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Exp Clin Psychopharmacol. Author manuscript; available in PMC 2016 April 01. Published in final edited form as: Exp Clin Psychopharmacol. 2016 April ; 24(2): 90–99. doi:10.1037/pha0000065.
Individual Differences in Subjective Alcohol Responses and Alcohol-Related Disinhibition Patrick D. Quinn, PhD and Kim Fromme, PhD The University of Texas at Austin
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There are important individual differences in acute subjective responses to alcohol, which have often been assessed using self-report measures. There is also evidence of meaningful betweenpersons variation in alcohol’s disinhibiting effects on behavior, such that some individuals become more impaired on tasks of inhibition than do others after an intoxicating dose. The degree to which subjective alcohol responses correspond with these disinhibition effects is not yet clear. In this study, we tested associations among indices of subjective alcohol responses and their correspondence with sensitivity to alcohol-related disinhibition. We recruited recent-bingedrinking emerging adults (N = 82) for a group-administered, placebo-controlled, within-subject, counterbalanced alcohol challenge in a simulated bar laboratory. Confirmatory factor analyses revealed that a two factor model with several cross-loadings explained associations among the subjective measures well, replicating a differentiation between stimulant-like and sedative-like subjective responses. Controlling sex and placebo performance, participants who reported greater subjective stimulant-like effects—but not sedative-like effects—experienced more alcohol-related disinhibition, as measured by Cued Go/No-Go Task inhibitory failures. This association was small-to-moderate in magnitude. The results of this study highlight the distinction between stimulant-like and sedative-like subjective alcohol effects. They suggest, additionally, that there may be modest commonalities between alcohol’s acute impacts on subjective stimulation and objective disinhibition.
Keywords Alcohol sensitivity; stimulation; sedation; inhibition; Cued Go/No-Go Task
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There are important individual differences in responses to the acute effects of alcohol (for reviews, see Morean & Corbin, 2010; Quinn & Fromme, 2011; Ray, MacKillop, & Monti, 2010). Although these differences are often assessed using self-reported, subjective measures, differential sensitivity has also been found on physiological indices (Brunelle, Barrett, & Pihl, 2007; King, de Wit, McNamara, & Cao, 2011; Schuckit, 1985). That is, some individuals experience alcohol’s pharmacological effects to a greater extent than do others, even when doses are identical.
Correspondence concerning this article should be addressed to: Patrick D. Quinn, Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E. 10th St., Bloomington, IN 47405. Phone: (812) 856-2588. [email protected]. Patrick D. Quinn is now at Indiana University Bloomington.
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An essential question has been whether to conceptualize subjective sensitivity to alcohol as a unidimensional construct varying only in magnitude or as a set of multiple constructs varying in both magnitude and quality (Ray, MacKillop, Leventhal, & Hutchison, 2009). Importantly, research driven by an understanding of alcohol’s acute biphasic nature has demonstrated that alcohol produces both aversive, sedative-like effects (characterizing the descending blood alcohol concentration [BAC] limb) and euphoric, stimulant-like effects (characterizing the ascending limb; Newlin & Thomson, 1990). Both of these sets of effects appear relevant to risk for alcohol use disorder (AUD), likely because of their distinct roles in motivating alcohol use (King, McNamara, Hasin, & Cao, 2014). There continue, however, to be advances in research using a unidimensional alcohol response conceptualization that emphasizes magnitude but not quality (e.g., Paulus et al., 2012; Trim, Schuckit, & Smith, 2013). This inconsistency underscores the need for clarity regarding the factor structure of subjective alcohol effects.
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Two recent factor analyses of responses to oral (Ray et al., 2009) and intravenous (IV; Bujarski, Hutchison, Roche, & Ray, 2015) alcohol have found that existing subjective measures reflect multiple factors, including positively valenced, stimulant-like effects and negatively valenced, sedative-like effects, in addition to craving and negative affect or its alleviation. Moreover, the Subjective High Assessment Scale, a measure of response magnitude used in much early research, loaded onto the sedation factor in both studies. These factor analyses support the perspective that alcohol responses can and should be distinguished into subjective stimulation and sedation—likely among other qualities as well (Morean, Corbin, & Treat, 2013). At the same time, however, both were conducted in settings that likely differed from typical consumption (e.g., individually, using IV administration) and without placebo controls, and the authors noted that further examination with greater ecological validity is warranted (Bujarski et al., 2015). Largely separate from this literature, a body of research has developed implicating acute alcohol intoxication as a causal factor in risk-taking or disinhibited behaviors. Theoretical accounts posit that alcohol’s effects on controlled processing, and response inhibition in particular, may play a role in these outcomes, at least under certain conditions (Fillmore, 2003; Giancola, Josephs, Parrott, & Duke, 2010; Moss & Albery, 2009). Of note, emerging evidence suggests that there may be meaningful individual differences in alcohol’s effects on response inhibition. Specifically, two studies have found that differential alcohol-related response disinhibition predicts ad libitum drinking (Gan et al., 2014; Weafer & Fillmore, 2008).
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Given the extensive literature on alcohol responses and the emerging evidence of individual variation in alcohol-related disinhibition, it will be important to determine the extent to which subjective responses correspond with differences in alcohol-related disinhibition. One study found that IV-alcohol-induced increases in risky decision-making were greater among individuals who reported stronger subjective stimulation or weaker subjective sedation, raising the possibility that stimulation “may be a marker for increased likelihood of risk taking following alcohol consumption” (Gilman, Smith, Ramchandani, Momenan, & Hommer, 2012, p. 474). Further examination would help clarify the relation between subjective alcohol responses and alcohol-related disinhibition.
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The Present Study We examined relations among subjective alcohol responses and alcohol-related disinhibition, with an emphasis on ecological validity. Young people are at increased risk for problematic alcohol involvement (Grant et al., 2015; Hingson, Zha, & Weitzman, 2009), and their alcohol consumption is often socially based and socially rewarding (Sayette et al., 2012). Given this context, we recruited emerging-adult drinkers for a placebo-controlled, group-administered, oral alcohol challenge in a simulated bar to test two questions. First, to what extent are subjective stimulant-like and sedative-like effects distinct? Second, to what extent are these responses associated with individual differences in alcohol-related disinhibition?
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Participants
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Participants (N = 82) were emerging adults aged 21 – 25 who reported two or more typicalweek drinking occasions, in addition to one or more binge drinking episodes (four or more standard drinks for women and five or more standard drinks for men, both in a two-hour period) during the two weeks prior to screening. We targeted an equal number of male and female participants from the surrounding community and introductory psychology subject pool of a large, public university. Exclusion criteria were as follows: possible alcohol dependence, defined as Alcohol Use Disorders Identification Test score greater than 15 (AUDIT; Babor, Higgins-Biddle, Saunders, & Monteiro, 2001); self-reported pregnancy, possible pregnancy, or positive pregnancy test (for women);1 and other self-reported medical, personal, or ethical contraindications to drinking alcohol, including a “flushing” response. Of the 97 eligible participants who completed one laboratory session, 84 returned for a second session. Of these participants, 2 did not reach a threshold breath alcohol concentration (BrAC; defined as .05 g%), resulting in the final sample of 40 female (45% White, 28% Asian or Asian-American, 8% Hispanic or Latina, 5% African-American, and 15% multiethnic) and 42 male participants (48% White, 31% Asian or Asian-American, 14% Hispanic or Latino, 2% African-American, and 5% multiethnic or other ethnicities). The mean age among women was 22.04 years (SD = 0.85, range = 21.05 – 24.70) and among men was 22.30 years (SD = 1.13, range = 21.08 – 25.40), and 33% of women and 38% of men reported a positive family history of alcohol problems (of n = 81 who provided complete family history data).
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Participants who completed only the first session were modestly but non-significantly (ps > . 05) more likely than those who completed both sessions to be older (Cohen’s d = .12), male (odds ratio [OR] = 2.15), White (versus non-White; OR = 1.94), or randomized to receive placebo first, OR = 1.76. They were also more likely to report a positive family history (OR = 2.17) and more total drinks (d = .23) and times drunk, d = .19. However, they were also
1A subset of participants (n = 8 included women) were additionally screened for ineligibility on the basis of self-reported nursing in accordance with a change in procedures requested by the Institutional Review Board. Exp Clin Psychopharmacol. Author manuscript; available in PMC 2016 April 01.
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less likely to score higher on the AUDIT (d = −.03), maximum drinks (d = −.05), and binge drinking, d = −.24. Procedures Participants completed a group-administered, within-subject, counter-balanced, placebocontrolled alcohol challenge study across two laboratory sessions separated by an average of 9.23 days (median = 7, SD = 9.06, range = 6 – 70). Participants were instructed to eat a full meal at least four hours before the start of the session and to abstain from alcohol for 48 hours and from caffeine and tobacco for 3 hours prior to the session. At baseline, female participants screened for pregnancy with urine hCG tests, and all participants provided informed consent (first session), screened for a .000 g% BrAC, and completed a baseline assessment, which included measures not reported here (for further details, see Quinn, 2014).
