Experimental and Clinical Psychopharmacology1996, Vol. 4, No. 1,29-36

Copyright 1996 by the American Psychological Association, Inc.1064-1297/96/$3.00

Affect, Expectancies, Urges, and Smoking:Do They Conform to Models of Drug Motivation and Relapse?

Thomas H. BrandonState University of New York at Binghamton

David W. Wetter and Timothy B. BakerUniversity of Wisconsin—Madison

Social-learning models of drug motivation and relapse often include the constructs of affect anddrug expectancies. Most research has taken a molar approach to examining relations between theseconstructs and level of drug use. An experiment examined the roles of affect and expectancies inmultiple measures of situation-specific motivation to smoke tobacco. Undergraduate smokers(n = 101) received either a positive or negative mood manipulation (false feedback on anintelligence test). Self-reported urge was influenced by both negative affect and expectancies forpositive reinforcement from smoking. Actual consumption was related only to smoking expectan-cies and only among abstaining smokers. Affect by expectancy interactions were also found.Findings support a limited role of affect and expectancies in smoking motivation. Poor coherenceamong the motivational indexes challenges the assumptions of existing models of drug motivation.

Several recent social-learning theories of drug motivationand drug relapse share key constructs. Two constructs inparticular appear repeatedly as theoretical predictors of drugmotivation or self-administration: affective state (negativeaffect in particular) and outcome expectancies. In general, themodels posit that motivation to use drug (measured either byself-reported urge or actual drug use) increases as a function of(a) a negative affective state, and (b) positive expectanciesabout the consequences of drug use, including the ameliora-tion of negative affect.

One such model (Cox & Klinger, 1988) posits that analcoholic individual makes a decision to drink when positiveaffect expectancies of drinking outweigh expectancies for notdrinking. Another motivational model of relapse to drug use(Niaura et al., 1988) views both urges and positive drugexpectancies as resulting from an affective imbalance orcontextual cues for drug use, or both. Marlatt (1985) describeda relapse model that included affect (as a potential high-risksituation) and expectancies as important elements. That con-structs such as affect and expectancies appear in models ofboth ongoing drug use and drug relapse reflects the assump-tion that motivational influences are likely to remain potenteven after drug use has initially ended.

Affect and Drug Motivation

Evidence for affect as a causal agent in the motivation tosmoke derives from self-reports collected with smoking motiva-

Thomas H. Brandon, Department of Psychology, State University ofNew York at Binghamton; David W. Wetter and Timothy B. Baker,Department of Psychology and Center for Tobacco Research andIntervention, University of Wisconsin—Madison. David W. Wetter isnow at the M. O. Anderson Cancer Center, University of Texas at Houston.

Preparation of this article was supported by American CancerSociety Grant JFRA389.

Correspondence concerning this article should be addressed toThomas H. Brandon, Department of Psychology, Box 6000, State Uni-versity of New York at Binghamton, Binghamton, New York 13902-6000. Electronic mail may be sent via Internet to brandon@bingvmb.cc.binghamton.edu.

tion questionnaires, epidemiological studies showing covari-ance between affective disorders and smoking, and findingsthat the majority of relapse episodes occur during negativeaffect states (for reviews, see Brandon, 1994; Carmody, 1989;Hall, Munoz, Reus, & Sees, 1993). Although such correla-tional studies suggest a relation between affect and drugmotivation, experimental studies are less clear. For instance,whereas experimental stressors generally increase smokingrates (Dobbs, Strickler, Maxwell, 1981; Pomerleau & Pomer-leau, 1987; Rose, Ananda, & Jarvik, 1983; Schachter, Silver-stein, Kozlowski, Herman, & Liebling, 1977), it is unclear thatthis stress effect is mediated by affective change. Affect isconsistently related to self-reported urges' across differentaddict groups in experimental settings (Childress, McLellan,Natale, & O'Brien, 1987; Litt, Cooney, Kadden, & Gaupp,1990; Payne, Schare, Levis, & Colletti, 1991; Sherman, Morse,& Baker, 1986; Sherman, Zinser, Sideroff, & Baker, 1989).However, urges tend to be unrelated to other measures of drugmotivation (Tiffany, 1990), so the significance of this relation isunclear.

