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. Author manuscript; available in PMC: 2016 Jun 1.
Published in final edited form as: Exp Clin Psychopharmacol. 2015 Jun;23(3):159–167. doi: 10.1037/pha0000018

Brief and Extended Alcohol Cue Exposure Effects on Craving and Attentional Bias

Jason J Ramirez 1, Peter M Monti 1, Ruth M Colwill 1
PMCID: PMC4460597  NIHMSID: NIHMS689178  PMID: 26053323

Abstract

Past research has shown that underage college student drinkers (UCSDs) report increased subjective craving and exhibit stronger attentional biases to alcohol following alcohol cue exposure. To date, less research has examined whether momentary decreases in alcohol craving are associated with reductions in attentional bias. One experimental manipulation that has been used to produce within-session decreases in alcohol craving is to extend the duration of laboratory-based alcohol cue exposure protocols. The aim of this study was to examine the effects of both brief and extended alcohol cue exposure on subjective craving and attentional bias among UCSDs. Eighty participants were randomized either to a group that received a short in vivo alcohol cue exposure period (Group Short Exposure [SE], two 3-min blocks) or to a group that received a long exposure period (Group Long Exposure [LE], six 3-min blocks). Both groups completed a visual probe task before and after cue exposure to assess changes in attentional bias. Analyses revealed no group differences in mean craving or mean attentional bias before or after cue exposure. Further, exploratory analyses revealed no sex differences in our measures of craving or attentional bias. For Group LE, but not Group SE, within-session changes in craving positively predicted within-session changes in attentional bias. However, further analyses revealed that this relationship was significant only for female participants in the LE Group. Implications for treatments that aim to reduce craving or attentional bias are discussed.

Keywords: Attentional Bias, Craving, Cue Reactivity, Underage College Student Drinking

Historically, a number of theories of addiction have proposed that exposure to cues associated with drug consumption results in a range of effects that maintain ongoing drug use or lead to relapse (e.g., Robinson & Berridge, 1993; Wikler, 1948). More recently, some theoretical models posit that these cue exposure effects include increases in subjective craving and changes in automatic cognitive processes thought to motivate drug use (Kavanagh, Andrade, & May, 2005; Ryan, 2002). One model in particular has specified that a reciprocal relationship between attentional bias to drug cues and subjective drug craving underlies drug-seeking and consummatory behaviors (Franken, 2003). This model asserts that, upon attentional processing of a drug cue, feelings of subjective craving are activated, which in turn strengthen an attentional bias towards drug cues.

Franken’s attentional bias model (2003) is supported by a meta-analysis demonstrating a small but significant positive relationship between subjective craving and attentional bias to drug cues (Field, Munafo, & Franken, 2009). Also consistent with Franken’s model, the relationship between these constructs is stronger among studies that include experimental manipulations to elicit craving. For example, studies have demonstrated increases in attentional bias to alcohol when measured in alcohol-related contexts (Cox, Yeates, & Regan, 1999), and increases in both attentional bias to alcohol and subjective craving following in vivo alcohol cue exposure with evidence of a positive relationship between these constructs when they simultaneously increase (Ramirez, Monti, & Colwill, 2014). Surprisingly few studies, however, have examined this positive relationship when craving or attentional bias are reduced. Simultaneous reductions in craving and attentional bias have been demonstrated for nicotine and food (Oh & Taylor, 2013, 2014; Szasz, Szentagotai, & Hofmann, 2012; Van Rensburg, Taylor, & Hodgson, 2009) and for alcohol (Rose, Brown, Field, & Hogarth, 2013; Taylor, Oh, & Cullen, 2013). However, only one of the alcohol studies used correlational procedures to determine the predictive relationship between the two constructs and that study found no significant relationship (Taylor et al., 2013). To our knowledge, no additional research has specifically examined the relationship between alcohol craving and attentional bias when manipulations aim to reduce these constructs.