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Alcohol challenge—Sessions were conducted in a simulated bar in variable-sexcomposition groups of three or four participants. All participants in a given session cohort were randomly assigned to the same condition (alcohol or placebo), although some cohorts included four individuals (participants and confederates) in one session and three in the other. Four participants returned on different occasions from the remainder of their groups. In cases when participants were unable to attend their scheduled sessions, undergraduate research assistants served as confederates in order to maintain a minimum of three individuals per laboratory session. Confederates were trained to engage neutrally with participants to help maintain a similar social milieu to that of the other sessions. Confederates always consumed placebo beverages but followed all other procedures during the dose-administration portion of the protocol.
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Participants had 10 minutes to consume each of 3 drinks containing a 1:3 ratio of 40%alcohol-by-volume vodka or a decarbonated tonic water placebo and mixer (cranberry juice, diet cherry soda, and sweetened lime juice). Alcohol doses were calculated using sex and weight to target a peak BrAC of .08 g%. First-session dosing was double-blinded through the alcohol-administration procedures. We followed standard procedures to ensure an effective placebo manipulation (Rohsenow & Marlatt, 1981), including informing participants that they might or might not receive a dose of alcohol ranging up to .08 g% during each session and asking them to use alcohol-free mouthwash prior to the baseline breathalyzer test, which helped mask beverage taste. Regardless of condition, doses were measured and poured from a sealed vodka bottle in view of the participants. Taste and olfactory cues were enhanced by rimming the first drink glasses with vodka, adding a squirt of 95% alcohol to the top of each drink, and wiping the bar with tequila immediately prior to participants’ entrance. Finally, after the ascending limb BrAC assessment, all participants were provided with false BrAC feedback ranging from .038 g% to .042 g% to evoke similar outcome expectancies across the alcohol and placebo conditions. Post-challenge procedures—Following dosing, subjective and response inhibition measures were collected in individual testing rooms to reduce cross-participant contamination.2 Participants completed three assessments corresponding to the ascending
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limb, peak, and descending limb of the BrAC curve. Subjective alcohol responses were assessed at all three assessments, whereas the Cued Go/No-Go Task was administered only at the peak assessment. Participants also completed additional self-report, psychomotor task, and resting heart rate measures, which were not used in the analyses reported here. After the completion of the protocol, BrAC measures were taken until participants reached . 04 g%, at which time they were driven home by project staff or a sober friend or family member. Compensation was $15 for the first laboratory session and $30 for the second, minus any introductory psychology course credit received. Twenty-two percent of participants received any course credit. Participants were subsequently enrolled in a dailydiary follow-up, the results of which are not reported here. All study procedures were approved by the university’s Institutional Review Board and followed NIAAA guidelines for administering alcohol in human subjects research (NIAAA, 2005).
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Measures Participants reported date of birth, biological sex, ethnicity, family history of alcohol problems (Mann, Sobell, Sobell, & Pavan, 1985) and other demographics. Family history was coded such that 0 = no definite family history of alcohol problems (i.e., never drank, social drinker, possible problem drinker, or don’t know/don’t remember for all reported family members) and 1 = definite family history.
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Timeline Follow-Back—Trained undergraduate research assistants administered a version of the widely used Timeline Follow-Back (TLFB) interview to assess past-30-day alcohol use, alcohol-related negative consequences, and behavioral risk-taking (Sobell & Sobell, 1992). We calculated four indices of alcohol consumption: 1) total standard drinks consumed (i.e., 12 oz of beer, 5 oz of wine, 1.5 oz of liquor); 2) frequency of binge drinking, defined as four or more standard drinks in a day for women or five or more drinks for men; 3) frequency of subjective intoxication; and 4) maximum standard drinks in a day. See Table 1 for summary statistics. BrAC—We used Intoxilyzer 5000 breathalyzers (CMI, Inc., Owensboro, KY), which produce hard-copy records, at baseline, on the ascending limb, and at the end of the sessions. All other BrAC testing used Alco-Sensor IVs (Intoximeters, Inc., St. Louis, MO). The average BrAC at the peak assessment was .083 g% (SD = .015). eTable 1 presents further BrAC data.
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Subjective alcohol responses—Participants completed the Biphasic Alcohol Effects Scale (BAES; Martin, Earleywine, Musty, & Perrine, 1993), the 7-item Subjective High Assessment Scale (SHAS; Schuckit et al., 2000), a Drug Effects Questionnaire (DEQ; Evans & Levin, 2003; Johanson & Uhlenhuth, 1980), a version of the Subjective Effects of Alcohol Scale (SEAS; Morean et al., 2013) including the 22 items from the Anticipated Effects of 2We conducted preliminary analyses to determine whether to adjust for dependency of observations as a function of laboratory session cohort. Excluding the four participants whose first and second session cohorts differed, intraclass correlations for subjective alcohol responses and Cued Go/No-Go Task inhibitory failures outcomes ranged from .00 to .32 (M = .06, SD = .11, median = .00). Only the placebo-adjusted Cued Go/No-Go Task inhibitory impairment score intraclass correlation exceeded .20. We therefore treated each participant’s data as independent of other participants’ data. Exp Clin Psychopharmacol. Author manuscript; available in PMC 2016 April 01.
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Alcohol Scale (Morean, Corbin, & Treat, 2012), and 11 items from the modified Profile of Mood States (POMS; Gabrielli, Nagoshi, Rhea, & Wilson, 1991; Stappenbeck & Fromme, 2014). Whereas the instruction set for the SEAS items following dosing asked participants to describe “the extent to which drinking alcohol has produced these feelings in you at the present time,” all other measures requested that participants describe their sensations “now” or “at the present time,” without reference to alcohol.
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From these questionnaires, we selected a priori four scales of rewarding, stimulant-like effects: (1) BAES Stimulation (7 items; alcohol and placebo peak-assessment Cronbach’s αs = 94. and .93, respectively), (2) POMS Energetic (4 items; αs = .68. and .75), (3) SEAS High Arousal Positive (4 items; αs = .90. and .94), and (4) DEQ Like (single visual analogue scale). We additionally selected four scales of sedative-like effects: (1) BAES Sedation (7 items; αs = .86. and .93), (2) POMS Intoxicated (7 “neurological affect,” “nausea,” and “sleepiness” items; αs = .85. and .72), (3) SEAS Low Arousal Negative (3 items; αs = .87. and .88), and (4) SHAS (7 items; αs = .92 and .88). Visual analogue scale (DEQ and SHAS) responses outside the upper bound of the scale were scored as the maximum possible scale value.
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Cued Go/No-Go Task—Participants completed a Cued Go/No-Go Task (Marczinski & Fillmore, 2003) on E-Prime software (Schneider, Eschman, & Zuccolotto, 2002). Participants were instructed to press a keyboard key highlighted in green as quickly as possible in response to Go targets (green rectangles) but not No-Go targets (blue rectangles). Each trial began with a fixation point (800 ms) and then a blank screen (500 ms), after which cues and targets were presented. Targets were preceded by cues that signaled the likelihood of a Go or No-Go target with 80% probability. The Go and No-Go cues were black, vertical or horizontal rectangle outlines, respectively, displayed for 1 of 5 stimulus onset asynchronies (150 – 550 ms). Targets were then displayed for 1000 ms or until the participant responded. The task consisted of 125 Go and 125 No-Go trials over approximately 15 minutes, with an inter-trial interval of 700 ms. Fast, accurate responding was encouraged by displaying the reaction time (RT) or “incorrect” following each trial. Participants completed 100 trials of the Cued Go/No-Go Task during the baseline periods to ensure familiarity with the rules governing the cue-target relationships.
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The primary task outcome was the proportion of No-Go trials with Invalid Go (i.e., vertical rectangle) cues in which the participant failed to inhibit a response. We also examined inhibitory failures following Valid No-Go cues, in addition to RTs on correct responses to Go targets. Following Fillmore and colleagues (e.g., Fillmore & Weafer, 2012), we removed all outlying trials in which RTs were less than 100 ms (101 trials or 0.49% of all nonomission Go trials) before calculating participants’ average RTs. We did not remove trials with low RTs in calculating proportions of inhibitory failures. Placebo manipulation check—Participants estimated the number of standard alcoholic drinks [they] were served during this experiment at the start of the ascending limb assessment.
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Analytic Approach
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The peak assessment was our primary assessment of interest. Although early theories stressed differences between the ascending and descending limbs of the BAC curve, recent evidence supports the predictive validity of subjective responses at peak, with greater emphasis on the magnitude and quality than on the timing of the response (King et al., 2011; King et al., 2014). Similarly, there is no evidence of change across limbs in Cued Go/No-Go Task inhibitory failures (Fillmore & Weafer, 2012).