Expectancies and Drug Motivation

Research has shown that positive expectancies regardingdrug effects are related to global measures of drug consump-tion (Brandon & Baker, 1991; Brown, 1993; Copeland, Bran-don, & Quinn, 1995; Wetter et al., 1994). There has been littleresearch, though, that examines the situation-specific impactof expectancies on drug motivation (see Leigh, 1989), and theresearch that exists is contradictory (e.g., Cooney, Gillespie,Baker, & Kaplan, 1987; Corcoran & Parker, 1991; Fromme &Dunn, 1992). That is, there is little evidence linking particularexpectancies of drug effects with motivation to use drug withina specific, relevant situation. Such evidence is needed to

1 Although there has been controversy over the terms craving andurge (Kozlowski & Wilkinson, 1987), in this article we use the wordsinterchangeably to refer to the self-reported subjective desire for adrug.

29

30 BRANDON, WETTER, AND BAKER

delineate, for example, the process by which a stressor leads todrug relapse.

The principal goal of the present study was to investigate, ina controlled laboratory setting, the relations among affectivestate, smoking expectancies, and motivation to smoke within aparticular motivational context, namely provision of positiveversus negative (distressing) information. Both experimentaland correlational approaches were used to examine theserelationships. The specific questions we addressed were asfollows:

1. Do diverse measures of smoking motivation cohere?2. Do expectancies predict urge and self-administration?3. Does affective state predict urge and self-administration?4. Do affect and affect-related expectancies interact to

predict urge and self-administration?The first question addresses the common assumption that

the motivation to use drug is unitary, and that one should seeagreement among the construct indicators (cf. Tiffany, 1990).In the absence of agreement, smoking motivation must beviewed as multicomponential, with no adequate, single indica-tor. In this study we used the following indicators to indexmotivation to smoke: self-reported urge to smoke, completionof a task that made smoking available, number of puffs taken,and amount of smoke administered as assessed via breathcarbon monoxide level.

The next two questions are straightforward examinations ofthe relation between the constructs of interest and smokingmotivation. The fourth question arises from the hypothesisthat expectancies may moderate the relationship betweenaffect and drug motivation (Baron & Kenny, 1986). Specifi-cally, affect should motivate smoking only if participantsbelieve that smoking can be used to regulate affect or copewith affective states. In this study, we manipulated smokingstatus (withdraw! vs. continued smoking) and performancefeedback (success vs. failure information) in part to produceincreased variance on the affective measures. Both smokingwithdrawal and stressful information increase negative affect(Zinser, Baker, Sherman, & Cannon, 1992), and increasedvariance in negative affect should increase the likelihood ofdetecting meaningful covariation in relevant measures.

Method

Experimental Design and Overview

The experiment used a 2 x 2 factorial design. The two factors wereSmoking Status (withdrawing vs. continuing smokers) and Perfor-mance Feedback (success vs. failure) on the Block Design subscale ofthe Wechsler Adult Intelligence Scale (WAIS; Wechsler, 1955). Wemanipulated smoking status because we believed that withdrawalwould increase smokers' affective reactions to the performance feed-back and would model the relapse process. The withdrawal conditionalso offers a closer approximation to natural smoking relapse episodes,which usually begin very early in the quit attempt while withdrawalsymptoms are present (Kenford et al., 1994). Nevertheless, at least onemodel (i.e., Baker, Morse, & Sherman, 1987) posits different relation-ships between affect and drug motivation in continuing versus withdraw-ing drug users. The smoking status manipulation allowed us to test thistheory. The second factor was intended as a positive versus negativeaffect manipulation. This manipulation allowed us to examine experi-

mentally the causal relationship between affect and measures ofsmoking motivation.

Participants attended two assessment sessions. They first met insmall groups on a weeknight (group session). During this meetingparticipants were informed about the experiment, administered pre-liminary assessments, and assigned to experimental conditions. Thefollowing week they each participated in an individual assessment thatincluded the mood manipulation and assessment of expectancies,smoking urges, and nicotine self-administration (individual session).

