behavioral inhibition and activation systems: differences ... dvorak lau-barraco 2009.pdf ·...

Behavioral Inhibition and Activation Systems: Differences in SubstanceUse Expectancy Organization and Activation in Memory

Jeffrey S. Simons and Robert D. DvorakThe University of South Dakota

Cathy Lau-BarracoResearch Institute on Addictions

We used multidimensional scaling to model the semantic network of alcohol and marijuana expectancies(N � 897). Preference mapping was used to estimate vectors representing patterns of activation throughthe network as a function of levels of behavioral inhibition (BIS) and behavioral activation (BAS).Individuals with low BIS combined with high BAS levels exhibited patterns of activation emphasizingbehavioral activation similar to heavier drug users in previous research. High BIS, low BAS individualsexhibited activation patterns with greater emphasis on inhibitory expectancies similar to low-level users.Differences in expectancy activation patterns were maintained after controlling for substance use andgender. Individual differences in BIS/BAS are associated with the organization of semantic networks andpatterns of activation of expectancies contributing to differences in substance use behavior.

Keywords: alcohol, marijuana, behavioral inhibition, expectancies, temperament

Not surprisingly, individuals who expect drugs to provide desirableoutcomes use them at greater rates. Research on drug use expectancieshas identified dimensions of expectancies for many drugs includingalcohol, marijuana, and stimulants (Aarons, Brown, Stice, & Coe,2001; Aarons, Goldman, Greenbaum, & Coovert, 2003; Brown,Glodman, Inn, & Anderson, 1980). Prospective and experimentalresearch has demonstrated that expectancies develop prior to drug useinitiation, predict future use patterns, and affect drug use behavior andexperience (Christiansen, Smith, Roehling, & Goldman, 1989;Fillmore, Carscadden, & Vogel-Sprott, 1998; Kirk, Doty, & de Wit,1998; Nagoshi, Noll, & Wood, 1992; Smith, Goldman, Greenbaum,& Christiansen, 1995).

Drug expectancies develop and change through a combinationof social learning mechanisms and direct experience (Goldman,Del Boca, & Darkes, 1999; Martino, Collins, Ellickson, Schell, &McCaffrey, 2006; Smith et al., 1995). Prior to ever trying a drug,individuals observe the effects in parents and peers, read aboutcelebrity drug use in the media, see actors using drugs in movies,and are exposed to a diverse range of educational material aboutthe effects of various substances. This prior knowledge contributesto the likelihood of drug experimentation and shapes the resulting

direct experience, which in turn has reciprocal effects on expect-ancies (Goldman et al., 1999; Smith et al., 1995; Stacy, 1995;Stacy, Newcomb, & Bentler, 1991). Through this developmentalsocial learning process drug use expectancies are formed andrefined and expectancies contribute to individual differences indrug use behavior. In addition, temperament (or broadly, indi-vidual differences) may shape the social environment (Hartup& van Lieshout, 1995; Scarr & McCartney, 1983; Tarter, 2002;Wills & Dishion, 2004), influence the interpretation and memoryof learning experiences (Carver & White, 1994; Smith, Williams,Cyders, & Kelley, 2006), and affect the experience of direct drugeffects, making them more or less pleasurable and reinforcing(Brunelle, Barrett, & Pihl, 2007; Erblich & Earleywine, 2003;Levenson, Oyama, & Meek, 1987; Ray, McGeary, Marshall, &Hutchison, 2006).

In the following sections, we review support for a model inwhich individual differences in levels of behavioral inhibition(BIS) and activation (BAS) affect the organization and patterns ofactivation of expectancies in memory and thus contribute to sub-stance use behavior. We review research on associations betweentemperament and expectancies, present research on cognitive net-work models of expectancy organization and activation, and thendiscuss how associations between BIS/BAS and substance usemay be mediated by differences in expectancy organization andactivation patterns.

Associations Between Temperament and Expectancies

Temperament and other individual difference characteristics areassociated with differences in both drug expectancies and usebehavior (Anderson, Schweinsburg, Paulus, Brown, & Tapert,2005; Goldman et al., 1999; Hendershot, Stoner, George, & Nor-ris, 2007). Expectancies have been shown to mediate some asso-

Jeffrey S. Simons and Robert D. Dvorak, Department of Psychology,The University of South Dakota; Cathy Lau-Barraco, Research Institute onAddictions, University of Buffalo.

This research and article preparation was supported in part by a NationalInstitute on Alcohol Abuse and Alcoholism Grant AA014573 to Jeffrey S.Simons. The authors would like to express their appreciation to Dr. TonyCoxon for his assistance during the preparation of this article.

Correspondence concerning this article should be addressed to Jeffrey S.Simons, Department of Psychology, The University of South Dakota, 414E. Clark Street, Vermillion, SD 57069. E-mail: [email protected]

Psychology of Addictive Behaviors © 2009 American Psychological Association2009, Vol. 23, No. 2, 315–328 0893-164X/09/$12.00 DOI: 10.1037/a0015834

315

ciations between temperament and other individual differencecharacteristics and drug use (Hendershot et al., 2007; McCarthy,Kroll, & Smith, 2001a; McCarthy, Miller, Smith, & Smith, 2001b).For example, research on the acquired preparedness model indi-cates that disinhibition may foster the development of more pos-itive and less negative alcohol expectancies (Anderson, Smith, &Fischer, 2003; McCarthy et al., 2001a, 2001b). The acquiredpreparedness model posits that individual difference characteris-tics affect learning experiences (e.g., an individual may be predis-posed to attend to rewards more than negative consequences) andthus the development of expectancies. Associations between per-sonality or other individual difference characteristics and sub-stance use are thus expected to be partially mediated by expect-ancies. In addition, research has indicated that temperament maymoderate associations between expectancies and use (Anderson,Smith, et al., 2005b; Fischer, Smith, Anderson, & Flory, 2003).Thus, previous research indicates that individual difference char-acteristics that are positively associated with substance use may beexpected to promote positive drug expectancies, reduce negativedrug expectancies, and moderate expectancy-use relationshipscontributing to greater substance use. Although less studied, indi-vidual difference characteristics that are negatively associated withsubstance use may also be expected to be associated with sub-stance use via similar mechanisms. Just as beliefs about substancesmay be shaped in part by temperament, so too may the organiza-tion and activation patterns of these expectancies.

Cognitive Network Models

Research on memory network models has examined how theorganization and activation patterns of expectancies in memorymay underlie differences in drug use behavior (Aarons et al., 2003;Linkovich-Kyle & Dunn, 2001; Rather, Goldman, Roehrich, &Brannick, 1992). Multidimensional scaling (MDS) is an empiricalprocedure to model similarity or likelihood judgments of objects orattributes into a dimensional space based on Euclidean distances(Davison, 1992; Rather & Goldman, 1994; Rather et al., 1992).Attributes that are closer together in the network are more likely tobe coactivated in memory. The resulting network may be describedby the axes or dimensions of the space and preferred points,theoretical vectors of activation, and dimensional weights can beidentified for subgroups (Davison, 1992; Rather & Goldman,1994; Rather et al., 1992). Multidimensional scaling approaches tomemory networks are supported by expected associations withsubstance use, intervention effects, age, and have been cross-validated with findings from research employing free-word-association techniques (Dunn & Goldman, 1998, 2000; Dunn, Lau,& Cruz, 2000).

Research on memory network models indicates that drug usersnot only hold more positive (and less negative) expectancies, butthat they differ in the organization and activation patterns ofexpectancies in memory. For example, heavier drinkers tend toactivate expectancies of positive arousing effects, whereas lighterdrinkers activate positive sedating effects in response to alcoholcues (Del Boca, Darkes, Goldman, & Smith, 2002; Rather &Goldman, 1994; Rather et al., 1992). In addition, activation pat-terns appear to shift from more negative alcohol expectanciestoward positive, arousing expectancies as individuals mature fromchildhood through adolescence (Dunn & Goldman, 1996, 1998).

Similar findings have been observed for marijuana (Alfonso &Dunn, 2007; Linkovich-Kyle & Dunn, 2001). In young adults,marijuana expectancies may be organized along two dimensions:detached-aware and relaxed-agitated. Heavier users appear to ac-tivate positive expectancies associated with relaxation and cogni-tive enhancement first, whereas negative agitated expectancies areleast likely to be activated. In contrast, nonusers activate expect-ancies of cognitive impairment and other negative effects, whereasexpectations of cognitive enhancement are least likely to be acti-vated. For nonusers, the relaxed-agitated is compressed such thatexpectancies of “stoned” and “annoying” are perceived as simi-larly likely and appear to be activated closer together. In contrast,for heavy users, the relaxed-agitated dimension is emphasized andthe detached-aware dimension is compressed such that expectan-cies such as “stoned” and “creative” are closer together(Linkovich-Kyle & Dunn, 2001).

Advertisements and interventions designed to alter alcohol ex-pectancies may function in part by altering the representation ofexpectancies in memory, changing activation patterns such thatactivation patterns shift from positive aroused expectancies towardsedating and more negative expectancies (Dunn et al., 2000; Dunn& Yniguez, 1999). Thus, although research on memory networkmodels of expectancies has focused primarily on differencesacross age and use groups, the experimental research demonstratesthat learning experiences (aside from direct experience with thedrug) may alter the representation of drug expectancies in memory.