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Our first goal was to compare measurement models of subjective responses using confirmatory factor analysis (CFA) in Mplus version 7 (Muthén & Muthén, 1998–2012). We computed placebo-adjusted change scores from mean scores on each measure. Following King and colleagues (2011), change scores represented change in alcohol responses from baseline to the peak assessment that was greater than the change produced by placebo and were computed as follows: alcohol response (peak – baseline) minus placebo response (peak – baseline). Where baseline scores were not available (i.e., DEQ Like), we used the difference between alcohol and placebo peak scores.
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Our second goal was to test whether individuals who experienced greater alcohol responses also made more inhibitory failures. We fit models in which alcohol response factors were permitted to covary with alcohol-condition proportions of inhibitory failures. We found minimal missing data, which reduced some preliminary analytic samples in SPSS 21 and Stata 13.1. For our primary analyses, however, we used full-information maximum likelihood estimation, which allowed us to analyze all available data. Because Cued Go/No Go Task inhibitory failures and placebo-adjusted SEAS Low Arousal Negative scores exceeded 3 in kurtosis, we fit all Mplus models using a robust estimator (ESTIMATOR = MLR). We report standardized Mplus results.
Results Placebo Manipulation Check Participants estimated that they consumed more drinks in the alcohol condition (M = 3.44, 95% CI [3.15, 3.74], SD = 1.33) than in the placebo condition (M = 2.09 [1.86, 2.32], SD = 1.05). However, only four participants estimated that they consumed zero drinks in placebo sessions. One participant did not estimate alcohol consumption in the alcohol condition. Alcohol-Related Disinhibition
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Cued Go/No-Go Task inhibitory failures were not normally distributed, skew ≥ 1.76, kurtosis ≥ 2.94. We therefore conducted a 2 × 2 repeated measures analysis of variance (ANOVA) using log-transformed (after adding .01 to all scores) inhibitory failures. Alcohol and Invalid Go cues increased failures relative to placebo, F (1,81) = 12.52, p < .001, ηp2 = . 13, and Valid No-Go cues, F (1,81) = 39.15, p < .001, ηp2 = .33, respectively. Alcoholcondition inhibitory failures were most common in the Invalid Go condition, but the beverage × cue interaction did not reach statistical significance, F (1,81) = 1.28, p = .26, ηp2 = .02 (Figure 1, Panel A).
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In a separate 2 × 2 repeated measures ANOVA, alcohol, F (1,81) = 18.40, p < .001, ηp2 = . 19, and Invalid No-Go cues, F (1,81) = 111.65, p < .001, ηp2 = .58, slowed reaction times. The alcohol-related slowing of reaction times did not significantly differ as a function of cues, F (1,81) = 3.74, p = .06, ηp2 = .04. Removing one case with outlying alcohol-condition reaction times resolved high skew (≥ 2.30) and kurtosis (≥ 10.86) without substantively affecting these effects. Results from the whole sample are presented in Figure 1, Panel B. Subjective Responses to Alcohol Our first major goal was to identify a measurement model for the placebo-adjusted subjective alcohol responses. eFigures 1 and 2 display the raw stimulation and sedation responses, respectively, from which we calculated placebo-adjusted difference scores. Greater difference scores on all measures indicated greater alcohol responses relative to placebo. See eTable 2 for difference score summary statistics and bivariate correlations.
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As displayed in Table 2 and suggested by the correlations in eTable 2, a single-factor model did not fit the data well. We next fit the two-factor model of subjective responses, with four indicators loading on each factor, although overall fit for this model was still poor. Modification indices suggested that this lack of fit may have been due to omitted crossloadings, and permitting two subjective sedation measures (BAES Sedation and POMS Intoxicated) to cross-load onto the subjective stimulation factor and one stimulation measure (POMS Energetic) to cross-load onto the sedation factor significantly improved model fit.
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As shown in Table 2, this final model fit the data well. The subjective stimulation factor was well defined, with standardized loadings of .80 or above for BAES Stimulation, SEAS High Arousal Positive, and POMS Energetic. Similarly, the subjective sedation factor had loadings of .87 or above for SHAS, SEAS Low Arousal Negative, and POMS Intoxicated. Unsurprisingly, the cross-loadings were all negative, such that higher scores on BAES Sedation and POMS Intoxicated were associated with lower stimulation, whereas higher scores on POMS Energetic were associated with lower sedation. The two factors were moderately but non-significantly correlated, r = .28 [−.04, .60], p = .08. See Figure 2. Subjective Responses and Alcohol-Related Disinhibition
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Having identified a good-fitting measurement model in CFA, we tested whether stimulantlike and sedative-like alcohol responses were associated with alcohol-related disinhibition, as measured by the (untransformed) proportion of inhibitory failures in the Invalid Go cue condition of the Cued Go/No-Go Task. Controlling placebo-condition inhibitory failures, alcohol-condition inhibitory failures were more common among men (p < .05) but did not differ by BrAC at peak, TLFB total drinks, or family history. We therefore included only sex as an exogenous covariate. We fit a model in which the response factors were permitted to covary with placebo- and alcohol-condition inhibitory failures. In addition, alcohol-condition inhibitory failures were regressed on placebo-condition inhibitory failures. The (residual) covariance paths therefore tested the associations of interest: Controlling sex and placebo performance, were alcohol responses associated with alcohol-condition inhibitory failures?
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This sex-controlled model is illustrated in Figure 3. The model fit the data well, χ2 (34) = 47.27, p = .06, CFI = .96, RMSEA = .07. There was a positive, small-to-medium-sized association between subjective stimulation and variance in alcohol-condition inhibitory failures not explained by placebo-condition failures, r = .19 [.02, .35], p = .03. This path could not be constrained to zero without significant decrement in model fit, Satorra-Bentler scaled Δχ2 (1) = 4.36, p = .04. That is, controlling sex and placebo performance, participants who experienced greater stimulation also made more alcohol-condition inhibitory failures. In contrast, subjective sedation was not associated with inhibitory failures, r = .12 [−.18, . 42], p = .44. Sensitivity Analyses
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We repeated three key analyses in order to determine whether the results differed excluding the four participants with a potentially ineffective placebo manipulation. Results were largely consistent with those reported above. First, alcohol and Invalid Go cues increased inhibitory failures relative to placebo (p < .001, ηp2 = .16) and Valid No-Go cues (p < .001, ηp2 = .35), respectively, but the beverage × cue interaction was not significant, p = .18, ηp2 = .02. Second, the final measurement model fit the subjective response data well, χ2 (16) = 11.92, p = .75, CFI = 1.00, RMSEA = .00. Third, in the sex-controlled model, stimulation was associated with alcohol-condition inhibitory failures (r = .20 [.04, .36], p = .02), but sedation was not, r = .11 [−.19, .42], p = .47. The stimulation path could not be constrained to zero without loss of fit, scaled Δχ2 (1) = 4.94, p = .03.
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We also tested the sensitivity of the association between subjective stimulation and alcoholrelated inhibitory failures in the full sample. First, we repeated our analyses with an alcohol minus placebo inhibitory failures difference score (Weafer & Fillmore, 2008). These results were largely consistent with those of our initial models. Most important, subjective stimulation was positively associated with placebo-adjusted inhibitory failures, r = .19 [.02, . 36], p = .03. Constraining the stimulation path to zero resulted in a marginal decrement in model fit, scaled Δχ2 (1) = 3.75, p = .05. Second, we fit a model without the sex covariate. In this model, stimulation was not significantly associated with alcohol-condition inhibitory failures (r = .15 [−.03, .33], p = .10), and the stimulation path could be constrained to zero without loss of model fit, scaled Δχ2 (1) = 2.48, p = .12.3
Discussion
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The present results highlight the breadth of alcohol’s subjective and disinhibiting effects. First, we found that, among recent binge drinkers with varying—but generally moderate— levels of typical drinking, placebo-adjusted subjective responses to group-administered, orally consumed alcohol in a simulated bar were better explained by two latent factors representing stimulation and sedation. These results are consistent with factor analyses of responses to individually administered, non-placebo-controlled oral and IV alcohol
3We also examined the robustness of the association to removal of scores for two participants with minor irregularities in Cued Go/No-Go Task procedures. Coding the values as missing produced a comparable association between stimulation and alcoholcondition inhibitory failures, r = .18 [.02, .35], p = .03. The association again could not be constrained to zero without loss of fit, scaled Δχ2 (1) = 4.34, p = .04.