Participants

Participants were 122 smokers who were students from introductorypsychology classes earning extra credit points for research participa-tion. Inclusion criteria were a smoking rate of at least 10 cigarettes perday (one-half pack) and an initial alveolar carbon monoxide (COa)level at the evening session of at least 10 ppm. Of the 122 participantswho attended the evening session and met these criteria, 4 participantsfailed to attend the individual session the following week. Two eachhad been assigned to the continuing and withdrawing conditions.Withdrawing smokers were required to report abstinence and have aCOa level at the individual session that was lower than their initial CO.,level. Five participants failed to meet this criterion. Continuingsmokers were required to maintain COa levels above 10 ppm. Twelveparticipants failed to do so. (Several participants said that the smokingstudy motivated them to attempt to quit.) In all, 7 withdrawingsmokers and 14 continuing smokers did not complete the experiment,leaving 101 participants (24-26 participants per condition).2

Participants' mean age was 20.1 years. They smoked a mean of 16.8cigarettes per day for 3.7-years. Mean score on the modified Fager-strom Tolerance Questionnaire (see below; Fagerstrom, 1978) was 3.9.Mean initial COa level was 20.0 ppm. Of the 101 participants, 51 weremen. Two-way analyses of variance (ANOVAs) were calculated onthese descriptive variables to verify equivalence across conditions. Theonly difference was that participants assigned to the continuingcondition had a higher initial COa level than those assigned to thewithdrawing condition (21.84 ppm vs. 18.16 ppm), F(\, 97) = 4.81,p < .05.

Instruments

Smoking Consequences Questionnaire (SCQ). The SCQ (Brandon& Baker, 1991) requires participants to rate the probability anddesirability of 50 possible consequences of smoking. The cross prod-ucts of these ratings yields the subjective expected utility (SEU) of theconsequences (Brandon & Baker, 1991). We used two versions of thequestionnaire that differed only in the instructions. The distal version(SCQ-D) instructed participants to respond in the manner that theythink about cigarettes in general, whereas the proximal version(SCQ-P) instructed participants to respond to how they were thinkingat the moment. In this study, we examined only the two scales that weexpected to be related to smoking motivation: Positive Reinforcement-Sensory Satisfaction and Negative Reinforcement-Negative AffectReduction.

Fagerstrom Tolerance Questionnaire. The standard measure ofnicotine dependence (Fagerstrom Tolerance Questionnaire; Fager-strom, 1978) was modified so that all questions were multiple choice.and a question concerning brand of cigarette was deleted. Possiblescores ranged from 0 to 9.

2 Data from participants in the failure feedback conditions were alsoincluded in the studies reported in Wetter, Brandon, and Baker(1992).

SPECIAL SECTION: AFFECT, EXPECTANCIES, URGES, AND SMOKING 31

Positive and Negative Affect Schedule (PANAS). The PANAS is a20-item Likert-type measure that yields two factor-derived orthogonalscales: positive affect and negative affect (Watson, Clark, & Tellegen,1988). Participants were instructed to describe their mood over thepast week as a measure of general affective level.

Withdrawal Rating Form (WRF). The WRF is a 25-item 7-pointLikert-type measure of common nicotine withdrawal symptoms (Shiff-man & Jarvik, 1979). It yields a total score, as well as scores on craving,psychological discomfort, sedation-stimulation, physical symptoms,and appetite scales.

Mood Adjective Check List (MACL). The MACL (Nowlis, 1965) isa self-report affect measure. Participants rated their current affect byendorsing 36 adjectives on a 7-point Likert scale. Subscales includeAggression, Anxiety, Fatigue, Sadness, Surgency, Elation, Sociability,Vigor, and Skepticism. The first four scales also made up a generalnegative affect scale, and the next four a general positive affect scale.The MACL was used as a check of the mood manipulation.

Attribution questionnaire. The attribution questionnaire was admin-istered after participants completed each experimental task (a wordassociation test and the block design task). Only the latter administra-tion was scored as a check on the feedback manipulation. Participantsfirst rated their performance on the task using a 10-point scale rangingfrom 1 (totalfailure) to 10 (total success). Additional questions assessedparticipants' attributions for their emotional response; the results havealso been reported elsewhere (Wetter, Brandon, & Baker, 1992).

Urge scale. A three-item self-report measure of urge was con-structed from items imbedded in the SCQ-P, the MACL, and theattribution questionnaire. Urge was assessed in this subtle manner soas to keep participants unaware that the goal of the experiment was tomanipulate urge. The questions asked participants to rate their desirefor a cigarette, their craving for a cigarette, and the probability thatthey would smoke if they could. Urge scores were derived by averagingthe z-scores of the three items. The coefficient alpha reliability of thisscale was .90.

Procedure

Group session. Participants were told that they were participatingin a study of "how smokers think." They completed a smoking statusform and provided COa samples. Participants who met the inclusioncriteria then completed the SCQ-D and the PANAS. Last, theycompleted two other questionnaires included to investigate hypoth-eses unrelated to the present report (see Wetter et al., 1992).Participants were then scheduled for individual sessions the followingweek and were randomly assigned to the continuing or withdrawingconditions. Those in the withdrawing condition were instructed toabstain from smoking for 24 hr before their individual session.Continuing participants were instructed to smoke at their normal rate.Finally, one cigarette was collected from each participant.