Whether a reduction in alcohol craving is associated with a weaker attentional bias to alcohol cues has significant implications for interventions that aim to reduce either of these constructs. These include treatments that specifically aim to reduce alcohol craving like cue exposure therapies (Drummond & Glautier, 1994), and also pharmacotherapies like naltrexone that have been shown to simultaneously reduce craving and drinking outcomes (Miranda et al., 2013). Further, attentional retraining treatments have been recently adapted as substance abuse interventions (e.g., Attentional Bias Modification Training, ABM; Alcohol Attention-Control Training Program, AACTP). These treatments utilize experimental paradigms typically used to assess attentional bias, and modify them in order to train biases away from alcohol. Studies have found that such treatments are effective at decreasing attentional bias towards alcohol cues (Schoenmakers et al., 2010), and that harmful drinkers exhibit post-training reductions in alcohol consumption with improvements still evident at a 3-month follow-up (Fadardi & Cox, 2009). However there is also evidence to suggest that treatment effects on reducing attentional bias may be stimulus-specific, and subsequent effects on decreasing alcohol consumption have not always been replicated (for review, see Christiansen, Schoenmakers, & Field, 2015). Given that treatments aim to reduce either craving or attentional bias to alcohol, whether or not these two constructs are positively associated is of critical importance. For example, if a treatment successfully reduces craving but not attentional bias to alcohol, then an individual would continue to be at risk for relapse to the extent that automatic cognitive processes motivate alcohol consumption in the absence of craving as proposed by some theories (e.g., Tiffany & Conklin, 2000). This would suggest that treatments combining both craving and attentional bias modifications would be more effective in reducing the risk of relapse. However, if a reduction in craving is accompanied by a weaker attentional bias, then treatments that completely eliminate craving might not benefit from the addition of an attentional bias modification.

One experimental manipulation that has led to within-subject reductions in alcohol craving is to extend the duration of an alcohol cue exposure paradigm. When cue exposure procedures are carried out over an extended period of time, participants have been shown to report initial rises in alcohol craving followed by decreases in craving (Kamboj et al., 2011), with one study in particular demonstrating peak craving after 6 minutes of alcohol cue exposure followed by gradual but significant decreases in craving (Monti et al., 1993). These decreases from peak craving resemble the theoretical underpinnings of cue exposure therapies (Glautier & Drummond, 1994). Such therapies are based on extinction learning principles in which repeated presentations of conditioned stimuli (alcohol cues) without allowing subsequent alcohol consumption result in the extinction of a conditioned response (subjective craving).

The current study implemented a between-groups design to examine the effects of a brief or an extended alcohol cue exposure period on craving and attentional bias while also assessing the relationship between these two constructs. We hypothesized that, as compared to participants receiving a short alcohol cue exposure period, participants receiving extended alcohol cue exposure would report less subjective craving, and would exhibit a weaker attentional bias to alcohol cues following cue exposure. Furthermore, within-participant changes in craving were hypothesized to positively predict within-participant changes in attentional bias to alcohol cues for each group consistent with past research demonstrating a positive relationship between these two constructs (Field et al., 2009). We also explored potential sex differences in craving and attentional bias given conflicting findings regarding sex differences in craving (Seo et al., 2011; Willner, Field, Pitts, & Reeve, 1998) and a general paucity of research examining sex differences in attentional bias to alcohol. All participants in the study were underage college student drinkers (UCSDs). UCSDs are a population of drinkers who report frequent bouts of heavy episodic drinking compared to adult drinkers and report less intention to drink responsibly than college students of legal drinking age (Barry, Stellefson, & Woolsey, 2014; Wechsler, Lee, Nelson, & Kuo, 2002). Moreover, UCSDs exhibit stronger attentional biases to alcohol and report increases in craving following exposure to in vivo alcohol cues (Ramirez et al., 2014).

Method

Participants

Eighty underage college student drinkers (46 female and 34 male) were recruited to participate in this study. Students, 18 to 20 years of age, were eligible to participate if they drank at least one beer on a weekly basis over the past month. Individuals were ineligible to participate if they were currently seeking treatment for alcohol problems or if they had received treatment in the past 30 days. The Brown University institutional review board approved this study and participants received either course credit or $15.00 for their participation.

Procedures

General Procedure

Participants were assigned to one of two groups matched for gender that differed on the basis of alcohol cue exposure duration in the cue reactivity procedure. All participants first provided demographic information. Next, participants completed baseline measures of craving and a baseline visual probe task (Time 1) followed by the cue reactivity procedure and a final visual probe task (Time 2) to assess changes in attentional bias towards alcohol. For the cue reactivity procedure, Group Long Exposure (LE) received an extended alcohol cue exposure period, whereas Group Short Exposure (SE) received a brief alcohol cue exposure period. Both groups received the same visual probe tasks, which included the same stimuli. Finally, additional individual difference measures were collected at the end of the session given the possibility that the nature of these assessments (e.g., recalling drinking episodes) might affect craving and attentional bias to alcohol. All participants signed informed consent before beginning the study.