Understanding of how drug use expectancies develop may beenhanced by examining the role of temperament and other indi-vidual difference characteristics in contributing to the developmentof drug expectancies. Alcohol expectancies bear similarity to cir-cumplex models of affect and personality (Goldman et al., 1999).Cognitive network models characterized by spreading activation ofconceptual nodes provide a theoretical basis by which an individ-ual’s characteristic emotion and behavior patterns may influencethe accessibility and integration of expectancies that are moreclosely linked to personal characteristics (cf. Goldman et al.,1999). For example, individuals with high positive affectivity arecharacteristically experiencing positive and arousing emotions. Aspreading activation model suggests that this behavioral patternmay facilitate the activation of similarly arousing and positivealcohol expectancies. Similarly, the acquired preparedness modelsuggests that disinhibition may bias the perception and retention ofpositive reinforcing characteristics of a drug relative to potentialnegative consequences and increase the likelihood of acting onthese positive expectancies (McCarthy et al., 2001a). Thus, re-search on cognitive network models suggests that individual dif-ference characteristics may not only be associated with globallevels of positive and negative substance use expectancies, butmay also be related to the organization and activation of expect-ancies in memory. The present study extends previous research onassociations between individual difference characteristics and ex-pectancies to examine associations between BIS/BAS and expect-ancy organization and activation patterns.

Behavioral Inhibition and Activation

BIS and BAS are theoretical brain systems that are associatedwith differences in temperament and personality functioning(Carver & White, 1994; Gray, 1987; Torrubia, Åvila, Molto, &

316 SIMONS, DVORAK, AND LAU-BARRACO

Caseras, 2001). These motivational systems may affect the shapingand encoding of developmental experiences. The behavioral inhi-bition system is sensitive to cues of punishment and nonrewardand is instrumental in ceasing behavioral output (Gray, 1987;Torrubia et al., 2001). The behavioral activation system is sensitiveto cues of reward and is instrumental in activating goal directedbehavior, including behavior to avoid punishment and negativeconsequences (Gray, 1987).

BIS and BAS activity levels are associated with alcohol, mari-juana, and methamphetamine use (Cloninger, 1987; Johnson,Turner, & Iwata, 2003; Knyazev, 2004; O’Connor & Colder, 2005;Pardo, Aguilar, Molinuevo, & Torrubia, 2007; Simons, Dvorak, &Batien, 2008). For example, BAS activity is positively associatedwith alcohol and marijuana use (O’Connor & Colder, 2005; Pardoet al., 2007; Simons et al., 2008). In contrast, the BIS is negativelyassociated with alcohol use frequency and quantity (Pardo et al.,2007) as well as marijuana use (Simons & Arens, 2007; Simons etal., 2008). Across studies, the BAS appears to be a fairly robustpredictor of substance use involvement, whereas evidence for therole of BIS is less consistent (Johnson et al., 2003; Knyazev,2004). The BIS may have complex associations with substance usebecause it is positively associated with anxiety, providing a moti-vational stimulus for use, yet also associated with risk aversion(Caseras, Åvila, & Torrubia, 2003; Cloninger, 1987). One reasonfor null findings regarding the BIS may be that its effects aredependent upon relative levels of BAS. Indeed, there is evidencethat the BIS and BAS interact such that BAS attenuates negativeassociations between the BIS and marijuana use (Simons & Arens,2007; Simons et al., 2008). Thus, considering interactions betweenthese dimensions is important.

The BIS exhibits modest positive associations primarily withnegative expectancies, whereas the BAS exhibits modest positiveassociations with positive expectancies (Simons & Arens, 2007).Furthermore, BIS attenuates the association between positive ex-pectancies and marijuana use and marginally increases the asso-ciation between negative expectancies and use (Simons & Arens,2007). Thus, one way in which BIS/BAS may affect drug usebehavior is by either influencing drug use expectancies or moder-ating the effects of expectancies on behavior. Integrating theresearch findings on cognitive network models of expectancies andresearch on associations between substance use and BIS/BASsuggests that characteristic high BAS activity and concomitant lowBIS activity may facilitate the development of a substance useexpectancy network in which relatively activating expectancies areinitially and easily accessed in response to drug cues. In contrast,individuals with low BAS activity and high BIS activity may bemore likely to activate expectancies conducive to behavioral inhi-bition.

In summary, previous research indicates that BIS/BAS activityis associated with substance use (Johnson et al., 2003; O’Connor &Colder, 2005; Simons et al., 2008), global levels of positive andnegative substance use expectancies (Simons & Arens, 2007), andmoderates associations between expectancies and substance use(Simons & Arens, 2007). However, the psychological mechanismsunderlying these associations are less clear. Cognitive networkmodels provide a framework to explicate how individual differ-ences in BIS/BAS activity may contribute to the formation ofsubstance use expectancy networks that mirror these individualdifferences in affective and behavioral patterns and how differ-

ences in expectancy organization and activation patterns maymanifest in observed differences in substance use behavior.

Alcohol and Marijuana Use

Alcohol and marijuana are commonly used drugs among youngadults, and use of the two drugs is positively correlated. Comparedwith nonmarijuana users, marijuana users start drinking at anearlier age, drink more frequently, in greater quantity, and reporthigher rates of alcohol problems (Collins, Bradizza, & Vincent,2007; Midanik, Tam, & Weisner, 2007; Shillington & Clapp,2002, 2006). Marijuana use also increases the association betweenalcohol consumption and problems (Simons & Carey, 2006; Si-mons, Gaher, Correia, Hansen, & Christopher, 2005). Whether thiseffect is because of the simultaneous use of the two drugs orindividual differences is unclear (Collins et al., 2007; Midanik etal., 2007; Simons & Carey, 2006; Simons et al., 2005). Onepossibility for differences in drinking behavior among marijuanausers could be differences in alcohol expectancies. We thus ex-amine differences in alcohol expectancy activation patterns as afunction of marijuana use status and test whether differences maybe accounted for by correlated differences in BIS/BAS levels andalcohol consumption.

Hypotheses

We suggest that differences in BIS/BAS may function to influ-ence organization of drug use expectancies in memory, which inturn contributes to level of substance use. For example, BIS/BASmay influence learning experiences either by promoting or inhib-iting drug experimentation or, given a common experience (e.g.,drug education class), may bias attention and processing of infor-mation (e.g., heightened attention to potential consequences, threatcues). This may be expected to result in differences in the orga-nization and activation patterns of the representation of expectan-cies in memory. We hypothesize that relative to individuals whoare highly sensitive to punishment (i.e., high BIS activity) and lesssensitive to reward (i.e., low BAS activity), individuals who arehighly sensitive to reward (i.e., high BAS activity) and less sen-sitive to punishment (i.e., low BIS activity) will have alcoholexpectancy activation that emphasizes higher positive arousal.Similarly, we hypothesize that relative to individuals who arehighly sensitive to punishment (SP) and less sensitive to reward(SR), individuals who are highly sensitive to reward and lesssensitive to punishment will have marijuana expectancy activationthat emphasizes more positive relaxed and aware effects. Thus, forboth alcohol and marijuana, individuals with high SR and low SPare hypothesized to expect positive approach effects of the drugsas more likely relative to individuals with low SR and high SP,who are hypothesized to emphasize more inhibiting effects. BIS/BAS activity is expected to be associated with expectancy activa-tion patterns as well as differences in marijuana and alcohol use.Use is hypothesized to be greatest for individuals with high SR andlow SP, and lowest among individuals with low SR and high SP.Given that BIS/BAS activity is posited to affect the representationof expectancies in memory, we expect to observe associationsbetween BIS/BAS levels and expectancy activation patterns aftercontrolling for use level, although given the hypothesized recip-rocal associations between substance use and expectancies these

317SUBSTANCE USE EXPECTANCY ACTIVATION

observed differences in activation patterns should be attenuated.Finally, we examine differences in alcohol expectancy activationas a function of marijuana use status. Compared with marijuanausers, nonusers are hypothesized to emphasize more inhibitingeffects similar to lighter drinkers in previous research. We thenexplore whether any observed differences are maintained aftercontrolling for BIS/BAS levels and alcohol consumption. Weexamine these hypotheses through the use of multidimensionalscaling and preference mapping.

Method

Participants

Participants were 897 young adults. Women made up 72% ofthe sample. The sample ranged in age from 18 to 25 years (M �19.67, SD � 1.67). Ninety-six percent of the sample were White,1% Asian, 1% Native American/Alaskan Native, and 2% other ordid not respond. Ninety-eight percent were non-Hispanic. Partic-ipants were recruited through a university research participantpool.

Measures

The Sensitivity to Punishment and Sensitivity to Reward Ques-tionnaire (Torrubia et al., 2001) is a 48-item two-factor scale witha dichotomous (yes/no) response format. For the current study, weused a factor solution obtained by O’Connor and colleagues(O’Connor, Colder, & Hawk, 2004). The sensitivity to reward(SR) scale has 17 items (� � .78), and the sensitivity to punish-ment (SP) scale has 18 items (� � .85). Construct validity of thesescales as indicators of behavioral inhibition and behavioral acti-vation systems is supported by expected relationships with otherindividual difference measures (e.g., anxiety, impulsivity, neurot-icism) and relative orthogonality (Caseras et al., 2003; Torrubia etal., 2001).