Individual session. Before this session, participants were randomlyassigned to the positive affect or negative affect condition (success vs.failure performance feedback). Figure 1 summarizes the procedures atthe individual session. On arrival at the session, participants wereadministered a COa test and the WRF. They then were administeredan oral word association test that included smoking-related wordsfollowed by the attribution questionnaire assessing their perceivedperformance on the word association test. These two instruments wereincluded to divert suspicion from the mood manipulation. The moodmanipulation (described below) followed. Participants then completedthe MACL as a check of their affective response to the false feedbackfollowed by the SCQ-P and the attribution questionnaire.

Nicotine self-administration was assessed by allowing the partici-pant to smoke a cigarette that had been collected at the group session.Participants were told that they could smoke once they completed atask that required the participant to trace with a pen along the line of

IWithdrawal Rating Form

IWord AssociationDistraction Task

IMood Manipulation

(Pos. or Neg. Feedbackon "Intelligence Test")

Attribution Questionnaire

ICircle-Tracing Task

ISmoking Trial

Figure 1. Flow diagram showing assessments used during the indi-vidual session. COa = alveolar carbon monoxide; Pos. = positive;Neg. = negative.

several circles and other shapes. Completion time of a similar task hasbeen used as an index of behavioral activation or "go" motivation(Bachorowski & Newman, 1990). The assumption was that partici-pants who were highly motivated to smoke their cigarette wouldcomplete the task quicker and commit more errors than less motivatedparticipants. After completing the tracing task, participants smokedtheir cigarette through a flow transducer that simultaneously recordedsmoking topography on a Grass Model 7A polygraph and a personalcomputer. This apparatus was used so that we could collect smokingtopography measures such as latency to first puff, number of puffs, andpuff volume that might reflect smoking motivation (e.g., Griffiths,Henningfield, & Bigelow, 1982; Payne et al., 1991). However, we foundthat the flow transducer measured puff volume unreliably. Therefore,we limited the smoking topography measures to latency to first puffand total number of puffs. Participants were told that they couldsmoke as much of their cigarette as they liked. When participantsfinished smoking, the experimenter administered a cigarette rating

32 BRANDON, WETTER, AND BAKER

form and the debriefing questionnaire. Then another COa sample wastaken to estimate the amount of smoke ingested. Finally, the experi-menter debriefed participants and provided information about quit-ting smoking.

Mood manipulation: Failure feedback. After participants com-pleted the WRF, they were told that they would next receive a test ofspatial problem-solving ability from a "standard IQ test." Participantswere given the standard instructions for the Block Design subscale ofthe WAIS. They were also told that to motivate them to work as fast asthey could, they would earn extra money based on their performance.Participants were given six quarters and told that they would gain orlose quarters based on their performance.

All participants gained quarters on WAIS Block Designs 2 and 3,but then lost quarters on the six most difficult Block Designs. Theexperimenter also made scripted condescending comments after eachtrial, such as "Are you sure you're working as fast as you can?" Afterthe final trial, participants were told to count up their earnings (50cents) and sign a receipt form. This form was in fact a counterfeit thatwas intended to induce negative social comparison. Above the partici-pant's name were 10 names of other ostensive participants and themoney that each had apparently earned, which averaged $2. Thus,participants were led to believe that they had performed more poorlythan had any previous participant.

Mood manipulation: Success feedback. The success manipulationfollowed the same general procedure as the failure manipulation.However, participants were initially given four quarters before beingadministered the six easiest Block Design trials, for which they earnedadditional quarters. When they signed the receipt form for their $2.50earnings, they saw that they had earned more money than the previousparticipants, who appeared to average only about 75 cents.

Results

Analytical Plan

After verifying the effectiveness of the experimental manipu-lations, each of the questions listed in the introduction wereexamined in turn. All factorial analyses used a three-waydesign, with Smoking Status, WAIS Feedback, and Gender asthe factors. A priori ANOVAs were conducted using thetraditional alpha level of .05. A more conservative alpha of .01was used for zero-order correlations to control for chanceassociations.