Cue Reactivity (CR)

Participants were asked to abstain from drinking the morning of the study. Immediately prior to starting the experiment, participants were breath-tested to ensure a Blood Alcohol Content (BAC) of 0.00 and all complied. Participants then received standardized instructions about the cue reactivity assessments while becoming acclimated to the laboratory. Prior to cue exposure, participants indicated which one of four beer options they drank most frequently and whether they were more likely to drink that beer from a cup or glass. The beer options covered the spectrum of beer choices popular among UCSDs and matched the beer preferences of the study participants.

Sessions began with a 3-minute relaxation period, in which participants were instructed to sit quietly. Next participants were given six consecutive 3-minute blocks of beverage exposure. For Group LE, each block was identical; participants were instructed to hold and smell a cup or glass of beer for 3 minutes, with the type of beer and container matching that which the participant indicated as having the most experience with outside of the laboratory. For Group SE, participants were instructed to hold and smell a glass of water for 3 minutes during each of the first four blocks to control for exposure to a potable liquid. For the last two blocks, participants in Group SE were exposed to beer in the same manner as participants in Group LE. For all blocks of beverage exposure, participants were asked to smell the beverage for 5 seconds each time a tone sounded with 13 tones delivered during each 3-minute block of time with variable intervals. Following each 3-minute block of beverage exposure, participants completed measures of subjective craving. Subjective craving was also measured prior to the baseline visual probe task and represents the baseline measure of craving (Time 1); subjective craving reported after the sixth and final block represents the final craving report (Time 2). Compliance was monitored by observation of participants through a one-way mirror and all participants complied with the study protocol.

Visual Probe

All participants completed visual probe tasks to assess attentional bias to alcohol cues at baseline immediately before the CR procedure (Time l) and immediately after the CR procedure (Time 2). There was no difference in the visual probe task procedure at these two timepoints. Participants were seated in front of a laptop computer and instructed to identify the location (left or right) of a single probe stimulus, which always followed a bilateral presentation of a pair of pictorial stimuli. The stimulus set consisted of ten alcohol pictures of solitary alcoholic beverages matched with ten neutral pictures of similarly sized non-alcoholic beverages. These stimuli have been used in previous research to assess attentional bias to alcohol (Miller & Fillmore, 2010). The task began with six practice trials, which only included neutral picture pairs. Following a short break, two buffer trials were presented with neutral pairs, followed by 80 critical trials, in which the ten alcohol-neutral picture pairs were presented. All trials began with a fixation cross presented in the center of the screen for 500 ms, immediately followed by the bilateral presentation of a picture pair for 1000 ms, with pictures positioned to the left and right of the previously presented fixation cross. After picture offset, the visual probe stimulus, an “X” in red font, was presented on either the left or right side of a black screen where either the alcohol or neutral picture had been presented. Upon presentation of the probe stimulus, participants were instructed to quickly press one of two response keys (“A” for left and “L” for right) on a keyboard indicating the location of the probe. There was an intertrial interval of 500 ms between each response and the following trial. Throughout the critical trials, each alcohol-neutral picture pair was presented eight times, with the alcohol-related picture being presented four times on the left and four times on the right of the screen. Probes replaced alcohol-related and neutral pictures with equal frequency, and appeared on each side of the screen with equal frequency. Attentional bias to alcohol cues was indicated by shorter (i.e., faster) reaction times to probes replacing alcohol-related pictures than to those replacing neutral pictures. Internal consistency of the visual probe task was examined by computing the split-half reliability (i.e., correlation between attentional bias scores for each half) for the entire sample at T1. The split-half reliability for the visual probe task at T1 was r = .04, which is consistent with past research demonstrating poor internal consistency (Schmukle, 2005).

Measures

Alcohol Craving

Before completing the baseline visual probe task, and after each CR block of beverage exposure, participants completed the Alcohol Urge Questionnaire (AUQ; Bohn, Krahn & Staehler, 1995). The AUQ is an 8-item measure of momentary craving with items rated on a scale from 1 to 7, with total scores on the questionnaire ranging from 8 to 56 and higher scores representing greater momentary craving.