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challenges (Bujarski et al., 2015; Ray et al., 2009). We found that most subjective measures, including scales such as the SHAS that have been used extensively in prior research, could be clearly understood as capturing either stimulation or sedation. Three subjective measures, however, loaded positively onto one factor and inversely on the other. Although crossloadings have been weaker and inconsistent in previous factor analyses (Bujarski et al., 2015; Ray et al., 2009), they may not be surprising. The BAES stimulation and sedation difference scores, for example, were themselves inversely correlated (albeit nonsignificantly), and they include items such as “up” and “down,” respectively, which could lead to inverse associations. Taken as a whole, though, this converging evidence suggests that future studies of subjective alcohol responses should consider both subjective stimulation and sedation, perhaps by using multiple measures or the SEAS, the subscales of which loaded highly onto their respective factors here and had no cross-loadings. It should be noted, however, that our results cannot speak to other subjective alcohol effects, such as craving, arousing-yet-aversive effects, or negatively reinforcing effects (Bujarski et al., 2015; Morean et al., 2013).
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Considered with recent findings, our results have implications for the potential role of subjective alcohol responses in motivating alcohol use. Specifically, greater stimulation and lower sedation predict increased AUD symptoms (King et al., 2014), but neither stimulation nor sedation appears to change substantially over time among young adults, even with heavier drinking (King, Hasin, O’Connor, McNamara, & Cao, 2015, May 14). As discussed by King and colleagues (2015, May 14), this pattern is most consistent with the early stage of an allostasis model of drinking motivation, during which positive reinforcement (e.g., greater stimulation) drives use. That our findings continue to support the stimulation vs. sedation distinction underscores how alcohol use can be differentially positively reinforcing to some early adult drinkers. An important question, therefore, is whether continued drinking might change alcohol responses or whether responses reflect truly stable, perhaps genetically based, individual differences (Schuckit, 2009).
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Our second substantial finding was a small-to-moderate association between subjective stimulation—but not sedation—and alcohol-related disinhibition when controlling sex. To our knowledge, this study is the first to report such an association, although it complements a recent finding of a strong association between stimulation and risk-taking choices in an IV-alcohol-administration study (Gilman et al., 2012). These results raise the possibility that alcohol sensitivity may have implications for the immediate behavioral consequences of intoxication. Specifically, if greater stimulation responders also experience greater alcoholrelated disinhibition, they may be more likely to engage in other dangerous behaviors while intoxicated. One diary study of real-world drinking, for example, found that individuals who reported stronger subjective intoxication magnitude were more likely to engage in aggressive behavior (Quinn, Stappenbeck, & Fromme, 2013). If the link between stimulation and disinhibition can be replicated, further study will be needed to determine the underlying mechanism. The euphoric experience produced by stimulant-like effects may result in an impelling disinhibition of behavior. Alternatively, disinhibition itself might produce positive sensations, or the association may reflect an underlying broader sensitivity to alcohol effects or unmeasured confounding.
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It is also important to note that the statistical significance of the association between alcohol-related stimulation and disinhibition apparently required controlling sex. At nearly half a standard deviation in magnitude, the sex difference in alcohol-condition inhibitory failures (controlling placebo inhibitory failures) was not trivial. This pattern matches previous studies of the Cued Go/No-Go Task, which have found that men make more inhibitory failures under alcohol only (Fillmore & Weafer, 2004) and under both alcohol and placebo (Weafer & Fillmore, 2012). It is not yet clear whether this pattern reflects a genuine sex difference or task-specific factors (see Weafer & de Wit, 2014 for a review), and the generalizability of our results to other tasks, other BAC levels, and real-world behavior is uncertain. For example, Weafer and Fillmore (2012) found no association between Cued Go/No-Go Task response inhibition and a measure of attentional inhibition, suggesting that alcohol may differentially impact various aspects of inhibitory control. Consequently, the stimulation-disinhibition association should be understood in light of outstanding questions regarding generalizability and sex differences in disinhibition.
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Three additional methodological considerations are relevant to the results of this study. First, to the extent that subjective responses are thought to influence drinking motivation, it may be valuable to ensure that laboratory assessment captures responses as they would be experienced in real-world drinking and to differentiate them from placebo responses. Our simulated-bar approach was intended to increase ecological validity while enhancing placebo efficacy, and the results held in analyses limited to participants who reported a positive placebo manipulation. There are trade-offs between ecological validity and internal validity, however, and a simulated bar cannot perfectly reproduce a real-world drinking setting. The concomitant limitations of the present design included decreased control over BrAC levels and the drinking environment, some participant attrition, and differing numbers of participants and, in select cases, differing members (including confederates) in laboratory session groups. It is also important to note that, on its own, this study cannot examine the value of enhanced ecological validity. That our approach complements prior studies with more experimental control but less adjustment for expectancies (e.g., Bujarski et al., 2015), however, increases confidence in the accumulated findings.
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Second, our sample was not small for an alcohol challenge but was modest for a study of individual differences. Its size may have limited power in the structural equation models, and it constrained our ability to test sex differences in the associations of interest. Given that our findings suggested an important role for sex differences in response inhibition, these should be examined in future research. Relatedly, we targeted recent binge drinkers for recruitment to ensure that we sampled from those at risk of alcohol-related consequences, and the included sample was generally moderate in typical alcohol consumption. Our conclusions may not generalize to other populations. Third, the use of difference scores to adjust for placebo responses may have reduced the reliabilities of the subjective measures. This reduction should have been attenuated by the latent measurement approach. Nevertheless, it highlights the importance of identifying the ideal method of accounting for placebo responses in measuring alcohol’s subjective effects.
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In spite of these limitations, our findings have several implications for future research. First, high-quality measurement of subjective responses to alcohol is crucial, and our findings add to previous studies in indicating that subjective stimulation and sedation can and should be differentiated. Second, this study is the first, to our knowledge, to report an association between subjective stimulation and alcohol-related disinhibition. Although this association was not strong, it was robust across alternative tests when controlling sex. If it is replicated, it would suggest that alcohol-related stimulation is not merely a set of hedonically rewarding sensations but may also relate to differences in intoxicated behavior as well.
Supplementary Material Refer to Web version on PubMed Central for supplementary material.
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Cued Go/No-Go Task natural-log transformed proportions of inhibitory failures (Panel A) and reaction times (Panel B). Bars are standard errors.
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Figure 2.
Measurement model of subjective alcohol responses. Values are standardized loadings and correlations. Residual variances not shown. Solid paths indicate p < .05; dashed path indicates p = .08. Parenthetical values are (unstandardized) model constraints.
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Figure 3.
Standardized parameter estimates for associations between subjective alcohol responses and alcohol-related inhibitory failures. Sex estimates are standardized mean differences. Solid lines indicate p < .05; dashed lines indicate p > .05. Parenthetical values are (unstandardized) model constraints. Measurement model for stimulation and sedation is not shown (see Figure 2).
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Author Manuscript 0 – 28 0 – 15 0 – 17
Maximum drinks
Frequency of binge
Frequency of intoxication
6.29
4.82
8.56
41.37
8.05
M
3.53
3.46
4.37
27.15
3.18
SD
4.95
4.63
11.41
50.48
8.83
M 2.84
SD
3.40
3.44
5.67
33.79
Male
.39
.05
−.56
−.30
−.26
Cohen’s d
n = 76 for Timeline Follow-Back. Data excluded for incompleteness (4 participants) or improbable values (total standard drinks ≥ 171; 2 participants).
a
Note. Positive Cohen’s d values indicate female > male. Bolded rows indicate p < .05. AUDIT (Alcohol Use Disorders Identification Test) scores are from screening and include re-tested scores when more than one month elapsed between screening and the first laboratory session.
0 – 139
3 – 15
Total standard drinks
30-day Timeline Follow-Backa
AUDIT
Observed Range
Female
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Variable
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Typical Alcohol Use Summary Statistics
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Table 1 Quinn and Fromme Page 18
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Strictly positive scaled chi-square difference test (Satorra & Bentler, 2010).
b
Satorra-Bentler scaled chi-square difference test.
p < .05.
a
*
Note. Bolded model was selected for subsequent analyses.
Model 3 plus POMS Energetic cross-loading
4
Two factors (Stimulation and Sedation)
2
Model 2 plus POMS Intoxicated and BAES Sedation cross-loadings
Single factor
1
3
Description
Model
.81
75.05* (19)
12.29 (16)
1.00
.96
.16
262.72* (20)
29.07* (17)
CFI
χ2 (df)
.00
.09
.19
.39
RMSEA
2782.79
2798.08
2837.89
2946.29
AIC
2850.18
2863.06
2898.06
3004.05
BIC
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Measurement Model Fit Statistics
16.41* (1)b
136.43* (2)a
--
--
Δχ2 (df)
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Table 2 Quinn and Fromme Page 19
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Exp Clin Psychopharmacol. Author manuscript; available in PMC 2016 April 01. Published in final edited form as: Exp Clin Psychopharmacol. 2016 April ; 24(2): 90–99. doi:10.1037/pha0000065.