Manipulation Checks

Smoking status. The first experimental manipulation wasassignment to either the withdrawing or continuing smokingconditions. As expected, continuing smokers had higher COa

levels than withdrawing smokers on arrival at the individualsession (19.51 ppm vs. 6.76 ppm), F(l, 93) = 131.15,p < .001.Withdrawing smokers also scored higher on the total WRF(4.26 vs. 3.81 on the 7-point scale), F(l, 93) = 24.35,p < .001,and on the craving, psychological discomfort, and appetitesubscales of the WRF.

Feedback-affect manipulation. The second experimentalmanipulation was the success versus failure feedback on theWAIS Block Design task. For this manipulation to be success-ful, participants had to believe the feedback, and the feedbackmust have altered their affective state. As expected, on the10-point performance rating scale of the attribution question-naire, failure participants tended to rate their performance as

failing (M = 3.20), and success participants rated their perfor-mance as successful (M = 8.54), F(l, 93) = 268.30, p < .001.No difference was found between the smoking status condi-tions. No participant guessed that the WAIS feedback wasfalse, either on a debriefing questionnaire or during oraldebriefing. Finally, on the 0-6 MACL skepticism scale, partici-pants averaged 1.99, with no significant difference betweenconditions. In summary participants appeared to believe theWAIS feedback.

Checks of group equivalency revealed that withdrawingparticipants reported slightly greater trait negative affect,assessed with the PAN AS at the baseline group session (2.18vs. 1.93), F(l, 97) = 4.81, p < .05. Because this differencecould have arisen only by chance and was not related fundamen-tally to the group manipulation, we used analysis of covariance(ANCOVA) to partial the influence of baseline affect from thepostmanipulation affect scores. The positive and negativeaffect scales of the MACL were used to assess postmanipula-tion affective state. Three-way ANCOVAs (SmokingStatus x Feedback Manipulation x Gender) were calculatedwith the positive and negative affect MACL scales as depen-dent variables and the respective baseline PANAS scales ascovariates. Success participants scored higher than failureparticipants on the positive affect MACL scale when thepositive affect PANAS scale was used as the covariate, F(l,92) = 13.73, p < .001. In addition, failure participants scoredhigher than success participants on the negative affect MACLscale when the baseline negative affect PANAS scale wascovaried out, F(l, 92) = 4.54, p < .05. Correlations betweenthe MACL scales and participants' ratings of their perfor-mance on the block design also suggest that the manipulationwas effective. Performance ratings were correlated with boththe positive affect scale, r(100) = .393, p < .001, and thenegative affect scale, r(100) = -.229,p < .05. In summary, itappears that the mood manipulation had a modest, butreliable, effect on participants' affective state. No significantdifference in affect was found for smoking status or gender, norwas any interaction found.

Do Diverse Measures of Smoking Motivation Cohere?

We examined five measures of smoking motivation acrossthree domains: one measure of self-reported urge (the urgescale), two measures of "eagerness" to smoke (number oferrors made on the circle-tracing task before smoking1 andlatency to first puff), and two measures of actual consumption(number of puffs taken and change in COa due to smoking).Table 1 shows the intercorrelations among these measures.The two measures of eagerness were significantly correlated,as were the two measures of consumption. However, measuresfrom the different domains were uncorrelated with one an-other, with the single exception of urge with tracing errors.This indicates an overall lack of coherance across smokingmotivation indexes, yet significant coherence within subdo-mains.

3 Errors were counted independently by two laboratory assistants.Their tallies correlated .96, so the mean of the two counts was used.

SPECIAL SECTION: AFFECT, EXPECTANCIES, URGES, AND SMOKING 33

Table 1Intercorrelations Between Measures of Smoking Motivation

Tracing Latency NumberMeasure Urge errors to smoke of puffs

EagernessTracing errorsLatency to smoke

ConsumptionNumber of puffsCOa change

.274*-.124

.212

.039

—-.446**

.030

.040

—

-.075 —-.027 .448**

Note. COa = alveolar carbon monoxide.*p < .01. *> < .001.

To simplify later analyses, given the correlations within thepairs of eagerness measures and consumption measures, themeasures in each pair were standardized and averaged toproduce single eagerness and consumption scores. The coeffi-cient alpha reliabilities of these two indexes were both .62.Later analyses of smoking motivation were performed usingonly the urge, eagerness, and consumption indexes.

Do Expectancies Predict Urge and Self-Administration?