Alcohol Use

All measures of alcohol use were collected upon completion of the visual probe task at the end of experimental sessions. Baseline (pre-study) drinking levels were assessed using the 90-day timeline follow-back interview (TLFB; Sobell & Sobell 1992). The TLFB was scored for the percentage of drinking days in the last 90 days, and the number of standardized drinks per drinking day. Two separate measures assessed alcohol use and alcohol-related problems. First, participants completed the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989); a 23-item measure for assessing adolescent problem drinking and negative consequences that result from drinking including problems at school/work, interpersonal difficulties, emotional troubles, and signs of tolerance. Items were rated on a scale of ‘0 = never’ to ‘4 = more than 10 times,’ indicating the number of occurrences of alcohol-related problems in the past three months. Second, the Alcohol Use Disorders Identification Test (AUDIT; Babor et al., 2001) was administered to gauge hazardous and harmful patterns of alcohol use. The AUDIT consists of 10 short questions based on three domains; hazardous alcohol use, dependence symptoms, and harmful alcohol use. The items were also rated on 0–4 scales with a total possible score of 40. A subset of participants (n = 38) also responded to an alcohol use history question indicating the number of years since first consuming alcohol.

Data Analysis

Descriptives and Bivariate Correlations

Group comparisons on demographic and other individual differences measures were conducted using independent samples t-tests for continuous variables and chi-squared tests for categorical variables. Bivariate correlations were run to explore the relationships between individual difference measures of drinking and the primary outcome measures of craving and attentional bias (at baseline before cue exposure).

Craving for alcohol

Total AUQ scores were used as the measure of craving in all analyses. To examine group differences in craving at Time 1 and Time 2, total AUQ scores were analyzed using a mixed-design 2 x 2 analysis of variance (ANOVA), with Group (2, Group SE, Group LE) as the between-subjects factor, and Time [2, Time 1 (Baseline), Time 2 (post CR Block 6)] as the within-subjects factor. To assess group differences in craving at Time 1 and Time 2 separately, craving was compared with independent group t-tests at each of these timepoints.

Attentional bias

For the visual probe task, trials with errors (incorrect identification of probe side) were removed (1.2%) and median reaction times were determined for each participant’s responses to probes that replaced alcohol cues and probes that replaced neutral cues. Median scores were used due to positive skewness among the reaction times. Attentional bias scores were calculated for each participant by subtracting the median reaction time to probes that replaced alcohol cues from the median reaction time to probes that replaced neutral cues. Attentional bias scores greater than zero indicate an attentional bias to alcohol cues. To examine group differences, attentional bias scores were analyzed using a mixed-design 2 x 2 analysis of variance (ANOVA), with Group (2, Group SE, Group LE) as the between-subjects factor, and Time [2, Time 1 (Baseline), Time 2 (post CR)] as the within-subjects factor. Separate independent group t-tests were run to compare group differences in attentional bias difference scores (Time 2 – Time 1).

Craving and attentional bias

Linear regression analyses were run to examine the relationship between craving and attentional bias. Specifically, for each group, within-subject changes in craving scores from Time 1 to Time 2 were used to predict within-subject changes in attentional biases from Time 1 to Time 2, as recommended to test for within-subjects mediation (Judd, Kenny, & McClelland, 2001).

Sex differences in craving and attentional bias

Independent group t-tests were run to examine sex differences in craving and attentional bias at T1 (prior to group manipulations) for the entire sample. Independent group t-tests were also run to examine sex differences in craving and attentional bias change scores (T2 – T1) within each group. Finally, regression analyses examining the relationship between craving and attentional bias were also run separately for males and females within each group.

Results

Descriptives and bivariate correlations

Table 1 shows descriptive information for the sample separated by Group SE and Group LE. There were no pairwise differences between any groups with regard to demographic or individual difference measures of alcohol-related problems and measures of baseline drinking (all p’s > .10).

Table 1.

Baseline Participant Characteristics by Group: Percentage or Mean (With Standard Deviation in Parentheses)

Variable Group SE (n = 40) Group LE (n = 40) Overall (N = 80)
Age 19.2 (0.7) 19.2 (0.9) 19.1 (0.8)
Gender (Female) 57.5 57.5 57.5
Racea
 Caucasian 57.5 57.5 57.5
 African-American 12.5 7.5 10.0
 Hispanic 5.0 7.5 6.3
 Asian 25.0 27.5 26.3
RAPI 5.7 (3.3) 6.4 (5.6) 6.1 (4.5)
AUDIT 10.0 (4.8) 9.4 (4.3) 9.7 (4.6)
Percent Drinking Daysb 23.7 (11.4) 22.9 (9.7) 23.3 (10.5)
Percent Heavy Drinking Daysb 9.7 (9.0) 10.1 (9.3) 9.9 (9.1)
Drinks Per Drinking Dayb 3.8 (1.8) 3.6 (1.6) 3.7 (1.7)
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Note.

a

Races were not considered mutually exclusive

b

Derived from the 90-day Timeline Follow-Back interview conducted at baseline; SE = Short Exposure; LE = Long Exposure; AUDIT = Alcohol Use Disorders Identification Test; RAPI = Rutgers Alcohol Problem Index