Expectancies. Thirty-three alcohol (Rather et al., 1992) and 54marijuana (Linkovich-Kyle & Dunn, 2001) expectancies wererated on 9-point scales (1 � never, 9 � always). These effectexpectancies have been empirically derived in previous researchfor use with MDS applications. Alcohol expectancy items werepreceded by the stem Drinking alcohol makes me . . . . Marijuanaexpectancy items were preceded by the stem Smoking marijuanamakes me . . . Instructions indicated that participants should rateeach item even if they had not used the drug before. Participantswho had not used the target drug were asked to rate the items basedupon how they thought it would affect them if they were to use it.

Alcohol and marijuana use in the past 6 months were assessedby 9-point scales (0 � no use, 8 � more than daily). Criterionvalidity of the scales is supported by previous research (Simons &Carey, 2006). Lifetime use of each was assessed by 7-point an-chored rating scales (0 � never used, 6 � more than 300 days).The rating scale was adapted from the National Household Surveyof Drug Abuse (SAMHSA, 2000).

Alcohol consumption in the past 6 months was assessed by theModified Daily Drinking Questionnaire (Dimeff, Baer, Kivlahan,& Marlatt, 1999), which consists of a grid representing the 7 daysof the week. The grid assessed participants’ typical daily alcoholconsumption for a typical week during the last 6 months. Weekly

consumption was calculated by summing the number of standarddrinks (one standard drink is equal to 12 oz. beer, 5 oz. wine, or 1.5oz. liquor) across the number of drinking days reported by theparticipant.

Procedure

Questionnaires on alcohol and marijuana use, expectancies, andsensitivity to punishment and reward were completed online. Allparticipants provided informed consent. Participants were re-cruited for a study entitled Substance Use Beliefs through an onlineuniversity research participation website that includes participantsfrom several departments (e.g., psychology, business, education,political science). Participants were told that all responses wouldbe anonymous and that participation would include answeringquestions regarding substance use and temperament. Participantsreceived course credit for their participation. Research supports thereliability and validity of online assessments of substance use andindividual difference characteristics (Christopher & Simons, 2002;Miller et al., 2002; Simons & Carey, 2006).

Results

Descriptive Statistics

Approximately 88% reported using alcohol at least once in theprevious 6 months. On average participants reported drinking 2 to3 times a month (rating scale M � 2.87, SD � 1.59) and consum-ing 12.01 (SD � 13.21, range � 0–79) drinks per week. Approx-imately 74% reported no marijuana use in the past 6 months, and57% reported never using marijuana in their lifetime. Among thosewho used marijuana in the past 6 months, average use was 2 to 3times a month (rating scale M � 2.56, SD � 1.70). Marijuana usefrequency in the past 6 months was positively correlated withalcohol use frequency (r � .30, p � .001) and drinks per week(r � .27, p � .001). Compared with nonusers of marijuana in thepast 6 months, marijuana users reported significantly more drinksper week, M � 19.67, SD � 15.01 vs. M � 9.41, SD � 11.40,t(878) � 10.72, p � .001, Cohen’s d � 0.77. Sensitivity to rewardwas positively correlated with drinks per week (r � .30, p � .001),alcohol use frequency (r � .22, p � .001), and marijuana usefrequency (r � .07, p � .03). Sensitivity to punishment was notsignificantly associated with drinks per week (r � �.05, p � .13),but exhibited modest correlations with marijuana (r � �.07, p �.05) and alcohol use frequency (r � �.09, p � .005).

MDS. MDS is a technique used to derive and map distancesbetween sets of stimuli or nodes. Expectancies can be thought ofas nodes of information stored in a semantic network of multidi-mensional space (Estes, 1991; Goldman, Rather, Dobson, & Ken-dall, 1993). MDS is well suited to represent knowledge structuressuch as nodes (i.e., expectancies) and map them into a hypotheticalnetwork of memory space (Aarons et al., 2003; Rather et al.,1992). The current study derived population-based Euclidean dis-tances of expectancies by using MDS with an Alternating LeastSquares Scaling (ALSCAL) algorithm (Borg & Groenen, 2005;Young & Harris, 1992). Euclidean distances are reliable measuresbetween discrete points, such that when mapped, each point has aspecific location relative to all other points on the map. To assessfit we considered the interpretability of the derived dimensions, the


variance accounted for (R2), the stress values, and replication ofprevious findings (Davison, 1992). The measure of stress is the“square root of a normalized residual sum of squares” (Kruskal &Wish, 1991). Stress, like R2, ranges in value from 0 to 1; as inOrdinary Least Squares (OLS) regression, R2 indicates explainedvariance, with higher levels of R2 equating to better model fit. Incontrast lower stress, being residually based, indicates bettermodel fit. All MDS ALSCAL configurations of expectancies werebased on the full sample (N � 897).

Preference Mapping

Preference mapping (PREFMAP; Meulman, Heiser, & Carroll,1986) is a regression technique that plots a vector through theexpectancies utilizing least squares procedures for each group.This vector represents a likely path of activation through thesemantic network. In order to examine whether individual differ-ences in SP and SR were associated with differing patterns ofexpectancy activation, we formed four groups based on the medi-ans of SP and SR. Sensitivity to punishment and reward arecontinuous variables and interactions between them should beconsidered to be continuous (cf. Simons & Arens, 2007; Simons etal., 2008). Although categorizing continuous variables to examineinteractions is often not optimal, preference mapping requires agrouping variable. We conducted analyses with groups based onthe upper and lower quartiles, as well as a median split to examinethe consistency of the effects across multiple splits of the data. Theresults were consistent across the different grouping methods, withgroups formed by the quartiles exhibiting greater differentiation inactivation patterns and substance use. Defining groups based uponthe upper and lower quartiles has the advantage of ensuring thatthe groups are defined by relatively narrow bands and are thusmore homogeneous in respect to relative levels of SP and SR.However, this is accompanied by the disadvantage of excludingmany cases from analysis and associated methodological costs inrespect to reliability and interpretability of the results (Preacher,Rucker, MacCallum, & Nicewander, 2005). Although the ob-served differences are less pronounced, we present results basedupon a median split of the data, which allows all cases to beincluded in the analyses.

Therefore, the high SP, high SR group (n � 188) was comprisedof individuals above the median on both SP and SR. The low SP,low SR group (n � 292) was comprised of individuals below themedian on both SP and SR. The low SP, high SR group (n � 212)was comprised of individuals below the median on SP and abovethe median on SR. Finally, the high SP, low SR group (n � 205)was comprised of individuals above the median on SP and belowthe median on SR. PREFMAP was then used to map paths of likelyactivation for the four groups through the hypothetical memorynetwork derived from the ALSCAL configurations of the expect-ancies. Adjusted means for the expectancies were calculated byregressing the expectancies on gender and SP/SR group and cal-culating predicted values for each group controlling for gender.Subsequent analyses are presented controlling for gender as wellas substance use. Mean scores on the expectancies for each of thegroups were used to estimate vectors of activation through thecommon stimulus configuration derived from the ALSCAL anal-ysis. The stimulus configurations and accompanying preferencevectors for groups show the activation pathways of expectancies

based on levels of SP and SR. Beginning at the arrowhead andworking back, the vector represents the expectancies most oftenthought to occur during substance use to the least often thought tooccur. As a vector moves closer to a given axis it begins toemphasize that axis, representing greater differentiation of theattributes. In contrast, as a vector moves away from an axis it isdeemphasized, and divergent attributes on the axis may be per-ceived as equally likely.

Our model posits that BIS/BAS levels are associated with sub-stance use in part through their influence on substance-relatedlearning experiences. BIS/BAS levels may promote or inhibitsubstance use exposure, which in turn is associated with individualdifferences in expectancies and expectancy activation patterns. Inaddition, BIS/BAS may influence substance-related subjective ex-perience affecting the organization and activation of expectanciesin memory. Three necessary conditions must hold to providesupport for the model. First, BIS/BAS levels should be associatedwith the organization and activation patterns of expectancies in thePREFMAP analyses. Second, BIS/BAS levels should be associ-ated with the expectancy activation patterns after controlling forsubstance use exposure in the PREFMAP analyses, although dif-ferences across groups should be attenuated. Third, BIS/BASlevels should be associated with substance use in the regressionanalyses.

Alcohol Expectancies

The MDS configuration of the 33 alcohol expectancies pro-duced a two-dimensional alcohol expectancy model consistentwith previous research (Aarons et al., 2003; Goldman & Darkes,2004; Rather et al., 1992). The two-dimensional model was bothinterpretable and fit the data well (stress � .146, R2 � .926). Athree-dimensional stimulus configuration model had slightly im-proved fit (stress � .091, R2 � .962). Although using moredimensions should always improve goodness-of-fit, it is importantto consider the contribution of the additional dimension in theinterpretation (Kruskal & Wish, 1991). The three-dimensionalsolution produced a very modest improvement in fit, did notproduce a clearly interpretable organization of expectancies, anddid not correspond to previously identified models using the 33expectancies (Goldman & Darkes, 2004; Rather et al., 1992).Thus, to maximize both interpretability and model fit, the two-dimensional model was selected. Consistent with previous re-search, the two dimensions appear to represent a positive-negativedimension and an arousal-sedation dimension of alcohol expect-ancies (Aarons et al., 2003; Goldman & Darkes, 2004; Rather etal., 1992).