Because expectancies were not directly manipulated in thisexperiment, this question can be addressed only throughcorrelational analyses. Correlations were calculated betweenthe SCQ (both distal, SCQ-D, and proximal, SCQ-P, versions)and the three motivational indexes. When all participants wereused in the analysis, the only significant correlation wasbetween the positive reinforcement scale of the SCQ-P andurge,r(99) = .284, p < .01.

Next, continuing and withdrawing smokers were examinedseparately. The magnitude of the correlations between positivereinforcement expectancies and urge remained the same forboth groups of participants (.28 and .31, respectively), but didnot reach significance because of the reduced sample size.Expectancies did not predict either of the other motivationalindexes among continuing smokers. However, among withdraw-ing participants significant relations were found between thepositive and negative reinforcement scales and the consump-tion index. Table 2 displays these correlations using both thedistal and proximal expectancy questionnaires.

Table 2Correlations Between Smoking Consequences Questionnaire(SCQ) Scales and Smoking Motivation Indexes(Withdrawing Participants Only)

Index

SCQ scale Urge Eagerness Consumption

DistalPositive reinforcementNegative reinforcement

Proximal »Positive reinforcementNegative reinforcement

.261

.175

.306

.107

-.013-.095

.125-.108

.362*t

.420*tt

.123

.384*t

*p < .01. tp < .05 when controlling for urge, ftp < .01 whencontrolling for urge.

To examine whether smoking expectancies predicted con-sumption among withdrawing smokers directly, independentof urge, we calculated partial correlations between the threepredictive SCQ scales and the consumption index, with urgescores partialled out. As indicated on Table 2, all three scalesremained predictive (all/vs > .34, allps < .05). Thus, expect-ancies were related to self-administration directly, withoutmediation by urge.

Does Affect Predict Urge and Self-Administration?

To examine the causal relation between affect and urge, wecompared the positive and negative feedback conditions onurge ratings. To control for urge levels before the feedbackmanipulation, we performed ANCOVAs with the urge scale ofthe WRF as the covariate, thereby assessing the effect of themanipulation on urge. This required separate analyses ofcontinuing and withdrawing smokers. The model of Baker etal. (1987) predicts that the failure feedback should producegreater urge among withdrawing smokers and the successfeedback should produce greater urge among continuingsmokers. However, among continuing participants, failurefeedback produced greater urge than success feedback, F(l,46) = 5.00, p < .05, and no difference was found amongwithdrawing participants.

ANOVAs were calculated to assess the effect of the experi-mental manipulations on self-administration measures. Nomain effects or interactions were found on the eagerness orconsumption measures.

To examine further the relation between affect and smokingmotivation, we performed correlational analyses between thepositive and negative affect scales of the MACL and theeagerness, consumption, and urge scales. No significant corre-lations were found between affect and the two self-administra-tion scales (i.e., the eagerness and consumption indexes).Multiple regression revealed a significant relation betweennegative affect and urge, even when the urge score from theWRF had been partialled out to reflect change in urge due tothe affect manipulation,/v = .31, F(l, 98) = 10.23, ;> < .01.This finding was maintained when baseline negative affectfrom the PANAS was also partialled oul,pr = .29, F(l, 97) =8.80, p < .01. No interaction was found between negativeaffect and smoking status condition. These results indicate thatchange in negative affect was related to change in self-reportedurge regardless of withdrawal state.

Do Affect and Affect-Related Expectancies Interactin the Prediction of Smoking Motivation?

Two types of interactions with theoretical significance wereexamined: Interactions between negative affect (MACL) andnegative reinforcement expectancies and between positiveaffect and positive reinforcement expectancies. Interactionswere tested using hierarchical regression, in which the productof the affect and proximal expectancy variables was enteredinto the regression equation after the variables themselves hadbeen entered (Cohen & Cohen, 1983).

Urge. To examine interaction effects in predicting changein urge score, we used postmanipulation urge as the dependent

34 BRANDON, WETTER, AND BAKER

variable and first entered the premanipulation urge score fromthe WRF. When the entire participant sample was examined,no interactions were revealed. Multiple regression did reveal,however, that negative affect and positive reinforcement expect-ancies contributed unique variance to urge ((is = .24 and .28,ps < .05 and .01, respectively), together explaining 14% of thevariance (p < .001). When withdrawing participants wereanalyzed separately, we found a significant interaction be-tween the MACL positive affect scale and the positive reinforce-ment scale of the SCQ-P(pr = .32,/? < .05). The greatest urgewas associated with high scores on positive affect and highexpectations for positive reinforcement. The interaction be-tween negative affect and the negative reinforcement scale ofthe SCQ-P approached significance (pr = .26, p < .10). Highnegative affect and high negative reinforcement expectancieswere associated with the greatest urge. No significant interac-tions were found among continuing smokers.

