009) Question 1 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people like to plan         BUT        Other people like to jump    things out one step at a  right into things without   time  planning them out beforehand   Question 2 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people spend very          BUT        Other people spend a lot    little time thinking about of time thinking about   how things might be in how things might be in    the future the future  Question 3 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people like to think         BUT        Other people don?t think    about all of the possible  it?s necessary to think   good and bad things that about every little   can happen before possibility before making   making a decision a decision   Question 4 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people usually         BUT        Other people just act-they    think about the   don?t waste time thinking    consequences before they about the consequences         do something   Question 5 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people would         BUT        Other people will give up    rather be happy today  their happiness now so   than take their chances on  that they can get what   what might happen in the they want in the future    future   Question 6 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people are always         BUT        Other people find making    making lists of things to a list of things to do a    do  waste of time   Question 7 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people make         BUT        Other people usually    decisions and then act  make plans before going    without making a plan  ahead with their decisions    Page 128    Question 8 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people would         BUT        Other people would    rather save their money  rather spend their money    for a rainy day than spend  right away on something    it right away on fun than save it for a    something fun rainy day   Question 9 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people have trouble        BUT        Other people are usually    imagining how things  pretty good at seeing in    might play out over time advance how one thing   can lead to another   Question 10 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people don?t spend         BUT        Other people think a lot    much time worrying about how their decisions    about how their decisions will affect others    will affect others   Question 11 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people often think         BUT        Other people don?t even    what their life will be like  try to imagine what their    10 years from now life will be like in 10 years   Question 12 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people think that         BUT        Other people think that    planning things out in things work out better if    advance is waste of time they are planned out in advance   Question 13 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people like to take         BUT        Other people find that    big projects and break breaking big projects    them down into small down into small steps    steps before starting to isn?t really necessary    work on them   Question 14 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people take life         BUT        Other people are always    one day at a time without  thinking about what    worrying about the future tomorrow will bring        Page 129    Question 15 Really True Sort of True Really True  Sort of True For Me For Me For Me  For Me                            Some people tink it?s         BUT        Other people think it?s    better to run through all better to make up your    the possible outcomes of mind without worrying    a decision in your mind about what things you can?t    before deciding what to do predict   Scoring: All items are scored left to right on a scale of 1-4. Reverse score items 1, 3, 4, 6, 8, 11, and 14, so that higher scores indicate a stronger future orientation. Future Orientation total score is the unweighted average of all 15 items. Planning Ahead is the unweighted average of items 1, 6, 7, 12, and 13. Time Perspective is the unweighted average of items 2, 5, 8, 11, 14. Anticipation of Future Consequences is the unweighted average of items 3, 4, 9, 10, 15. 

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