Individual Differences in Subjective Alcohol Responses and Alcohol-Related Disinhibition Patrick D. Quinn, PhD and Kim Fromme, PhD The University of Texas at Austin
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There are important individual differences in acute subjective responses to alcohol, which have often been assessed using self-report measures. There is also evidence of meaningful betweenpersons variation in alcohol’s disinhibiting effects on behavior, such that some individuals become more impaired on tasks of inhibition than do others after an intoxicating dose. The degree to which subjective alcohol responses correspond with these disinhibition effects is not yet clear. In this study, we tested associations among indices of subjective alcohol responses and their correspondence with sensitivity to alcohol-related disinhibition. We recruited recent-bingedrinking emerging adults (N = 82) for a group-administered, placebo-controlled, within-subject, counterbalanced alcohol challenge in a simulated bar laboratory. Confirmatory factor analyses revealed that a two factor model with several cross-loadings explained associations among the subjective measures well, replicating a differentiation between stimulant-like and sedative-like subjective responses. Controlling sex and placebo performance, participants who reported greater subjective stimulant-like effects—but not sedative-like effects—experienced more alcohol-related disinhibition, as measured by Cued Go/No-Go Task inhibitory failures. This association was small-to-moderate in magnitude. The results of this study highlight the distinction between stimulant-like and sedative-like subjective alcohol effects. They suggest, additionally, that there may be modest commonalities between alcohol’s acute impacts on subjective stimulation and objective disinhibition.
Keywords Alcohol sensitivity; stimulation; sedation; inhibition; Cued Go/No-Go Task
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There are important individual differences in responses to the acute effects of alcohol (for reviews, see Morean & Corbin, 2010; Quinn & Fromme, 2011; Ray, MacKillop, & Monti, 2010). Although these differences are often assessed using self-reported, subjective measures, differential sensitivity has also been found on physiological indices (Brunelle, Barrett, & Pihl, 2007; King, de Wit, McNamara, & Cao, 2011; Schuckit, 1985). That is, some individuals experience alcohol’s pharmacological effects to a greater extent than do others, even when doses are identical.
Correspondence concerning this article should be addressed to: Patrick D. Quinn, Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E. 10th St., Bloomington, IN 47405. Phone: (812) 856-2588. [email protected]. Patrick D. Quinn is now at Indiana University Bloomington.
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An essential question has been whether to conceptualize subjective sensitivity to alcohol as a unidimensional construct varying only in magnitude or as a set of multiple constructs varying in both magnitude and quality (Ray, MacKillop, Leventhal, & Hutchison, 2009). Importantly, research driven by an understanding of alcohol’s acute biphasic nature has demonstrated that alcohol produces both aversive, sedative-like effects (characterizing the descending blood alcohol concentration [BAC] limb) and euphoric, stimulant-like effects (characterizing the ascending limb; Newlin & Thomson, 1990). Both of these sets of effects appear relevant to risk for alcohol use disorder (AUD), likely because of their distinct roles in motivating alcohol use (King, McNamara, Hasin, & Cao, 2014). There continue, however, to be advances in research using a unidimensional alcohol response conceptualization that emphasizes magnitude but not quality (e.g., Paulus et al., 2012; Trim, Schuckit, & Smith, 2013). This inconsistency underscores the need for clarity regarding the factor structure of subjective alcohol effects.
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Two recent factor analyses of responses to oral (Ray et al., 2009) and intravenous (IV; Bujarski, Hutchison, Roche, & Ray, 2015) alcohol have found that existing subjective measures reflect multiple factors, including positively valenced, stimulant-like effects and negatively valenced, sedative-like effects, in addition to craving and negative affect or its alleviation. Moreover, the Subjective High Assessment Scale, a measure of response magnitude used in much early research, loaded onto the sedation factor in both studies. These factor analyses support the perspective that alcohol responses can and should be distinguished into subjective stimulation and sedation—likely among other qualities as well (Morean, Corbin, & Treat, 2013). At the same time, however, both were conducted in settings that likely differed from typical consumption (e.g., individually, using IV administration) and without placebo controls, and the authors noted that further examination with greater ecological validity is warranted (Bujarski et al., 2015). Largely separate from this literature, a body of research has developed implicating acute alcohol intoxication as a causal factor in risk-taking or disinhibited behaviors. Theoretical accounts posit that alcohol’s effects on controlled processing, and response inhibition in particular, may play a role in these outcomes, at least under certain conditions (Fillmore, 2003; Giancola, Josephs, Parrott, & Duke, 2010; Moss & Albery, 2009). Of note, emerging evidence suggests that there may be meaningful individual differences in alcohol’s effects on response inhibition. Specifically, two studies have found that differential alcohol-related response disinhibition predicts ad libitum drinking (Gan et al., 2014; Weafer & Fillmore, 2008).
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Given the extensive literature on alcohol responses and the emerging evidence of individual variation in alcohol-related disinhibition, it will be important to determine the extent to which subjective responses correspond with differences in alcohol-related disinhibition. One study found that IV-alcohol-induced increases in risky decision-making were greater among individuals who reported stronger subjective stimulation or weaker subjective sedation, raising the possibility that stimulation “may be a marker for increased likelihood of risk taking following alcohol consumption” (Gilman, Smith, Ramchandani, Momenan, & Hommer, 2012, p. 474). Further examination would help clarify the relation between subjective alcohol responses and alcohol-related disinhibition.
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The Present Study We examined relations among subjective alcohol responses and alcohol-related disinhibition, with an emphasis on ecological validity. Young people are at increased risk for problematic alcohol involvement (Grant et al., 2015; Hingson, Zha, & Weitzman, 2009), and their alcohol consumption is often socially based and socially rewarding (Sayette et al., 2012). Given this context, we recruited emerging-adult drinkers for a placebo-controlled, group-administered, oral alcohol challenge in a simulated bar to test two questions. First, to what extent are subjective stimulant-like and sedative-like effects distinct? Second, to what extent are these responses associated with individual differences in alcohol-related disinhibition?
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Participants
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Participants (N = 82) were emerging adults aged 21 – 25 who reported two or more typicalweek drinking occasions, in addition to one or more binge drinking episodes (four or more standard drinks for women and five or more standard drinks for men, both in a two-hour period) during the two weeks prior to screening. We targeted an equal number of male and female participants from the surrounding community and introductory psychology subject pool of a large, public university. Exclusion criteria were as follows: possible alcohol dependence, defined as Alcohol Use Disorders Identification Test score greater than 15 (AUDIT; Babor, Higgins-Biddle, Saunders, & Monteiro, 2001); self-reported pregnancy, possible pregnancy, or positive pregnancy test (for women);1 and other self-reported medical, personal, or ethical contraindications to drinking alcohol, including a “flushing” response. Of the 97 eligible participants who completed one laboratory session, 84 returned for a second session. Of these participants, 2 did not reach a threshold breath alcohol concentration (BrAC; defined as .05 g%), resulting in the final sample of 40 female (45% White, 28% Asian or Asian-American, 8% Hispanic or Latina, 5% African-American, and 15% multiethnic) and 42 male participants (48% White, 31% Asian or Asian-American, 14% Hispanic or Latino, 2% African-American, and 5% multiethnic or other ethnicities). The mean age among women was 22.04 years (SD = 0.85, range = 21.05 – 24.70) and among men was 22.30 years (SD = 1.13, range = 21.08 – 25.40), and 33% of women and 38% of men reported a positive family history of alcohol problems (of n = 81 who provided complete family history data).
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Participants who completed only the first session were modestly but non-significantly (ps > . 05) more likely than those who completed both sessions to be older (Cohen’s d = .12), male (odds ratio [OR] = 2.15), White (versus non-White; OR = 1.94), or randomized to receive placebo first, OR = 1.76. They were also more likely to report a positive family history (OR = 2.17) and more total drinks (d = .23) and times drunk, d = .19. However, they were also
1A subset of participants (n = 8 included women) were additionally screened for ineligibility on the basis of self-reported nursing in accordance with a change in procedures requested by the Institutional Review Board. Exp Clin Psychopharmacol. Author manuscript; available in PMC 2016 April 01.
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less likely to score higher on the AUDIT (d = −.03), maximum drinks (d = −.05), and binge drinking, d = −.24. Procedures Participants completed a group-administered, within-subject, counter-balanced, placebocontrolled alcohol challenge study across two laboratory sessions separated by an average of 9.23 days (median = 7, SD = 9.06, range = 6 – 70). Participants were instructed to eat a full meal at least four hours before the start of the session and to abstain from alcohol for 48 hours and from caffeine and tobacco for 3 hours prior to the session. At baseline, female participants screened for pregnancy with urine hCG tests, and all participants provided informed consent (first session), screened for a .000 g% BrAC, and completed a baseline assessment, which included measures not reported here (for further details, see Quinn, 2014).