Figure 1 shows the PREFMAP activation vectors through theMDS ALSCAL configuration for each of the four groups. Thefigure represents expectancy activation as beginning at the arrow-head and spreading along the vector. The order of expectancyactivation can be determined by points along the vector derivedfrom drawing perpendicular lines from each expectancy to thevector. The analysis used group means adjusted for gender. Therewere high correlations between the alcohol expectancy stimuluspoints and the plotted preference vectors for each group (rs �.988–.995). Correlations between preference vectors and stimulimust be high for appropriate interpretation of vectors (Borg &Groenen, 2005). SP/SR levels were hypothesized to be associated


with patterns of expectancy activation. Consistent with the hypoth-eses, activation of alcohol expectancies for individuals high in SRand low in SP begins in the positive-aroused quadrant and spreadstoward the negative-sedated quadrant. Thus, for individuals whoare high in SR and low in SP initial alcohol expectancies are:sociable, intoxicated, funny, energetic, jolly, horny, verbal, noisy,wobbly, courageous, woozy, and mellow. In contrast, for individ-uals who are low in SR and high in SP, activation begins in thepositive-sedated quadrant and spreads toward the negative-arousedquadrant. This group begins to activate more sedated expectanciesearlier; initial alcohol expectancies are: sociable, intoxicated,funny, jolly, energetic, horny, verbal, noisy, wobbly, woozy, cou-rageous, and sleepy. Activation patterns for individuals with bothhigh or both low levels of SP and SR were intermediary betweenthese two vectors. Overall, the low SP groups tended to emphasizethe positive-negative dimension such that negative expectanciesare less likely to be activated with more positive ones. In contrast,as SP increases positive and negative expectancies are more likelyto be activated together as the sedation-arousal dimension gainsprominence. For example, for individuals with high SP and lowSR, the expectancy “courageous” may be coactivated along withnegative inhibiting expectancies such as “foolish.”

These patterns of activation across groups defined by SP and SRare similar to differences seen across drinking groups. Previousresearch indicates that for heavier drinkers, expectancy activationinitiates in the positive-aroused quadrant, similar to what is ob-served for the high SR, low SP group (Rather et al., 1992).

Similarly, for lighter drinkers, expectancy activation initiates in thepositive-sedation quadrant, similar to what is observed for the lowSR, high SP group (Rather et al., 1992). Thus, activation pathsbased on levels of SP and SR are similar to those observed acrossdrinking groups. Indeed, as shown in Table 1, number of drinks perweek was lowest in the high SP, low SR group and highest in thelow SP, high SR group.

To test the hypothesis that SP/SR levels would be associatedwith alcohol use, a negative binomial regression analysis wasconducted to examine differences in drinks per week across thegroups. Groups were dummy coded and gender was included as a

Figure 1. Multidimensional scaling (MDS) ALSCAL configuration of alcohol expectancies with preferencemapping (PREFMAP) vectors for groups based on sensitivity to punishment (SP)/sensitivity to reward (SR)levels. Expectancy means for the groups are adjusted to control for alcohol consumption and/or gender. Solidarrowhead vectors control for gender. Open arrowhead vectors control for gender and alcohol consumption.

Table 1Substance Use by Highly Sensitive to Punishment (SP)/LessSensitive to Reward (SR) Groups

SP/SR groupDrinks perweek (SD)

% usedmarijuana in

past 6 months

High SP, Low SR (n � 198, 204; 83%) 7.51 (8.77) 17.16Low SP, Low SR (n � 287, 291; 77%) 9.30 (10.58) 22.34High SP, High SR (n � 187, 187; 71%) 15.09 (14.75) 26.74Low SP, High SR (n � 211, 212; 53%) 17.22 (15.84) 37.26

Note. Groups are formed based on a median split. Ns differ because ofmissing substance use data. N for the alcohol data is first, followed by themarijuana data and the percentage of women in the group.


covariate. The analysis indicated significant associations betweengroup status, gender, and number of drinks per week, �2(4, N �881) � 123.44, p � .0001, Cragg-Uhler R2 � .13. Relative to thehigh SP, low SR group the high SP, high SR (b � .58, p � .001),low SP, high SR (b � .63, p � .001), and the low SP, low SR (b �.22, p � .04) groups were each associated with more drinks perweek. Relative to the low SP, high SR group, the low SP, low SRgroup (b � �.42, p � .001) was associated with fewer drinks perweek, but the high SP, high SR group ( p � .657) did not differ.Finally, the low SP, low SR group (b � �.37, p � .001) wasassociated with significantly fewer drinks per week relative to thehigh SP, high SR group. The above analyses thus demonstrate thatindividual differences in SP and SR are associated with hypothe-sized differences in both alcohol expectancy activation patterns aswell as reported alcohol consumption.

The results are consistent with the hypothesis that individualdifferences in SP and SR may contribute to the development ofdiffering patterns of expectancy organization and activation. How-ever, this interpretation is tempered by the fact that the SP/SRgroups are confounded with differing rates of alcohol use in thiscross-sectional study. We thus conducted an additional PREFMAPanalysis to determine whether the observed differences in expect-ancy activation patterns across SP/SR groups were maintainedafter controlling for alcohol consumption. We hypothesized thatSP and SR should be associated with differences in expectancyactivation when controlling for alcohol consumption, althoughdifferences should be attenuated. Adjusted means for the expect-ancies were calculated by regressing the expectancies on gender,alcohol consumption, and SP/SR group and calculating predictedvalues for each group controlling for gender and alcohol consump-tion.1 There were high correlations between the alcohol expect-ancy stimulus points and the plotted preference vectors for eachgroup (rs � .988–.995). As seen in Figure 1, the differences inactivation patterns across groups are slightly attenuated but thefindings are consistent with results from the unadjusted means.Thus, after controlling for differences in alcohol consumption, thehigh SP, low SR group expectancy activation initiates in thepositive-sedated quadrant, while the low SP, high SR group ini-tiates activation in the positive-aroused quadrant, more closelyaligned with the positive-negative axis. Thus, for individuals withlow SP and high SR, positive expectancies are most likely to beactivated and negative expectancies are maximally distal. As SPincreases, coactivation of negative inhibiting expectancies alongwith the positive expectancies becomes increasingly likely. Al-though the results are correlational, this suggests that the observeddifferences in activation patterns as a function of SP/SR levels arenot because of the confounding effects of group differences inalcohol consumption.

Marijuana Expectancies

Following the above procedures, we conducted multidimen-sional scaling of the 54 marijuana expectancies followed by pref-erence mapping based on the same four SP/SR groups. The MDSALSCAL configuration of the 54 marijuana expectancies pro-duced a two-dimensional marijuana expectancy model consistentwith previous research (Linkovich-Kyle & Dunn, 2001). Similar tothe alcohol configuration, the two-dimensional marijuana modelwas both interpretable and fit the data well (stress � .166, R2 �

.902). As expected, a three-dimensional stimulus configurationmodel showed modestly improved fit (stress � .099, R2 � .950).However, inspection of the three-dimensional solution did notreveal a clearly interpretable organization of expectancies in thethree dimensions. Thus, a two-dimensional solution was chosen tomaximize model fit, interpretation, and comparability with previ-ously identified models of the 54 marijuana expectancies(Linkovich-Kyle & Dunn, 2001). Consistent with previous re-search, the dimensions may be described as detached-aware andrelaxed-agitated effects related to marijuana use (Linkovich-Kyle& Dunn, 2001).

Following the mapping of the 54 expectancies, PREFMAP wasused to plot paths of likely activation through the memory net-work. The analyses used group expectancy means adjusted forgender. There were high correlations between the marijuana ex-pectancy stimulus points and the plotted preference vectors foreach group (rs � .976–.983). In Figure 2 the marijuana expectancyconfiguration suggests that individuals high in SR and low in SPemphasize the relax-agitated axis. Activation of marijuana expect-ancies for individuals high in SR and low in SP begins in therelaxed side of the relaxed-agitated axis and spreads toward theagitated side. The first expectancies to be activated are stoned,laugh, high, mellow, hungry, relaxed, chill-out, thirsty, easygoing,sleepy, tired, and calm. Expectancies along the detached awareaxis are not differentiated. For example, for this group, “think,”“creative,” and “dopey” may be equally likely and coactivated. Incontrast, the activation vector for the high SP, low SR group isrotated toward the detached-aware axis. Activation begins in therelaxed-detached quadrant and spreads toward the agitated-awarequadrant. This group begins to activate detached expectanciesearlier. Their initial marijuana expectancies are: high, stoned,hungry, laugh, lazy, mellow, relaxed, thirsty, chill-out, tired,baked, and stupid. Thus, for this group, the expectancy of “stupid”may be activated early on with more positive relaxing expectan-cies, such as “easygoing” and “chill-out.” The groups with eitherboth high, or both low, levels of SP and SR had vectors runningbetween those of the other groups.