Bivariate correlations among individual predictor variables and the main outcome variables at baseline are presented in Table 2. Significant correlations were present between all measures of alcohol-related problems and measures of alcohol use in the past 90 days (all p’s < .01). Craving at baseline was significantly positively correlated with scores reported on the RAPI (r = .36, p = .001) and AUDIT (r = .42, p < .001), percent of drinking days in the past ninety days (r = .23, p = .03), standard drinks consumed per drinking day (r = .28, p = .011), and the number of years since first consuming alcohol (r = .43, p = .008). The percent of drinking days in the past ninety days also significantly correlated with the number of years since first consuming alcohol (r = .40, p = .012). Attentional bias towards alcohol cues during the baseline visual probe task was not significantly correlated with craving at baseline, measures of alcohol-related problems or baseline alcohol use (all p’s > .05).

Table 2.

Correlation Matrix for Baseline Outcome and Predictor Variables

Measure 1 2 3 4 5 6 7
1. Craving (Baseline) ____
2. Attentional Bias (Baseline) .09 ____
3. RAPI .36** .05 ____
4. AUDIT .42** .08 .63** ____
5. Drinking Days (%)a .24* .21 .38** .52** ____
6. Drinks Per Drinking Daya .28* .02 .46** .62** .33** ____
7. Years Since First Useb .43** .01 .17 .25 .40* .28 ____
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Note. Craving reflects subjective ratings scored at Time 1 (Baseline) prior to cue exposure. Attentional bias reflects the bias of median reaction times during visual probe task at Time 1 (Baseline) prior to cue exposure; RAPI = Rutgers Alcohol Problem Index (higher scores indicate greater severity of alcohol-related problems); AUDIT = Alcohol Use Disorders Identification Test (higher scores indicate greater severity of alcohol-related problems);

a

derived from the 90-day Timeline Follow-Back interview conducted during first session;

b

collected only from subsample (n = 38);

*

p < .05,

**

p < .01.

Craving for alcohol

Figure 1 presents each group’s mean levels of subjective craving (total AUQ scores) measured at Time 1 and after each of the six 3-min blocks of cue exposure. Total AUQ scores were analyzed using a mixed-design 2 x 2 analysis of variance (ANOVA), with Group (2, Group SE, Group LE) as the between-subjects factor, and Time [2, Time 1 (Baseline), Time 2 (post CR Block 6)] as the within-subjects factor. There was a main effect of time [F(1,78) = 36.43, p < .001] such that participants reported greater craving at Time 2 than at Time 1. There was no main effect of Group [F(1,78) = 0.66, p = .42], and no significant Time x Group interaction [F(1,78) = .03, p = .87]. Independent group t tests indicated that the groups did not report significantly different craving at Time 1 [t(78) = 1.07, p = .29] or Time 2 [t(78) = .53, p = .60].

Figure 1.

Figure 1

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Total AUQ scores at Time 1 (Baseline), after each block of the cue reactivity protocol including Time 2 (after Block 6), separated by Group SE with dotted lines and square markers and Group LE in solid lines and circle markers. Group SE received water cue exposure for Blocks 1–4, whereas Group LE received alcohol cue exposure for Blocks 1–4. Both groups received alcohol cue exposure for Blocks 5–6. Total AUQ scores fall within a possible range of 8–56. Values are group means ± SEM.

Attentional bias

Figure 2 presents the group mean attentional bias scores at Time 1 and Time 2. Attentional bias scores were analyzed using a mixed-design 2 x 2 analysis of variance (ANOVA), with experimental group (2, Group SE, Group LE) as the between-subjects factor, and time [2, Time 1 (Baseline), Time 2 (post CR)] as the within-subjects factor. There was a main effect of time [F(1,78) = 5.10, p = .027] such that participants exhibited significant increases in attentional bias from Time 1 to Time 2. There was no main effect of Group [F(1,78) = 0.73, p = .40] and no significant Time x Group interaction [F(1,78) = 0.44, p = .51]. Independent group t tests show that Group SE and Group LE did not differ from each other with regards to changes in attentional bias from Time 1 to Time 2 [t(78) = 0.66, p = .51] suggesting that both groups experienced similar within-session increases in attentional bias.

Figure 2.

Figure 2

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Attentional bias scores at Time 1 (Baseline) in light gray bars and at Time 2 (after CR procedure) in dark gray bars, shown separately for Group SE and Group LE. Values are group means of attentional bias scores ± SEM.