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Alcohol challenge—Sessions were conducted in a simulated bar in variable-sexcomposition groups of three or four participants. All participants in a given session cohort were randomly assigned to the same condition (alcohol or placebo), although some cohorts included four individuals (participants and confederates) in one session and three in the other. Four participants returned on different occasions from the remainder of their groups. In cases when participants were unable to attend their scheduled sessions, undergraduate research assistants served as confederates in order to maintain a minimum of three individuals per laboratory session. Confederates were trained to engage neutrally with participants to help maintain a similar social milieu to that of the other sessions. Confederates always consumed placebo beverages but followed all other procedures during the dose-administration portion of the protocol.
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Participants had 10 minutes to consume each of 3 drinks containing a 1:3 ratio of 40%alcohol-by-volume vodka or a decarbonated tonic water placebo and mixer (cranberry juice, diet cherry soda, and sweetened lime juice). Alcohol doses were calculated using sex and weight to target a peak BrAC of .08 g%. First-session dosing was double-blinded through the alcohol-administration procedures. We followed standard procedures to ensure an effective placebo manipulation (Rohsenow & Marlatt, 1981), including informing participants that they might or might not receive a dose of alcohol ranging up to .08 g% during each session and asking them to use alcohol-free mouthwash prior to the baseline breathalyzer test, which helped mask beverage taste. Regardless of condition, doses were measured and poured from a sealed vodka bottle in view of the participants. Taste and olfactory cues were enhanced by rimming the first drink glasses with vodka, adding a squirt of 95% alcohol to the top of each drink, and wiping the bar with tequila immediately prior to participants’ entrance. Finally, after the ascending limb BrAC assessment, all participants were provided with false BrAC feedback ranging from .038 g% to .042 g% to evoke similar outcome expectancies across the alcohol and placebo conditions. Post-challenge procedures—Following dosing, subjective and response inhibition measures were collected in individual testing rooms to reduce cross-participant contamination.2 Participants completed three assessments corresponding to the ascending
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limb, peak, and descending limb of the BrAC curve. Subjective alcohol responses were assessed at all three assessments, whereas the Cued Go/No-Go Task was administered only at the peak assessment. Participants also completed additional self-report, psychomotor task, and resting heart rate measures, which were not used in the analyses reported here. After the completion of the protocol, BrAC measures were taken until participants reached . 04 g%, at which time they were driven home by project staff or a sober friend or family member. Compensation was $15 for the first laboratory session and $30 for the second, minus any introductory psychology course credit received. Twenty-two percent of participants received any course credit. Participants were subsequently enrolled in a dailydiary follow-up, the results of which are not reported here. All study procedures were approved by the university’s Institutional Review Board and followed NIAAA guidelines for administering alcohol in human subjects research (NIAAA, 2005).
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Measures Participants reported date of birth, biological sex, ethnicity, family history of alcohol problems (Mann, Sobell, Sobell, & Pavan, 1985) and other demographics. Family history was coded such that 0 = no definite family history of alcohol problems (i.e., never drank, social drinker, possible problem drinker, or don’t know/don’t remember for all reported family members) and 1 = definite family history.
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Timeline Follow-Back—Trained undergraduate research assistants administered a version of the widely used Timeline Follow-Back (TLFB) interview to assess past-30-day alcohol use, alcohol-related negative consequences, and behavioral risk-taking (Sobell & Sobell, 1992). We calculated four indices of alcohol consumption: 1) total standard drinks consumed (i.e., 12 oz of beer, 5 oz of wine, 1.5 oz of liquor); 2) frequency of binge drinking, defined as four or more standard drinks in a day for women or five or more drinks for men; 3) frequency of subjective intoxication; and 4) maximum standard drinks in a day. See Table 1 for summary statistics. BrAC—We used Intoxilyzer 5000 breathalyzers (CMI, Inc., Owensboro, KY), which produce hard-copy records, at baseline, on the ascending limb, and at the end of the sessions. All other BrAC testing used Alco-Sensor IVs (Intoximeters, Inc., St. Louis, MO). The average BrAC at the peak assessment was .083 g% (SD = .015). eTable 1 presents further BrAC data.
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Subjective alcohol responses—Participants completed the Biphasic Alcohol Effects Scale (BAES; Martin, Earleywine, Musty, & Perrine, 1993), the 7-item Subjective High Assessment Scale (SHAS; Schuckit et al., 2000), a Drug Effects Questionnaire (DEQ; Evans & Levin, 2003; Johanson & Uhlenhuth, 1980), a version of the Subjective Effects of Alcohol Scale (SEAS; Morean et al., 2013) including the 22 items from the Anticipated Effects of 2We conducted preliminary analyses to determine whether to adjust for dependency of observations as a function of laboratory session cohort. Excluding the four participants whose first and second session cohorts differed, intraclass correlations for subjective alcohol responses and Cued Go/No-Go Task inhibitory failures outcomes ranged from .00 to .32 (M = .06, SD = .11, median = .00). Only the placebo-adjusted Cued Go/No-Go Task inhibitory impairment score intraclass correlation exceeded .20. We therefore treated each participant’s data as independent of other participants’ data. Exp Clin Psychopharmacol. Author manuscript; available in PMC 2016 April 01.
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Alcohol Scale (Morean, Corbin, & Treat, 2012), and 11 items from the modified Profile of Mood States (POMS; Gabrielli, Nagoshi, Rhea, & Wilson, 1991; Stappenbeck & Fromme, 2014). Whereas the instruction set for the SEAS items following dosing asked participants to describe “the extent to which drinking alcohol has produced these feelings in you at the present time,” all other measures requested that participants describe their sensations “now” or “at the present time,” without reference to alcohol.
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From these questionnaires, we selected a priori four scales of rewarding, stimulant-like effects: (1) BAES Stimulation (7 items; alcohol and placebo peak-assessment Cronbach’s αs = 94. and .93, respectively), (2) POMS Energetic (4 items; αs = .68. and .75), (3) SEAS High Arousal Positive (4 items; αs = .90. and .94), and (4) DEQ Like (single visual analogue scale). We additionally selected four scales of sedative-like effects: (1) BAES Sedation (7 items; αs = .86. and .93), (2) POMS Intoxicated (7 “neurological affect,” “nausea,” and “sleepiness” items; αs = .85. and .72), (3) SEAS Low Arousal Negative (3 items; αs = .87. and .88), and (4) SHAS (7 items; αs = .92 and .88). Visual analogue scale (DEQ and SHAS) responses outside the upper bound of the scale were scored as the maximum possible scale value.
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Cued Go/No-Go Task—Participants completed a Cued Go/No-Go Task (Marczinski & Fillmore, 2003) on E-Prime software (Schneider, Eschman, & Zuccolotto, 2002). Participants were instructed to press a keyboard key highlighted in green as quickly as possible in response to Go targets (green rectangles) but not No-Go targets (blue rectangles). Each trial began with a fixation point (800 ms) and then a blank screen (500 ms), after which cues and targets were presented. Targets were preceded by cues that signaled the likelihood of a Go or No-Go target with 80% probability. The Go and No-Go cues were black, vertical or horizontal rectangle outlines, respectively, displayed for 1 of 5 stimulus onset asynchronies (150 – 550 ms). Targets were then displayed for 1000 ms or until the participant responded. The task consisted of 125 Go and 125 No-Go trials over approximately 15 minutes, with an inter-trial interval of 700 ms. Fast, accurate responding was encouraged by displaying the reaction time (RT) or “incorrect” following each trial. Participants completed 100 trials of the Cued Go/No-Go Task during the baseline periods to ensure familiarity with the rules governing the cue-target relationships.
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The primary task outcome was the proportion of No-Go trials with Invalid Go (i.e., vertical rectangle) cues in which the participant failed to inhibit a response. We also examined inhibitory failures following Valid No-Go cues, in addition to RTs on correct responses to Go targets. Following Fillmore and colleagues (e.g., Fillmore & Weafer, 2012), we removed all outlying trials in which RTs were less than 100 ms (101 trials or 0.49% of all nonomission Go trials) before calculating participants’ average RTs. We did not remove trials with low RTs in calculating proportions of inhibitory failures. Placebo manipulation check—Participants estimated the number of standard alcoholic drinks [they] were served during this experiment at the start of the ascending limb assessment.
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Analytic Approach
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The peak assessment was our primary assessment of interest. Although early theories stressed differences between the ascending and descending limbs of the BAC curve, recent evidence supports the predictive validity of subjective responses at peak, with greater emphasis on the magnitude and quality than on the timing of the response (King et al., 2011; King et al., 2014). Similarly, there is no evidence of change across limbs in Cued Go/No-Go Task inhibitory failures (Fillmore & Weafer, 2012).