As with alcohol use, the patterns of activation across groups,defined by SP and SR, are similar to differences seen acrossmarijuana use groups. Previous research indicates that for heaviermarijuana users, expectancy activation initiates along the relaxed-agitated dimension, similar to what is observed for the low SP,high SR group (Linkovich-Kyle & Dunn, 2001). For experiment-ers and nonusers, expectancy activation initiates in the detached-relaxed quadrant with never-users placing more emphasis on the

1 To explore potential gender interactions, we conducted analyses sep-arately for men and women. This was done for the analyses of both alcoholand marijuana expectancies. Overall, the pattern of results for men andwomen indicated similar differences in activation patterns as a function ofBIS/BAS group. However, vectors were rotated in a manner consistentwith greater activating expectancies among men even though use level wascontrolled for. For example, in the analysis of alcohol expectancies, vectorsfor men were rotated further toward the positive-aroused quadrant. Theanalysis examining alcohol expectancies as a function of marijuana userstatus indicated that, overall, vectors initiated in the positive-sedated quad-rant for women and in the positive-aroused quadrant for men. For both menand women, marijuana use status was associated with a rotation towardgreater positive arousal.


detached-aware dimension, similar to what is observed for the highSP, low SR group (Linkovich-Kyle & Dunn, 2001). As shown inTable 1, the percent of participants reporting any use of marijuanain the past 6 months was lowest in the high SP, low SR group andhighest in the low SP, high SR group.

Differences across the groups in the likelihood of using mari-juana in the previous 6 months were examined by a logisticregression analysis with gender included as a covariate. Probabilityof any use was chosen as the criterion because of the low level ofuse in the sample. Group status and gender were significantlyassociated with the likelihood of using marijuana, �2(4, N �892) � 35.17, p � .001, Cragg-Uhler R2 � .06. Relative to the lowSP, high SR group the high SP, low SR (odds ratio [OR] � 0.41,p � .001) and low SP, low SR groups (OR � 0.55, p � .004) wereassociated with decreased likelihood of using marijuana while usein the high SP, high SR group (OR � 0.67, p � .072) wasmarginally less likely. Finally, the high SP, high SR group (OR �1.63, p � .051) was marginally more likely to use marijuanarelative to the high SP, low SR group. The other groups did notdiffer from each other ( ps � .21).

We conducted an additional PREFMAP analysis with expect-ancy means adjusted for gender and marijuana use to examinewhether differences in activation patterns across groups weremaintained after controlling for correlated differences in marijuanause frequency in the past 6 months. There were high correlationsbetween the marijuana expectancy stimulus points and the plottedpreference vectors for each group (rs � .981–.995). As shown in

Figure 2, differences in activation patterns across the SP/SRgroups are maintained, although attenuated, when controlling formarijuana use frequency.

Finally, 57% of the sample reported no lifetime use of mari-juana. This thus allowed a sufficient sample size to conduct thepreference mapping analysis only on the nonusers to determinewhether comparable differences in expectancy activation existacross the SP/SR groups who have not yet tried marijuana. Weagain formed four groups based on a median split of SP and SRamong never-users: high SP, high SR (n � 127), low SP, low SR(n � 145), low SP, high SR (n � 125), and high SP, low SR (n �110). Expectancy means adjusted for gender were used. The initialpopulation based MDS ALSCAL configuration of the 54 expect-ancies was again used. Figure 3 depicts the preference mappingresults to examine the activation of marijuana expectancies amongindividuals who report no history of marijuana use. Correlationsamong the vectors and the stimulus points were sufficiently highfor interpretation (rs � .968–.976). Figure 3 shows the pattern ofactivation paths for the four groups are similar to Figure 2. Asexpected, given the association between expectancy activation anduse, all the vectors in the nonuse sample are shifted toward thedetached pole of the detached-aware axis, consistent with previousresearch indicating this activation pattern for nonusers (Linkovich-Kyle & Dunn, 2001). However, of the groups, the low SP, high SRgroup places the greatest emphasis on the relaxed-agitated axis,similar to heavier users in previous research (Linkovich-Kyle &Dunn, 2001). In contrast the high SP, low SR group maintains

Figure 2. Multidimensional scaling (MDS) ALSCAL configuration of marijuana expectancies with preferen-tial mapping (PREFMAP) vectors for groups based on sensitivity to punishment (SP)/sensitivity to reward (SR)levels. Expectancy means for the groups are adjusted to control for marijuana use frequency and/or gender. Solidarrowhead vectors control for gender. Open arrowhead vectors control for gender and marijuana use.


emphasis on the detached-aware axis, again consistent with nonusegroups in previous research (Linkovich-Kyle & Dunn, 2001). Thissuggests that SP and SR are associated with differing patterns ofactivation in marijuana expectancies in individuals who have notyet tried the drug.

Alcohol Expectancies as a Function ofMarijuana Use Status

We conducted a PREFMAP analysis of the alcohol expectancymeans adjusted for gender to compare the activation patterns forparticipants who had versus had not used marijuana in the past 6months. There were high correlations between the alcohol expect-ancy stimulus points and the plotted preference vectors for both theuse group (r � .994) and the nonuse group (r � .989). For thenonusers, the activation began in the positive-sedated quadrant andspread to the negative-aroused quadrant. The vector angle was 16°from the positive-negative axis. Angles are such that 45° wouldbisect the positive-sedated quadrant and �45° would bisect thepositive-aroused quadrant. For users, the activation vector wasmore closely aligned with the positive-negative axis (�1°). Thus,expectancy activation patterns across the two groups are consistentwith the elevated alcohol use among marijuana users. We thenconducted the PREFMAP analysis adjusting for gender, SP, SR,and the SP/SR interaction. There were high correlations betweenthe alcohol expectancy stimulus points and the plotted preferencevectors for both the use group (r � .997) and the nonuse group

(r � .992). This resulted in a slight attenuation of the previouslyobserved differences in the vector angles. The nonuser vector wasnow 15° and the user group vector 2° off the positive-negativeaxis. Finally, we conducted a PREFMAP analysis adjusting forgender, SP, SR, their interaction, and alcohol consumption. Therewere high correlations between the alcohol expectancy stimuluspoints and the plotted preference vectors for both the use group(r � .998) and the nonuse group (r � .995). There were nowessentially no differences in the activation patterns. The nonuservector was now 11° and the user group vector 9.6° off the positive-negative axis. Thus, marijuana users and nonusers exhibited ex-pected differences in alcohol expectancy activation patterns, butthese differences were essentially eliminated once differences inBIS/BAS levels and alcohol consumption were controlled for.

Discussion

The current study used MDS to create a representation of asemantic network of alcohol and marijuana expectancies. Theresulting configurations were consistent with previous research onalcohol (Aarons et al., 2003; Del Boca et al., 2002; Rather et al.,1992) and marijuana (Linkovich-Kyle & Dunn, 2001) expectan-cies. The alcohol model consisted of two dimensions representingpositive-negative and arousal-sedation characteristics. The mari-juana model may be characterized by relaxed-agitated anddetached-aware dimensions. Preference mapping was used to plotvectors representing likely paths of activation through the stimulus

Figure 3. Multidimensional scaling (MDS) ALSCAL configuration of marijuana expectancies among nonuserswith preferential mapping (PREFMAP) vectors for groups based on sensitivity to punishment (SP)/sensitivity toreward (SR) levels. Expectancy means are adjusted to control for gender differences across the groups.


space as a function of levels of SP and SR, indicators of BIS andBAS activity, respectively. As hypothesized, there were markeddifferences in the paths of activation across the SP and SR groups.These activation patterns appear similar to patterns derived basedon substance use groups in previous research (Linkovich-Kyle &Dunn, 2001; Rather & Goldman, 1994; Rather et al., 1992). TheSP/SR groups exhibited expected differences in substance uselevel, with the lowest use being among individuals with high SPand low SR and the highest use among individuals with low SPand high SR. Differences in expectancy activation patterns acrossgroups were maintained, although attenuated, after adjusting fordifferences in gender and substance use level. Furthermore, ananalysis of the nonusers of marijuana demonstrated that compara-ble differences in activation patterns of marijuana expectancieswere observed in a subsample of participants who had not triedmarijuana. Finally, marijuana users and nonusers exhibited differ-ing patterns of activation of alcohol expectancies consistent withthe higher rates of alcohol use among marijuana users. However,this appears to be related to the confounded differences in BIS/BAS levels and drinking. Taken together, the findings are consis-tent with the hypothesis that individual differences in BIS andBAS functioning may foster the development of different organi-zation and activation of drug use expectancies, which contribute toobserved associations between BIS/BAS activity and drug use.

Alcohol Expectancies

The pattern of activation for individuals with high levels of SRand low levels of SP initiated in the positive-aroused quadrant. Incontrast, activation initiated in the positive-sedated quadrant forindividuals with low SR and high SP. These activation vectors aresimilar to the pattern of activation seen among heavier and lighterdrinkers, respectively, in several previous studies (Del Boca et al.,2002; Rather & Goldman, 1994; Rather et al., 1992). The activa-tion of positive and arousing expectancies is hypothesized toactivate associated affective and behavioral patterns that fostercontinued drinking, whereas sedating expectancies are hypothe-sized to be linked to behavior and affective patterns that inhibitcontinued drinking (Rather et al., 1992). Such “cross-linkages” aresupported by comparable organization of alcohol expectancies andcircumplex models of affect and personality (Goldman et al., 1999;Rather et al., 1992). The current findings add to this body ofresearch by providing data consistent with the reverse association.Cognitive network models characterized by spreading activation ofconceptual nodes provide a theoretical basis for this (Goldman etal., 1999).