Craving and attentional bias

Linear regression analyses were run to examine if within-participant changes in craving positively predicted within-participant changes in attentional bias from Time 1 to Time 2 for each group separately. For Group SE, craving difference scores did not significantly predict attentional bias change scores between Time 1 and Time 2 (r = .07, r2 = .01, p = .68). For Group LE, craving differences scores significantly predicted attentional bias change scores, such that greater increases in craving from Time 1 to Time 2 predicted greater increases in attentional bias from Time 1 to Time 2 (r = .57, r2 = .33, p < .001). Exploratory analyses revealed a single outlier that had an influential effect on the regression analysis for Group LE. When this outlier was removed, craving differences marginally predicted attentional bias scores in the same direction for Group LE (r = .31, r2 = .10, p = .056).

Sex Differences

Independent group t tests were run to explore the possibility of sex differences in craving and attentional bias at baseline for the entire sample and within each group (see Table 3). There were no sex differences in craving at T1 [t(78) = 0.30, p = .77], or attentional bias at T1 [t(78) = 1.73, p = .09], which represent baseline measures of our constructs unaffected by group manipulations. There were also no significant sex differences in craving change scores (T2 – T1) within Group SE [t(38) = 1.05, p = .30] or within Group LE [t(38) = 0.01, p = .99]. There were also no significant sex differences in attentional bias change scores (T2 – T1) within Group SE [t(38) = 1.07, p = .29] or within Group LE [t(38) = 0.59, p = .56]. Finally, regression analyses to examine the relationship between craving and attentional bias were conducted separately for males and females within each group. Changes in craving did not significantly predict changes in attentional bias for either males (r = .08, r2 = .01, p = .77) or females (r = .01, r2 = .00, p = .99) in Group SE. Changes in craving positively predicted changes in attentional bias for females (r = .70, r2 = .49, p < .001), but not for males (r = .17, r2 = .03, p = .51), in Group LE. Further, the effect for females was also significant upon removal of the previously noted outlier (r = .43, r2 = .19, p = .046).

Table 3.

Sex Comparisons in Demographic Variables and Craving and Attentional Bias Indices: Percentage or Mean (With Standard Deviation in Parentheses)

Variable Males (N = 34) Females (N = 46) P value
Age 19.1 (0.8) 19.2 (0.8) .59
RAPI 5.8 (4.5) 6.2 (4.6) .67
AUDIT 9.2 (5.0) 9.9 (4.3) .34
Percent Drinking Daysa 22.6 (11.1) 23.8 (10.1) .61
Percent Heavy Drinking Daysa 9.2 (9.6) 10.5 (8.8) .54
Drinks Per Drinking Daya 4.0 (2.0) 3.5 (1.4) .24
T1 Craving (AUQ Score) 14.2 (6.4) 14.6 (6.8) .77
T1 Attentional Bias (ms) −0.1 (15.3) 6.1 (19.5) .09
Group SE (N = 17) (N = 23)
 Craving Change 4.1 (6.1) 6.7 (9.2) .30
 Attentional Bias Change (ms) 11.5 (19.9) 4.0 (23.4) .29
Group LE (N = 17) (N = 23)
 Craving Change 5.9 (7.4) 5.9 (10.2) .99
 Attentional Bias Change (ms) 1.5 (15.5) 5.8 (26.3) .56
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Note.

a

Derived from the 90-day Timeline Follow-Back interview conducted at baseline; SE = Short Exposure; LE = Long Exposure; AUDIT = Alcohol Use Disorders Identification Test; RAPI = Rutgers Alcohol Problem Index; AUQ = Alcohol Urge Questionnaire

Discussion

The relationship between alcohol craving and attentional bias to alcohol cues was examined among UCSDs, using brief and extended cue exposure paradigms and a visual probe task. Five important findings emerged. First, exposure to in vivo alcohol cues increased subjective reports of craving for the study sample. Second, the effect of extended alcohol cue exposure on craving did not replicate previous findings (Kamboj et al., 2011; Monti et al., 1993), as a group that received an extended alcohol cue exposure period (Group LE) reported similar levels of mean craving to a group that received a brief alcohol cue exposure period (Group SE). Third, exposure to alcohol cues also increased attentional bias across the entire sample. Fourth, within-session changes in craving positively predicted within-session changes in attentional bias for Group LE, but not Group SE. Fifth, exploratory analyses revealed no sex differences with regards craving and attentional bias to alcohol at baseline or after cue exposure, however the positive relationship between craving and attentional bias was only significant for females in Group LE.