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Our first goal was to compare measurement models of subjective responses using confirmatory factor analysis (CFA) in Mplus version 7 (Muthén & Muthén, 1998–2012). We computed placebo-adjusted change scores from mean scores on each measure. Following King and colleagues (2011), change scores represented change in alcohol responses from baseline to the peak assessment that was greater than the change produced by placebo and were computed as follows: alcohol response (peak – baseline) minus placebo response (peak – baseline). Where baseline scores were not available (i.e., DEQ Like), we used the difference between alcohol and placebo peak scores.
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Our second goal was to test whether individuals who experienced greater alcohol responses also made more inhibitory failures. We fit models in which alcohol response factors were permitted to covary with alcohol-condition proportions of inhibitory failures. We found minimal missing data, which reduced some preliminary analytic samples in SPSS 21 and Stata 13.1. For our primary analyses, however, we used full-information maximum likelihood estimation, which allowed us to analyze all available data. Because Cued Go/No Go Task inhibitory failures and placebo-adjusted SEAS Low Arousal Negative scores exceeded 3 in kurtosis, we fit all Mplus models using a robust estimator (ESTIMATOR = MLR). We report standardized Mplus results.
Results Placebo Manipulation Check Participants estimated that they consumed more drinks in the alcohol condition (M = 3.44, 95% CI [3.15, 3.74], SD = 1.33) than in the placebo condition (M = 2.09 [1.86, 2.32], SD = 1.05). However, only four participants estimated that they consumed zero drinks in placebo sessions. One participant did not estimate alcohol consumption in the alcohol condition. Alcohol-Related Disinhibition
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Cued Go/No-Go Task inhibitory failures were not normally distributed, skew ≥ 1.76, kurtosis ≥ 2.94. We therefore conducted a 2 × 2 repeated measures analysis of variance (ANOVA) using log-transformed (after adding .01 to all scores) inhibitory failures. Alcohol and Invalid Go cues increased failures relative to placebo, F (1,81) = 12.52, p < .001, ηp2 = . 13, and Valid No-Go cues, F (1,81) = 39.15, p < .001, ηp2 = .33, respectively. Alcoholcondition inhibitory failures were most common in the Invalid Go condition, but the beverage × cue interaction did not reach statistical significance, F (1,81) = 1.28, p = .26, ηp2 = .02 (Figure 1, Panel A).
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In a separate 2 × 2 repeated measures ANOVA, alcohol, F (1,81) = 18.40, p < .001, ηp2 = . 19, and Invalid No-Go cues, F (1,81) = 111.65, p < .001, ηp2 = .58, slowed reaction times. The alcohol-related slowing of reaction times did not significantly differ as a function of cues, F (1,81) = 3.74, p = .06, ηp2 = .04. Removing one case with outlying alcohol-condition reaction times resolved high skew (≥ 2.30) and kurtosis (≥ 10.86) without substantively affecting these effects. Results from the whole sample are presented in Figure 1, Panel B. Subjective Responses to Alcohol Our first major goal was to identify a measurement model for the placebo-adjusted subjective alcohol responses. eFigures 1 and 2 display the raw stimulation and sedation responses, respectively, from which we calculated placebo-adjusted difference scores. Greater difference scores on all measures indicated greater alcohol responses relative to placebo. See eTable 2 for difference score summary statistics and bivariate correlations.
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As displayed in Table 2 and suggested by the correlations in eTable 2, a single-factor model did not fit the data well. We next fit the two-factor model of subjective responses, with four indicators loading on each factor, although overall fit for this model was still poor. Modification indices suggested that this lack of fit may have been due to omitted crossloadings, and permitting two subjective sedation measures (BAES Sedation and POMS Intoxicated) to cross-load onto the subjective stimulation factor and one stimulation measure (POMS Energetic) to cross-load onto the sedation factor significantly improved model fit.
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As shown in Table 2, this final model fit the data well. The subjective stimulation factor was well defined, with standardized loadings of .80 or above for BAES Stimulation, SEAS High Arousal Positive, and POMS Energetic. Similarly, the subjective sedation factor had loadings of .87 or above for SHAS, SEAS Low Arousal Negative, and POMS Intoxicated. Unsurprisingly, the cross-loadings were all negative, such that higher scores on BAES Sedation and POMS Intoxicated were associated with lower stimulation, whereas higher scores on POMS Energetic were associated with lower sedation. The two factors were moderately but non-significantly correlated, r = .28 [−.04, .60], p = .08. See Figure 2. Subjective Responses and Alcohol-Related Disinhibition
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Having identified a good-fitting measurement model in CFA, we tested whether stimulantlike and sedative-like alcohol responses were associated with alcohol-related disinhibition, as measured by the (untransformed) proportion of inhibitory failures in the Invalid Go cue condition of the Cued Go/No-Go Task. Controlling placebo-condition inhibitory failures, alcohol-condition inhibitory failures were more common among men (p < .05) but did not differ by BrAC at peak, TLFB total drinks, or family history. We therefore included only sex as an exogenous covariate. We fit a model in which the response factors were permitted to covary with placebo- and alcohol-condition inhibitory failures. In addition, alcohol-condition inhibitory failures were regressed on placebo-condition inhibitory failures. The (residual) covariance paths therefore tested the associations of interest: Controlling sex and placebo performance, were alcohol responses associated with alcohol-condition inhibitory failures?
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This sex-controlled model is illustrated in Figure 3. The model fit the data well, χ2 (34) = 47.27, p = .06, CFI = .96, RMSEA = .07. There was a positive, small-to-medium-sized association between subjective stimulation and variance in alcohol-condition inhibitory failures not explained by placebo-condition failures, r = .19 [.02, .35], p = .03. This path could not be constrained to zero without significant decrement in model fit, Satorra-Bentler scaled Δχ2 (1) = 4.36, p = .04. That is, controlling sex and placebo performance, participants who experienced greater stimulation also made more alcohol-condition inhibitory failures. In contrast, subjective sedation was not associated with inhibitory failures, r = .12 [−.18, . 42], p = .44. Sensitivity Analyses
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We repeated three key analyses in order to determine whether the results differed excluding the four participants with a potentially ineffective placebo manipulation. Results were largely consistent with those reported above. First, alcohol and Invalid Go cues increased inhibitory failures relative to placebo (p < .001, ηp2 = .16) and Valid No-Go cues (p < .001, ηp2 = .35), respectively, but the beverage × cue interaction was not significant, p = .18, ηp2 = .02. Second, the final measurement model fit the subjective response data well, χ2 (16) = 11.92, p = .75, CFI = 1.00, RMSEA = .00. Third, in the sex-controlled model, stimulation was associated with alcohol-condition inhibitory failures (r = .20 [.04, .36], p = .02), but sedation was not, r = .11 [−.19, .42], p = .47. The stimulation path could not be constrained to zero without loss of fit, scaled Δχ2 (1) = 4.94, p = .03.
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We also tested the sensitivity of the association between subjective stimulation and alcoholrelated inhibitory failures in the full sample. First, we repeated our analyses with an alcohol minus placebo inhibitory failures difference score (Weafer & Fillmore, 2008). These results were largely consistent with those of our initial models. Most important, subjective stimulation was positively associated with placebo-adjusted inhibitory failures, r = .19 [.02, . 36], p = .03. Constraining the stimulation path to zero resulted in a marginal decrement in model fit, scaled Δχ2 (1) = 3.75, p = .05. Second, we fit a model without the sex covariate. In this model, stimulation was not significantly associated with alcohol-condition inhibitory failures (r = .15 [−.03, .33], p = .10), and the stimulation path could be constrained to zero without loss of model fit, scaled Δχ2 (1) = 2.48, p = .12.3
Discussion
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The present results highlight the breadth of alcohol’s subjective and disinhibiting effects. First, we found that, among recent binge drinkers with varying—but generally moderate— levels of typical drinking, placebo-adjusted subjective responses to group-administered, orally consumed alcohol in a simulated bar were better explained by two latent factors representing stimulation and sedation. These results are consistent with factor analyses of responses to individually administered, non-placebo-controlled oral and IV alcohol
3We also examined the robustness of the association to removal of scores for two participants with minor irregularities in Cued Go/No-Go Task procedures. Coding the values as missing produced a comparable association between stimulation and alcoholcondition inhibitory failures, r = .18 [.02, .35], p = .03. The association again could not be constrained to zero without loss of fit, scaled Δχ2 (1) = 4.34, p = .04.