Self-administration. We performed similar analyses to exam-ine interaction effects with the two self-administration indexesas dependent variables. Only one significant interaction wasfound. Among continuing smokers, consumption was pre-dicted by interactions between negative affect and the negativereinforcement scale of the SCQ-P (pr = -.37, p < .01). Thenature of this interaction differed from predictions in thatnegative affect was positively associated with consumption onlyif participants held low expectancies for negative reinforce-ment from smoking. When participants held high expectan-cies, there was no relationship between negative affect andconsumption.

We also examined whether affect and expectancy measurescovaried. This would be consistent with some of the motiva-tional models (Cox & Klinger, 1988; Marlatt, 1985; Niaura etal., 1988). No significant correlations were found.

Discussion

This study examined the relation between affect and out-come expectancies and the motivation to smoke. In addition,we addressed whether smoking motivation measures coheredwith one another, suggesting the presence of a unitary con-struct.

Manipulation checks indicated that the two experimentalmanipulations (smoking status and affective state) were sound.We now consider each of the guiding questions in turn.

Do Diverse Measures of Smoking Motivation Cohere?

We found almost no covariance between measures ofself-reported urge, eagerness to smoke, and smoke consump-tion. Moreover, these hypothetical indexes of smoking motiva-tion tended to be associated with different predictor variables.For example, urge was best predicted by affect, whereasconsumption was best predicted by expectancies. The relation-ship between expectancies and self-administration was notmediated by urge, as would have been predicted by Marlatt's(1985) model. A similar dysynchrony across different responsemodes thought to reflect smoking motivation (i.e., self-report,physiological, and behavioral) has been reported by others(Niaura, Abrams, Pedraza, Monti, & Rohsenow, 1992; Tiffany

& Hakenewerth, 1991). The lack of relationship between urgeand self-administration appears consistent with models of drugmotivation that view the two variables as somewhat function-ally independent (Tiffany, 1990) and inconsistent with modelsthat view self-reported urge as functionally integrated withself-administration behaviors (e.g., Baker et al., 1987; Heather& Stallard, 1989; Marlatt, 1985; Niaura et al., 1988). It is alsopossible that other factors are causing a response dysynchrony,such as different response thresholds, response reactivity, orother measurement artifact (Baker & Brandon, 1990). Ananalogy may be made to the low correlations between verbal,behavioral, and somatic measures of fear (Lang, Levin, Miller,& Kozak, 1983). It is also possible that our sample was toohomogeneous with regard to smoking motivation. A samplecomprising both novice and heavily dependent smokers mightyield less dysynchrony among measures.

Do Expectancies Predict Urge and Self-Administration?

Both positive and negative reinforcement expectancies pre-dicted consumption among withdrawing smokers. This wasfound with both proximal expectancies collected at the time ofthe experiment and distal expectancies collected a weekearlier. Although several studies have found that expectancieswere related to molar and global levels of alcohol or cigaretteuse (e.g., Brandon & Baker, 1991; Brown, 1993), this study isthe first to find that affect-related expectancies predicted drugconsumption in a given situation. This finding is consistent withmodels that assign expectancies a proximal causal role in druguse or relapse (Cox & Klinger, 1988; Niaura et al., 1988). Moreresearch is needed to determine if expectancies are consis-tently more predictive among withdrawn versus continuingsmokers.

Whereas both positive and negative reinforcement expectan-cies predicted consumption, only the positive reinforcementscale completed just before the smoking trial (the "proximal"form) predicted urge self-report. Moreover, there was noevidence that the relation between expectancies and consump-tion was mediated by urge.

Does Affective State Predict Urgeand Self-Administration?

Both experimental and correlational results revealed arelationship between affective state and cigarette urge, addingto findings by Payne et al. (1991), Sherman et al. (1989), andothers (e.g., Zinser et al., 1992). Evidence for a causal role ofnegative affect on urge—based on the experimental manipula-tion—was found only among continuing smokers. This mayreflect a ceiling effect among withdrawing smokers, who hadhigher urge scores before the manipulation.