Study participants’ reports of increased subjective craving following exposure to in vivo alcohol cues is consistent with past alcohol cue reactivity research that included similarly aged drinkers (Ramirez & Miranda, 2014; Ramirez et al., 2014; Thomas, Drobes, & Deas, 2005). Further, mean levels of craving before and after cue exposure were similar to craving levels reported by non-treatment-seeking alcoholics in a study using a similar alcohol cue exposure but in combination with a stress manipulation (Thomas, Randall, Brady, See, & Drobes, 2011). These robust effects indicate that alcohol cues are potent elicitors of craving among UCSDs. To the degree that cue–elicited craving reflects a learned mechanism, the results suggest that craving responses develop among underage drinkers with presumably less drinking experience than their adult counterparts. However, baseline rates of craving positively correlated with the number of years since participants first consumed alcohol suggesting that craving in general is greater for those with longer histories of alcohol use. Baseline craving in this sample also significantly correlated with two measures of alcohol-related problems and baseline rates of drinking such that those with more alcohol-related problems and greater reports of baseline drinking reported higher levels of craving. These findings are in line with previous adolescent research in which teens with more severe alcohol-related problems or greater drinking histories reported greater subjective cue-elicited craving for alcohol (Curtin, Barnett, Colby, Rohsenow, & Monti, 2005; Thomas et al., 2005) and experienced greater neural activation in reward areas of the brain (Tapert et al., 2003).

The effects of extended alcohol cue exposure on craving did not align with the experimental hypotheses. Group LE reported similar mean levels of craving to Group SE at Time 2 despite an alcohol cue exposure period that was three times longer than Group SE, and with total alcohol cue exposure durations (six 3-min blocks) comparable to past research that demonstrated significant within-session reductions in craving (four 5-min blocks, Kamboj et al., 2011; one 3-min block and one 15-min block, Monti et al., 1993). It is important to note that the current study’s sample of UCSDs differs from these previous studies which included older, heavier drinkers (Kamboj et al., 2011) and alcoholic men hospitalized for detoxification (Monti et al., 1993). Further research will be necessary to determine if the profile of craving response to extended cue exposure observed in the current study is unique to UCSDs, and if longer exposure periods would result in within-session reductions in craving for UCSDs. It may also be worth considering a possible classification of craving responders (e.g., non-responders vs. responders) for UCSDs to see if individual differences in craving response are predictive of alcohol-related problems or accounted for by personality traits in this population (Papachristou et al., 2013; Szegedi et al., 2000)

The effects of alcohol cue exposure on attentional bias paralleled the effects on craving. That is, exposure to alcohol cues increased attentional bias towards the alcohol stimuli in the visual probe task for both Groups SE and LE. This finding is consistent with past work in which increases in attentional bias were observed following manipulations that increase craving (Cox et al., 1999; Ramirez et al., 2014). It is also noteworthy that the current effects of alcohol cue exposure on attentional bias occurred in a sample of UCSDs that reported relatively low mean rates of alcohol consumption and related problems. Previous studies have demonstrated baseline attentional biases to alcohol among European college students who report a high level of alcohol-related problems or heavy drinking in comparison to those who report few alcohol-related problems or light drinking (Field, Mogg, Zetteler, & Bradley, 2004; Townshend & Duka, 2001). In line with recent conceptualizations of attentional bias as a phenomenon prone to fluctuations with motivational state (Field, Marhe, & Franken, 2014), the current findings suggest that UCSDs with modest rates of alcohol consumption may not display reliable attentional biases to alcohol at baseline, but are susceptible to changes in attentional bias that may influence alcohol-seeking and consumption behavior.

Another primary aim of the study was to examine the relationship between alcohol craving and attentional bias. Within-session differences in craving predicted within-session changes in attentional bias for Group LE lending some support to a theorized positive relationship between these constructs (Franken, 2003). Future research will need to include manipulations that successfully reduce subjective reports of craving to determine whether reductions in craving are associated with reductions in attentional bias. Although other studies have demonstrated simultaneous reductions in craving and attentional bias for alcohol (Rose et al., 2013; Taylor et al., 2013), nicotine (Oh & Taylor, 2014; Szasz et al., 2012; Van Rensburg et al., 2009) and food (Oh & Taylor, 2013; 2014), current evidence to support a positive relationship between alcohol craving and attentional bias for alcohol is limited to studies that report reciprocal increases in these constructs (Ramirez et al., 2014).