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challenges (Bujarski et al., 2015; Ray et al., 2009). We found that most subjective measures, including scales such as the SHAS that have been used extensively in prior research, could be clearly understood as capturing either stimulation or sedation. Three subjective measures, however, loaded positively onto one factor and inversely on the other. Although crossloadings have been weaker and inconsistent in previous factor analyses (Bujarski et al., 2015; Ray et al., 2009), they may not be surprising. The BAES stimulation and sedation difference scores, for example, were themselves inversely correlated (albeit nonsignificantly), and they include items such as “up” and “down,” respectively, which could lead to inverse associations. Taken as a whole, though, this converging evidence suggests that future studies of subjective alcohol responses should consider both subjective stimulation and sedation, perhaps by using multiple measures or the SEAS, the subscales of which loaded highly onto their respective factors here and had no cross-loadings. It should be noted, however, that our results cannot speak to other subjective alcohol effects, such as craving, arousing-yet-aversive effects, or negatively reinforcing effects (Bujarski et al., 2015; Morean et al., 2013).
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Considered with recent findings, our results have implications for the potential role of subjective alcohol responses in motivating alcohol use. Specifically, greater stimulation and lower sedation predict increased AUD symptoms (King et al., 2014), but neither stimulation nor sedation appears to change substantially over time among young adults, even with heavier drinking (King, Hasin, O’Connor, McNamara, & Cao, 2015, May 14). As discussed by King and colleagues (2015, May 14), this pattern is most consistent with the early stage of an allostasis model of drinking motivation, during which positive reinforcement (e.g., greater stimulation) drives use. That our findings continue to support the stimulation vs. sedation distinction underscores how alcohol use can be differentially positively reinforcing to some early adult drinkers. An important question, therefore, is whether continued drinking might change alcohol responses or whether responses reflect truly stable, perhaps genetically based, individual differences (Schuckit, 2009).
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Our second substantial finding was a small-to-moderate association between subjective stimulation—but not sedation—and alcohol-related disinhibition when controlling sex. To our knowledge, this study is the first to report such an association, although it complements a recent finding of a strong association between stimulation and risk-taking choices in an IV-alcohol-administration study (Gilman et al., 2012). These results raise the possibility that alcohol sensitivity may have implications for the immediate behavioral consequences of intoxication. Specifically, if greater stimulation responders also experience greater alcoholrelated disinhibition, they may be more likely to engage in other dangerous behaviors while intoxicated. One diary study of real-world drinking, for example, found that individuals who reported stronger subjective intoxication magnitude were more likely to engage in aggressive behavior (Quinn, Stappenbeck, & Fromme, 2013). If the link between stimulation and disinhibition can be replicated, further study will be needed to determine the underlying mechanism. The euphoric experience produced by stimulant-like effects may result in an impelling disinhibition of behavior. Alternatively, disinhibition itself might produce positive sensations, or the association may reflect an underlying broader sensitivity to alcohol effects or unmeasured confounding.
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It is also important to note that the statistical significance of the association between alcohol-related stimulation and disinhibition apparently required controlling sex. At nearly half a standard deviation in magnitude, the sex difference in alcohol-condition inhibitory failures (controlling placebo inhibitory failures) was not trivial. This pattern matches previous studies of the Cued Go/No-Go Task, which have found that men make more inhibitory failures under alcohol only (Fillmore & Weafer, 2004) and under both alcohol and placebo (Weafer & Fillmore, 2012). It is not yet clear whether this pattern reflects a genuine sex difference or task-specific factors (see Weafer & de Wit, 2014 for a review), and the generalizability of our results to other tasks, other BAC levels, and real-world behavior is uncertain. For example, Weafer and Fillmore (2012) found no association between Cued Go/No-Go Task response inhibition and a measure of attentional inhibition, suggesting that alcohol may differentially impact various aspects of inhibitory control. Consequently, the stimulation-disinhibition association should be understood in light of outstanding questions regarding generalizability and sex differences in disinhibition.
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Three additional methodological considerations are relevant to the results of this study. First, to the extent that subjective responses are thought to influence drinking motivation, it may be valuable to ensure that laboratory assessment captures responses as they would be experienced in real-world drinking and to differentiate them from placebo responses. Our simulated-bar approach was intended to increase ecological validity while enhancing placebo efficacy, and the results held in analyses limited to participants who reported a positive placebo manipulation. There are trade-offs between ecological validity and internal validity, however, and a simulated bar cannot perfectly reproduce a real-world drinking setting. The concomitant limitations of the present design included decreased control over BrAC levels and the drinking environment, some participant attrition, and differing numbers of participants and, in select cases, differing members (including confederates) in laboratory session groups. It is also important to note that, on its own, this study cannot examine the value of enhanced ecological validity. That our approach complements prior studies with more experimental control but less adjustment for expectancies (e.g., Bujarski et al., 2015), however, increases confidence in the accumulated findings.
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Second, our sample was not small for an alcohol challenge but was modest for a study of individual differences. Its size may have limited power in the structural equation models, and it constrained our ability to test sex differences in the associations of interest. Given that our findings suggested an important role for sex differences in response inhibition, these should be examined in future research. Relatedly, we targeted recent binge drinkers for recruitment to ensure that we sampled from those at risk of alcohol-related consequences, and the included sample was generally moderate in typical alcohol consumption. Our conclusions may not generalize to other populations. Third, the use of difference scores to adjust for placebo responses may have reduced the reliabilities of the subjective measures. This reduction should have been attenuated by the latent measurement approach. Nevertheless, it highlights the importance of identifying the ideal method of accounting for placebo responses in measuring alcohol’s subjective effects.
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In spite of these limitations, our findings have several implications for future research. First, high-quality measurement of subjective responses to alcohol is crucial, and our findings add to previous studies in indicating that subjective stimulation and sedation can and should be differentiated. Second, this study is the first, to our knowledge, to report an association between subjective stimulation and alcohol-related disinhibition. Although this association was not strong, it was robust across alternative tests when controlling sex. If it is replicated, it would suggest that alcohol-related stimulation is not merely a set of hedonically rewarding sensations but may also relate to differences in intoxicated behavior as well.
Supplementary Material Refer to Web version on PubMed Central for supplementary material.
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Author Manuscript Author Manuscript Author Manuscript Figure 1.
Cued Go/No-Go Task natural-log transformed proportions of inhibitory failures (Panel A) and reaction times (Panel B). Bars are standard errors.
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Figure 2.
Measurement model of subjective alcohol responses. Values are standardized loadings and correlations. Residual variances not shown. Solid paths indicate p < .05; dashed path indicates p = .08. Parenthetical values are (unstandardized) model constraints.
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Figure 3.
Standardized parameter estimates for associations between subjective alcohol responses and alcohol-related inhibitory failures. Sex estimates are standardized mean differences. Solid lines indicate p < .05; dashed lines indicate p > .05. Parenthetical values are (unstandardized) model constraints. Measurement model for stimulation and sedation is not shown (see Figure 2).
Author Manuscript Author Manuscript Exp Clin Psychopharmacol. Author manuscript; available in PMC 2016 April 01.
Author Manuscript 0 – 28 0 – 15 0 – 17
Maximum drinks
Frequency of binge
Frequency of intoxication
6.29
4.82
8.56
41.37
8.05
M
3.53
3.46
4.37
27.15
3.18
SD
4.95
4.63
11.41
50.48
8.83
M 2.84
SD
3.40
3.44
5.67
33.79
Male
.39
.05
−.56
−.30
−.26
Cohen’s d
n = 76 for Timeline Follow-Back. Data excluded for incompleteness (4 participants) or improbable values (total standard drinks ≥ 171; 2 participants).
a
Note. Positive Cohen’s d values indicate female > male. Bolded rows indicate p < .05. AUDIT (Alcohol Use Disorders Identification Test) scores are from screening and include re-tested scores when more than one month elapsed between screening and the first laboratory session.
0 – 139
3 – 15
Total standard drinks
30-day Timeline Follow-Backa
AUDIT
Observed Range
Female
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Variable
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Typical Alcohol Use Summary Statistics
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Table 1 Quinn and Fromme Page 18
Exp Clin Psychopharmacol. Author manuscript; available in PMC 2016 April 01.
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Strictly positive scaled chi-square difference test (Satorra & Bentler, 2010).
b
Satorra-Bentler scaled chi-square difference test.
p < .05.
a
*
Note. Bolded model was selected for subsequent analyses.
Model 3 plus POMS Energetic cross-loading
4
Two factors (Stimulation and Sedation)
2
Model 2 plus POMS Intoxicated and BAES Sedation cross-loadings
Single factor
1
3
Description
Model
.81
75.05* (19)
12.29 (16)
1.00
.96
.16
262.72* (20)
29.07* (17)
CFI
χ2 (df)
.00
.09
.19
.39
RMSEA
2782.79
2798.08
2837.89
2946.29
AIC
2850.18
2863.06
2898.06
3004.05
BIC
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Measurement Model Fit Statistics
16.41* (1)b
136.43* (2)a
--
--
Δχ2 (df)
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Table 2 Quinn and Fromme Page 19
Exp Clin Psychopharmacol. Author manuscript; available in PMC 2016 April 01.