Characteristic emotion and behavior patterns may influence theaccessibility and integration of expectancies that are more closelylinked to personal characteristics (cf. Goldman et al., 1999). Thatis, high BAS combined with low BIS activity is associated withhigher activity levels and positive affect (e.g., extraversion; Gray,1987). This pattern of activity may foster the development of asemantic network of alcohol expectancies that has similar charac-teristics (i.e., emphasizes positive arousal and behavioral ap-proach). In contrast, the BIS is sensitive to signals of punishmentand is instrumental in ceasing behavioral output (Gray, 1987). Thepattern observed in individuals with high BIS activity and lowBAS activity, activates sedating expectancies sooner, is more

likely to coactivate negative inhibiting expectancies along with thepositive ones, and is associated with lower levels of consumption.

Temperament may influence the development of expectancies(Anderson, Schweinsburg, et al., 2005a; McCarthy et al., 2001a,2001b) and expectancies may mediate and/or moderate associa-tions between temperament and substance use (McCarthy et al.,2001a, 2001b). Research on associative memory processes alsosuggests that differences in temperament may influence the devel-opment of drug use expectancies. For example, Ames and col-leagues (Ames, Sussman, Dent, & Stacy, 2005; Ames, Zogg, &Stacy, 2002) reported that substance related memory associationmediated the association between sensation seeking and alcoholand marijuana use. The current study is consistent with thesefindings and suggests that individual differences in BIS/BAS ac-tivity are also associated with different patterns of activation ofexpectancies in memory. We hypothesize that individual differ-ences in BIS and BAS activity, contribute to the development ofexpectancy organization through their influence on activity levels,behavioral experiences, and information processing of approachand inhibiting cues. Although the cross-sectional design precludessuch a conclusion, the finding that the observed differences acrossBIS/BAS groups were maintained after controlling for differentalcohol use levels across groups is consistent with such an inter-pretation.

Marijuana Expectancies

The stimulus configuration was consistent with the previousstudy that examined a memory network model of marijuana ex-pectancies among young adults (Linkovich-Kyle & Dunn, 2001),resulting in a two-dimensional configuration consisting of adetached-aware dimension and a relaxed-agitated dimension. Thelow SP, high SR group initiated activation closer to the relaxed-agitated axis, emphasizing positive relaxing characteristics such ashigh, mellow, and relaxed. This is the pattern of activation ob-served in heavier users in previous research (Linkovich-Kyle &Dunn, 2001), and members of this group were the most likely toreport using marijuana in the past 6 months. In contrast, theactivation pattern for the high SP, low SR group was shiftedtoward the detached pole of the detached-aware axis. This is apattern consistent with the abstainers in previous research(Linkovich-Kyle & Dunn, 2001). The likelihood of using mari-juana in the previous 6 months was the lowest in this group. Theseresults are generally consistent with previous research indicatingthat SP and SR interact in predicting marijuana use, and thatsensitivity to punishment may attenuate associations between pos-itive marijuana expectancies and use (Simons & Arens, 2007;Simons et al., 2008).

For both substances, analyses of expectancy means adjusted forgroup differences in substance use level indicated that the differ-ences in activation patterns were not simply a function of con-founded use level. The results are consistent with the premise thatBIS/BAS levels may influence drug–related learning experiences,both affecting exposure (e.g., drug experimentation) as well as thesubjective experience and learning from drug-related experiences(e.g., prevention messages, drug use). This interpretation is sup-ported by the analysis of marijuana expectancies of participantswho had never used marijuana. The pattern of activation vectorswas largely consistent with the analysis of the full sample, al-


though the vectors shift toward the detached pole of the detached-aware axis. Notably, the low SP, high SR group continues to bemore closely aligned with the relaxed-agitated axis than the othergroups. This indicates that although they have not tried marijuana,the pattern of activation is closest to that observed among theheavier marijuana users in previous research. Expectancies de-velop prior to drug experimentation and predict future use of thedrug (Aarons et al., 2001; Smith et al., 1995). Individuals in thelow SP, high SR group may thus have an expectancy network thatplaces them at increased risk for experimenting with marijuana. Incontrast, individuals with high SP and low SR may have anexpectancy network that promotes continued abstinence.

Dual User Comparisons

Marijuana users reported higher alcohol consumption than non-users. Previous research indicates that dual alcohol and marijuanausers start drinking at an earlier age, drink more, and exhibitgreater alcohol problems, even when controlling for differences inconsumption levels (Midanik et al., 2007; Shillington & Clapp,2006; Simons & Carey, 2006; Simons et al., 2005). We thusexamined whether dual users exhibited differing expectancy acti-vation patterns compared with users of alcohol only. The expect-ancy activation vector for nonusers was rotated further into thepositive-sedated quadrant relative to the marijuana users, consis-tent with the lower rates of alcohol use observed in this group.However, the difference in the vector angles was relatively mod-est. The observed difference was attenuated slightly after control-ling for differences in BIS/BAS levels, and essentially eliminatedafter controlling for differences in drinking levels. Thus, the resultsdo not suggest a marked difference in expectancy activation pat-terns across marijuana use groups that cannot be accounted for bythe correlated difference in alcohol use levels. Future research mayexplore differences as a function of simultaneous use. Simulta-neous use appears to exhibit stronger associations with problem-atic alcohol use and expectancies specific to the simultaneouseffects of the two drugs are associated with use levels (Barnwell &Earleywine, 2006; Midanik et al., 2007).

Implications for Intervention

Expectancy challenge based interventions have demonstratedsome promise in reducing alcohol use (Lau-Barraco & Dunn,2008). Research with expectancy memory models has indicatedthat expectancy challenge can affect expectancy activation patternsand that these changes are associated with resulting decrements indrinking (Dunn et al., 2000). The current results suggest thatBIS/BAS levels may influence the response to such expectancychallenges and affect the longevity of intervention effects. That is,to the extent that BIS/BAS may influence learning experiences bybiasing perception toward cues of reward or punishment, thesecharacteristics may affect the response to learning-based interven-tions such as expectancy challenge. Furthermore, subsequent to theintervention, BIS/BAS levels could affect whether treatment gainsare maintained or diminish as the individual encounters additionalsubstance-related experiences. Similarly, the results may be rele-vant to the construction of maximally effective prevention mes-sages. Previous research suggests that substance-related publicservice announcements may be tailored to enhance exposure and

recall for individuals high in sensation seeking (D’Silva &Palmgreen, 2007; Palmgreen, Lorch, Stephenson, Hoyle, &Donohew, 2007).

Limitations

Although alcohol use was prevalent, overall the sample hadcomparably little experience with marijuana. Women were overrepresented in the sample. While we were able to control for theeffects of gender in examining associations between BIS/BAS andexpectancy activation and use level, the stimulus configurationitself reflects the proportion of women in the sample. Givenassociations between gender and substance use expectancies(Read, Wood, Lejuez, Palfai, & Slack, 2004; Simons & Arens,2007), the proportion of women should be considered in interpret-ing the results. Drinking among college students is often charac-terized by “binge” drinking (Wechsler et al., 2002), enhancementrather than coping motives (Simons et al., 2005), and many of theparticipants are underage. The relative strength of BIS associationswith substance use may vary across developmental periods. De-velopmental research has indicated that expectancy memory orga-nization changes over the course of development and is associatedwith different alcohol use patterns (Dunn & Earleywine, 2001;Dunn & Goldman, 1998). The cross-sectional design precludesconclusions about the nature of the observed associations overtime. However, BIS/BAS was associated with differences in ex-pectancy activation patterns when controlling for level of sub-stance use and similar differences in activation patterns were alsoobserved among nonusers of marijuana.

The statistical techniques themselves are both a strength and alimitation. MDS and preference mapping provide a useful visualrepresentation of a hypothetical semantic network and activationpatterns within it. However, the statistical significance of differ-ences between vectors is not tested. Ultimately, the interpretationof differences in vectors across groups relies on replication, theo-retical consistency, observed differences across correlated behav-iors (e.g., substance use), and results of experimental studies. Incontrast to methods that rely on first associates, results may beinfluenced by the presentation of the stimuli themselves. However,research has supported the findings derived from MDS techniques.Differences in activation patterns are associated with substance use(Del Boca et al., 2002; Rather & Goldman, 1994; Rather et al.,1992), interventions affect the organization and activation patternsin expected ways (Dunn et al., 2000; Dunn & Yniguez, 1999), andresults are congruent with research using first associates tech-niques (Dunn & Goldman, 2000). Finally, recent conceptualiza-tions of the BIS have emphasized its role in approach-avoidanceconflict arising from activation of the BAS and fight-flight-freezing system (Gray & McNaughton, 2000). Further researchexamining the roles of these three systems is warranted.