In contrast to the experimental finding, correlational analy-ses revealed a relation between negative affect and urge acrossall participants. No relationship was found between affect stateand eagerness or consumption indexes, which is inconsistentwith several earlier studies that found increased smoking inresponse to stress or negative affect (Dobbs et al., 1981; Payneet al., 1991; Pomerleau & Pomerleau, 1987; Rose et al., 1983;Schacter et al., 1977). We designed our affect manipulation to

SPECIAL SECTION: AFFECT, EXPECTANCIES, URGES, AND SMOKING 35

be less obvious to participants than is usual in such studies.Our more modest findings might reflect less experimenterdemand or simply a weaker manipulation.

Do Affect and Expectancies Interact to Predict Urgeand Self-Administration?

When urge self-report was the dependent variable, therewas some evidence that both positive and negative reinforce-ment expectancies moderated the influence of affect. Thisrelation was found only among withdrawn smokers, however.When self-administration was the dependent variable, wefound no theoretically significant interaction between affectand expectancies. Therefore, there was modest support for thehypothesis that expectancies moderate the relation betweenaffect and urge, at least among withdrawn smokers.

General Conclusions

The main findings of this research are as follows:1. Putative measures of drug motivation were not highly

related to one another. However, there were meaningfulsubcategories of measures: urge, eagerness to smoke, andsmoke intake (consumption).

2. Expectations of smoking reinforcement predicted smokeconsumption in withdrawn smokers. However, measures ofaffect and urge were largely unrelated to smoke intake.

3. Affect and self-reported urge were significantly associ-ated with one another across all participants.

4. There was modest support, among withdrawn smokers,that expectancies of reinforcement from smoking moderatedthe relation between affect and urge. There was no evidencethat affect was related to self-administration measures evenwhen using reinforcement expectancies as a moderator.

There are potential limits on the generalizability of thisstudy. Although participants were dependent smokers (i.e.,they experienced nicotine withdrawal symptoms), they smokedless than older, more experienced smokers. Older smokersmay also hold different, more specific expectancies aboutsmoking (Copeland et al., 1995). Also, participants in thewithdrawing condition were not actually attempting to quit,nor did most express even a desire to quit. It is also possiblethat the consumption measures used were not the best analogof natural relapse. A dichotomous measure of whether or not aparticipant decides to smoke when given the option may haveyielded different results (see Cox & Klinger, 1988). Finally,these participants were tested while they were experiencingthe first 24 hr of nicotine withdrawal. The influence of affectand expectancies on motivation to smoke may change asex-smokers progress to later stages of abstinence.

The above findings provide some support for models of drugmotivation that include roles for affect and drug expectancies.However, the data suggest that these variables do not influencedrug motivation in a straightforward manner. First, interrela-tions among variables may be present, or at their greatest, onlyamong highly motivated participants (i.e., those in with-drawal). The tendency for measures to cohere more strongly inhighly motivated participants has been found in both addictand nonaddict populations (e.g., Baker et al., 1987). Neverthe-

less most current models, including Baker et al. (1987), assumegreater coherence among these variables. The general lack ofcoherence challenges the idea that these diverse measuresreflect the same underlying construct. This is a more fundamen-tal problem for the theory than the mixed findings regardingthe specific directional predictions. Second, urge and stateaffect self-reports may tend to covary, and expectancy mea-sures and drug consumption-intake measures may tend tocovary, but there may be little relation between the two pairs ofmeasures. This may be because affect measures (especiallystate affect) and urge measures are, by nature, highly situation-ally specific. Expectancies and self-administration assessmentsmay necessarily involve stable beliefs and attitudes and automa-tized self-administration behaviors acquired through countlessprior exposures to drug and drug information (Tiffany, 1990).Third, even though significant relations among variables werefound, relations were generally weak. For instance, the twobest preditors across all participants—negative affect andexpectancies of positive reinforcement—together explainedonly 15% of the variance in urge and failed to explainsignificant variance in consumption. Clearly the models areunderidentified.

This study provides support for the constructs of negativeaffect and outcome expectancies having roles in the motivationto smoke in a given situation. In particular, the molecularanalysis of the role of expectancies in consumption augmentsexisting evidence of a more molar relationship between expect-ancies and general level of substance use. Conversely, thesituation-specific contributions of affect and expectancies ap-pear to be quite limited. The challenge remains to find a modelwith constructs that together can explain greater variance insituational drug use and drug relapse.

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Received March 8, 1995Revision received June 30, 1995

Accepted July 12, 1995 •