In summary, the current study replicated and extended previousresearch on semantic network expectancy models. The stimulusconfigurations for both alcohol and marijuana were consistent withprevious research, supporting their reliability. Consistent with ourhypothesis, groups defined by differing levels of BIS/BAS activityexhibited different activation patterns. These activation patternsbore strong similarity to those observed across substance usegroups. Participants characterized by low levels of BIS activity andhigh levels of BAS activity exhibited activation patterns similar to


heavier substance users in previous research. In contrast, high BISactivity and low BAS activity was associated with expectancyactivation patterns similar to that observed in light or nonusers.High BAS activity was associated with activation patterns thatemphasized greater behavioral approach. High BIS activity in-creased the likelihood that negative inhibiting expectancies wouldbe co-activated with positive ones. Levels of alcohol and mari-juana use covaried with group status in expected ways. The ob-served differences in expectancy activation patterns remained aftercontrolling for group differences in substance use. Furthermore,analysis of participants who had never used marijuana indicatedthat BIS/BAS activity is associated with differing patterns ofexpectancy activation independent of differences in marijuana use.BIS and BAS activity may contribute to substance use, in part, byaffecting the development of the organization and activation pat-terns of substance use expectancies in memory.

References

Aarons, G. A., Brown, S. A., Stice, E., & Coe, M. T. (2001). Psychometricevaluation of the marijuana and stimulant effect expectancy question-naires for adolescents. Addictive Behaviors, 26, 219–236.

Aarons, G. A., Goldman, M. S., Greenbaum, P. E., & Coovert, M. D.(2003). Alcohol expectancies: Integrating cognitive science and psycho-metric approaches. Addictive Behaviors, 28, 947–961.

Alfonso, J., & Dunn, M. E. (2007). Differences in the marijuana expect-ancies of adolescents in relation to marijuana use. Substance Use &Misuse, 42, 1009–1025.

Ames, S. L., Sussman, S., Dent, C. W., & Stacy, A. W. (2005). Implicitcognition and dissociative experiences as predictors of adolescent sub-stance use. American Journal of Drug and Alcohol Abuse, 31, 129–162.

Ames, S. L., Zogg, J. B., & Stacy, A. W. (2002). Implicit cognition,sensation seeking, marijuana use and driving behavior among drugoffenders. Personality and Individual Differences, 33, 1055–1072.

Anderson, K. G., Schweinsburg, A., Paulus, M. P., Brown, S. A., & Tapert,S. (2005a). Examining personality and alcohol expectancies using func-tional magnetic resonance imaging (fMRI) with adolescents. Journal ofStudies on Alcohol, 66, 323–331.

Anderson, K. G., Smith, G. T., & Fischer, S. F. (2003). Women andacquired preparedness: Personality and learning implications for alcoholuse. Journal of Studies on Alcohol, 64, 384–392.

Anderson, K. G., Smith, G. T., McCarthy, D. M., Fischer, S. F., Fister, S.,Grodin, D., et al. (2005b). Elementary school drinking: The role oftemperament and learning. Psychology of Addictive Behaviors, 19, 21–27.

Barnwell, S. S., & Earleywine, M. (2006). Simultaneous alcohol andcannabis expectancies predict simultaneous use. Substance Abuse Treat-ment and Prevention Policy, 1, 29.

Borg, I., & Groenen, P. J. F. (2005). Modern multidimensional scaling:Theory and applications (2nd ed.). New York: Springer Science andBusiness Media.

Brown, S. A., Goldman, M. S., Inn, A., & Anderson, L. R. (1980).Expectations of reinforcement from alcohol: Their domain and relationto drinking patterns. Journal of Consulting and Clinical Psychology, 48,419–426.

Brunelle, C., Barrett, S. P., & Pihl, R. O. (2007). Relationship between thecardiac response to acute intoxication and alcohol-induced subjectiveeffects throughout the blood alcohol concentration curve. Human Psy-chopharmacology: Clinical and Experimental, 22, 437–443.

Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioralactivation, and affective responses to impending reward and punishment:The BIS/BAS Scales. Journal of Personality and Social Psychology, 67,319–333.

Caseras, X., Åvila, C., & Torrubia, R. (2003). The measurement of indi-vidual differences in Behavioural Inhibition and Behavioural ActivationSystems: A comparison of personality scales. Personality and IndividualDifferences, 34, 999–1013.

Christiansen, B. A., Smith, G. T., Roehling, P. V., & Goldman, M. S.(1989). Using alcohol expectancies to predict adolescent drinking be-havior after one year. Journal of Consulting and Clinical Psychology,57, 93–99.

Christopher, M. S., & Simons, J. S. (2002, April). Psychometric propertiesof five traditional measures of affective functioning completed on theInternet. Paper presented at the convention of the Rocky MountainPsychological Association, Park City, UT.

Cloninger, C. R. (1987). Neurogenetic adaptive mechanisms in alcoholism.Science, 236, 410–416.

Collins, R. L., Bradizza, C. M., & Vincent, P. C. (2007). Young-adult maltliquor drinkers: Prediction of alcohol problems and marijuana use.Psychology of Addictive Behaviors, 21, 138–146.

D’Silva, M. U., & Palmgreen, P. (2007). Individual differences and con-text: Factors mediating recall of anti-drug public service announcements.Health Communication, 21, 65–71.

Davison, M. L. (1992). Multidimensional scaling. New York: Wiley.Del Boca, F. K., Darkes, J., Goldman, M. S., & Smith, G. T. (2002).

Advancing the expectancy concept via the interplay between theory andresearch. Alcoholism: Clinical & Experimental Research, 26, 926–935.

Dimeff, L. A., Baer, J. S., Kivlahan, D. R., & Marlatt, G. A. (1999). Briefalcohol screening and intervention for college students (BASICS): Aharm reduction approach. New York: Guilford Press.

Dunn, M. E., & Earleywine, M. (2001). Activation of alcohol expectanciesin memory in relation to limb of the blood alcohol curve. Psychology ofAddictive Behaviors, 15, 18–24.

Dunn, M. E., & Goldman, M. S. (1996). Empirical modeling of an alcoholexpectancy memory network in elementary school children as a functionof grade. Experimental and Clinical Psychopharmacology, 4, 209–217.

Dunn, M. E., & Goldman, M. S. (1998). Age and drinking-related differ-ences in the memory organization of alcohol expectances in 3rd-, 6th-,9th-, and 12th-grade children. Journal of Consulting and Clinical Psy-chology, 66, 579–585.

Dunn, M. E., & Goldman, M. S. (2000). Validation of multidimensionalscaling-based modeling of alcohol expectancies in memory: Age anddrinking-related differences in expectancies of children assessed as firstassociates. Alcoholism: Clinical & Experimental Research, 24, 1639–1646.

Dunn, M. E., Lau, H. C., & Cruz, I. Y. (2000). Changes in activation ofalcohol expectancies in memory in relation to changes in alcohol useafter participation in an expectancy challenge program. Experimentaland Clinical Psychopharmacology, 8, 566–575.

Dunn, M. E., & Yniguez, R. M. (1999). Experimental demonstration of theinfluence of alcohol advertising on the activation of alcohol expectanciesin memory among fourth- and fifth-grade children. Experimental andClinical Psychopharmacology, 7, 473–483.

Erblich, J., & Earleywine, M. (2003). Behavioral undercontrol and subjec-tive stimulant and sedative effects of alcohol intoxication: Independentpredictors of drinking habits? Alcoholism: Clinical & ExperimentalResearch, 27, 44–50.

Estes, W. K. (1991). Cognitive architectures from the standpoint of anexperimental psychologist. Annual Review of Psychology, 42, 1–28.

Fillmore, M. T., Carscadden, J. L., & Vogel-Sprott, M. (1998). Alcohol,cognitive impairment and expectancies. Journal of Studies on Alcohol,59, 174.

Fischer, S., Smith, G. T., Anderson, K. G., & Flory, K. (2003). Expectancyinfluences the operation of personality on behavior. Psychology ofAddictive Behaviors, 17, 108–114.

Goldman, M. S., & Darkes, J. (2004). Alcohol Expectancy Multiaxial


Assessment: A memory network-based approach. Psychological Assess-ment, 16, 4–15.

Goldman, M. S., Del Boca, F. K., & Darkes, J. (1999). Alcohol expectancytheory: The application of cognitive neuroscience. In E. Leonard & H. T.Blane (Eds.), Psychological theories of drinking and alcoholism (2nded., pp. 203–246). New York: Guilford Press.

Goldman, M. S., Rather, B. C., Dobson, K. S., & Kendall, P. C. (1993).Substance use disorders: Cognitive models and architecture. In Psycho-pathology and cognition (pp. 245–292). San Diego, CA: AcademicPress.

Gray, J. A., & McNaughton, N. (2000). A theory of the behaviouralinhibition system. In The neuropsychology of anxiety: An enquiry intothe functions of the septo-hippocampal system (2nd ed., pp. 83–93). NewYork: Oxford University Press.

Gray, J. A. (1987). The neuropsychology of emotion and personality. InS. M. Stahl, S. D. Iversen, & E. C. Goodman (Ed.), Cognitive neuro-chemistry (pp. 171–190). New York: Oxford University Press.

Hartup, W. W., & van Lieshout, C. F. M. (1995). Personality developmentin social context. Annual Review of Psychology, 46, 655–687.

Hendershot, C. S., Stoner, S. A., George, W. H., & Norris, J. (2007).Alcohol use, expectancies, and sexual sensation seeking as correlates ofHIV risk behavior in heterosexual young adults. Psychology of AddictiveBehaviors, 21, 365–372.

Johnson, S. L., Turner, R. J., & Iwata, N. (2003). BIS/BAS levels andpsychiatric disorder: An epidemiological study. Journal of Psychopa-thology and Behavioral Assessment, 25, 25–36.

Kirk, J. M., Doty, P., & de Wit, H. (1998). Effects of expectancies onsubjective responses to oral d-sup-9-tetrahydrocannabinol. Pharmacol-ogy, Biochemistry and Behavior, 59, 287–293.

Knyazev, G. G. (2004). Behavioural activation as predictor of substanceuse: Mediating and moderating role of attitudes and social relationships.Drug and Alcohol Dependence, 75, 309–321.

Kruskal, J. B., & Wish, M. (1991). Multidimensional scaling (Sage Uni-versity Paper Series on Quantitative Applications in the Social Sciences,No. 07–001). Beverly Hills, CA: Sage.

Lau-Barraco, C., & Dunn, M. E. (2008). Evaluation of a single-sessionexpectancy challenge intervention to reduce alcohol use among collegestudents. Psychology of Addictive Behaviors, 22, 168–175.

Levenson, R. W., Oyama, O. N., & Meek, P. S. (1987). Greater reinforce-ment from alcohol for those at risk: Parental risk, personality risk, andsex. Journal of Abnormal Psychology, 96, 242–253.

Linkovich-Kyle, T. L., & Dunn, M. E. (2001). Consumption-related dif-ferences in the organization and activation of marijuana expectancies inmemory. Experimental and Clinical Psychopharmacology, 9, 334–342.

Martino, S. C., Collins, R. L., Ellickson, P. L., Schell, T. L., & McCaffrey,D. (2006). Socio-environmental influences on adolescents’ alcohol out-come expectancies: A prospective analysis. Addiction, 101, 971–983.

McCarthy, D. M., Kroll, L. S., & Smith, G. T. (2001a). Integratingdisinhibition and learning risk for alcohol use. Experimental and Clin-ical Psychopharmacology, 9, 389–398.

McCarthy, D. M., Miller, T. L., Smith, G. T., & Smith, J. A. (2001b).Disinhibition and expectancy in risk for alcohol use: Comparing blackand white college samples. Journal of Studies on Alcohol, 62, 313–321.

Meulman, J., Heiser, W. J., & Carroll, J. D. (1986). PREFMAP-3 usersguide. Unpublished manuscript. Murray Hill, NJ: Bell Telephone Labs.

Midanik, L. T., Tam, T. W., & Weisner, C. (2007). Concurrent andsimultaneous drug and alcohol use: Results of the 2000 National AlcoholSurvey. Drug and Alcohol Dependence, 90, 72–80.

Miller, E. T., Neal, D. J., Roberts, L. J., Baer, J. S., Cressler, S. O., Metrik,J., et al. (2002). Test-Retest reliability of alcohol measures: Is there adifference between Internet-based assessment and traditional methods?Psychology of Addictive Behaviors, 16, 56–63.

Nagoshi, C. T., Noll, R. T., & Wood, M. D. (1992). Alcohol expectancies

and behavioral and emotional responses to placebo versus alcohol ad-ministration. Alcoholism: Clinical & Experimental Research, 16, 255.

O’Connor, R. M., Colder, C. R., & Hawk, L. W., Jr. (2004). Confirmatoryfactor analysis of the Sensitivity to Punishment and Sensitivity to Re-ward Questionnaire. Personality and Individual Differences, 37, 985–1002.

O’Connor, R. M., & Colder, C. R. (2005). Predicting alcohol patterns infirst-year college students through motivational systems and reasons fordrinking. Psychology of Addictive Behaviors, 19, 10–20.

Palmgreen, P., Lorch, E. P., Stephenson, M. T., Hoyle, R. H., & Donohew,L. (2007). Effects of the Office of National Drug Control Policy’sMarijuana Initiative Campaign on high-sensation-seeking adolescents.American Journal of Public Health, 97, 1644–1649.

Pardo, Y., Aguilar, R. l., Molinuevo, B., & Torrubia, R. (2007). Alcoholuse as a behavioural sign of disinhibition: Evidence from J. A. Gray’smodel of personality. Addictive Behaviors, 32, 2398–2403.

Preacher, K. J., Rucker, D. D., MacCallum, R. C., & Nicewander, W. A.(2005). Use of the Extreme Groups Approach: A critical reexaminationand new recommendations. Psychological Methods, 10, 178–192.

Rather, B. C., & Goldman, M. S. (1994). Drinking-related differences inthe memory organization of alcohol expectancies. Experimental andClinical Psychopharmacology, 2, 167–183.

Rather, B. C., Goldman, M. S., Roehrich, L., & Brannick, M. (1992).Empirical modeling of an alcohol expectancy memory network usingmultidimensional scaling. Journal of Abnormal Psychology, 101, 174–183.

Ray, L. A., McGeary, J., Marshall, E., & Hutchison, K. E. (2006). Riskfactors for alcohol misuse: Examining heart rate reactivity to alcohol,alcohol sensitivity, and personality constructs. Addictive Behaviors, 31,1959–1973.

Read, J. P., Wood, M. D., Lejuez, C. W., Palfai, T. P., & Slack, M. (2004).Gender, alcohol consumption, and differing alcohol expectancy dimen-sions in college drinkers. Experimental and Clinical Psychopharmacol-ogy, 12, 298–308.

SAMHSA. (2000). National household survey on drug abuse, 1998 [Com-puter file]. 2nd ICPSR version. Research Triangle Institute. RetrievedJanuary 2, 2008, from http://www.icpsr.umich.edu/cgi-bin/bob/file?comp�none&study�2934&ds�1&file_id�656068

Scarr, S., & McCartney, K. (1983). How people make their own environ-ments: A theory of genotype–environment effects. Child Development,54, 424–435.

Shillington, A. M., & Clapp, J. D. (2002). Beer and bongs: Differentialproblems experienced by older adolescents using alcohol only comparedto combined alcohol and marijuana use. American Journal of Drug &Alcohol Abuse, 28, 379–397.

Shillington, A. M., & Clapp, J. D. (2006). Heavy alcohol use compared toalcohol and marijuana use: Do college students experience a differencein substance use problems? Journal of Drug Education, 36, 91–103.

Simons, J. S., & Arens, A. M. (2007). Moderating effects of sensitivity topunishment and sensitivity to reward on associations between marijuanaeffect expectancies and use. Psychology of Addictive Behaviors, 21,409–414.

Simons, J. S., & Carey, K. B. (2006). An affective and cognitive model ofmarijuana and alcohol problems. Addictive Behaviors, 31, 1578–1592.

Simons, J. S., Dvorak, R. D., & Batien, B. D. (2008). Methamphetamineuse in a rural college population: Associations with marijuana use,sensitivity to punishment, and sensitivity to reward. Psychology ofAddictive Behaviors, 22, 444–449.

Simons, J. S., Gaher, R. M., Correia, C. J., Hansen, C. L., & Christopher,M. S. (2005). An affective-motivational model of marijuana and alcoholproblems among college students. Psychology of Addictive Behaviors,19, 326–334.

Smith, G. T., Goldman, M. S., Greenbaum, P. E., & Christiansen, B. A.(1995). Expectancy for social facilitation from drinking: The divergent


paths of high-expectancy and low-expectancy adolescents. Journal ofAbnormal Psychology, 104, 32–40.

Smith, G. T., Williams, S. F., Cyders, M. A., & Kelley, S. (2006). Reactivepersonality-environment transactions and adult developmental trajecto-ries. Developmental Psychology, 42, 877–887.

Stacy, A. W. (1995). Memory association and ambiguous cues in modelsof alcohol and marijuana use. Experimental and Clinical Psychophar-macology, 3, 183–194.

Stacy, A. W., Newcomb, M. D., & Bentler, P. M. (1991). Cognitivemotivation and drug use: A 9-year longitudinal study. Journal of Ab-normal Psychology, 100, 502–515.

Tarter, R. E. (2002). Etiology of adolescent substance abuse: A develop-mental perspective. The American Journal on Addictions, 11, 171–191.

Torrubia, R., Åvila, C., Molto, J., & Caseras, X. (2001). The Sensitivity toPunishment and Sensitivity to Reward Questionnaire (SPSRQ) as a

measure of Gray’s anxiety and impulsivity dimensions. Personality andIndividual Differences, 31, 837–862.

Wechsler, H., Lee, J. E., Kuo, M., Seibring, M., Nelson, T. F., & Lee, H.(2002). Trends in college binge drinking during a period of increasedprevention efforts. Journal of American College Health, 50, 203–217.

Wills, T. A., & Dishion, T. J. (2004). Temperament and adolescent sub-stance use: A transactional analysis of emerging self-control. Journal ofClinical Child and Adolescent Psychology, 33, 69–81.

Young, F. W., & Harris, D. F. (1992). Multidimensional Scaling. In M. J.Norvsis (Ed.), SPSS for Windows professional statistics release 6 (pp.397–461). Chicago: SPSS.

Received June 26, 2008Revision received February 22, 2009

Accepted March 12, 2009 �


behavioral inhibition and activation systems: differences ... dvorak lau-barraco 2009.pdf ·...

Documents