Despite the positive association between craving and attentional bias in Group LE, this association was not present in Group SE. The methodological difference between these groups may explain why this effect was not consistent between groups. One possibility is that the additional blocks of alcohol cue exposure for Group LE allowed participants’ craving to stabilize at Time 2 as compared to Group SE. Consistent with this possibility, the variance among T5–T6 change scores in craving for Group LE (SD = 2.77) was smaller than the variance among change scores for any other pair of blocks (SD range = 3.82–5.76), and was smaller than the variance among T5–T6 change scores for Group SE (SD = 3.12). Therefore, although mean levels of craving were similar between groups at Time 2, craving reported by Group SE may have fluctuated more after Time 2 when assessing attentional bias. If craving predicts attentional bias as posited by Franken (2003), we would expect the measurement of this relationship to be stronger when there is less fluctuation in craving, and when the gaps between assessments of craving and attentional bias are minimized. When possible, future studies are recommended to utilize eye-tracking technology to assess attentional bias in real time while simultaneously assessing craving. It is also noteworthy that the positive relationship between craving and attentional bias was significant for females, but not males, in Group LE. However, we caution interpretation of this finding given the limited power when analyzing sex differences within group (17 males, 23 females). Further, this sex difference was not apparent in Group SE, and previous studies have shown that craving for nicotine is correlated with attentional bias for males, although the relationship was not reported for females (Atwood, O’Sullivan, Leonards, Mackintosh, & Munafo, 2008). Future studies are recommended to investigate potential sex differences to help clarify the relationship between craving and attentional bias.

Other exploratory analyses found no sex differences with regard to changes in craving or attentional bias for either Groups SE or LE. Therefore, it does not appear as though males and females exhibit different responses to either short or long durations of alcohol cue exposure. There were also no observed sex differences in baseline craving and attentional bias for the entire sample at T1, which were assessed prior to differing group manipulations. Past research examining sex differences in craving have reported conflicting findings. One study found that drinking a half pint of low-alcohol beer increased craving in male, but not female, recreational drinkers although these male participants also reported greater pre-study rates of alcohol consumption than the female participants (Willner et al., 1998). A separate study found no sex differences in craving following cue and stress exposure among males and females who did not significantly differ in rates of alcohol consumption (Seo et al., 2011). In line with these findings, males and females in our current sample did not differ with regard to baseline rates of drinking or related problems. It is important for future studies to account for these sex differences, but the current study suggests that craving and attentional bias do not significantly differ between males and females who report similar amounts of alcohol consumption and related problems.

The current study lays the groundwork for further investigations of the relationship between drug craving and attentional bias to drug cues. From a practical, therapeutic standpoint, it will be important to assess whether the observed cue exposure effects on craving and attentional bias can be generalized from the study sample of UCSDs to other drinking populations such as alcohol-dependent individuals or younger adolescent drinkers. This is especially important given the low drinking eligibility criteria for the current study, which resulted in a sample of college students with relatively low mean levels of drinking and related problems. From a theoretical perspective, it will be important to confirm that the increases in attentional bias were the direct result of changes in craving that occurred during cue reactivity. To this end, both attentional bias and craving should be measured periodically during cue reactivity. Of course, the extent to which these constructs are linked also has implications for alcohol use disorder treatments that currently aim to reduce either craving or attentional bias but not both. Thus, if craving and attentional bias independently motivate problematic alcohol use, then modifying only one of these constructs may leave patients at risk for relapse.

In summary, in vivo alcohol cue exposure increased subjective craving and strengthened attentional biases to alcohol cues presented in a visual probe task. Contrary to hypotheses, participants who received extended alcohol cue exposure did not report less subjective craving or demonstrate weaker attentional bias to alcohol as compared to participants who received a short alcohol cue exposure period. Further, males and females did not differ with regards to craving or attentional bias, suggesting a lack of sex differences when males and females do not differ with regards to rates of alcohol consumption. Despite the lack of these differences in craving or attentional bias, within-session changes in craving predicted significant within-session changes for Group LE, an effect that was more pronounced for females. Although the effects of extended alcohol cue exposure were unanticipated, this experiment provides some support for a positive relationship between craving and attentional bias and highlights the importance of continued study of these constructs.

References

  1. Attwood AS, O’Sullivan H, Leonards U, Mackintosh B, Munafo MR. Attentional bias training and cue reactivity in cigarette smokers. Addiction. 2008;103:1875–1882. doi: 10.1111/j.1360-0443.2008.02335.x. [DOI] [PubMed] [Google Scholar]
  2. Babor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG. AUDIT: The Alcohol Use Disorders Identification Test: Guidelines for use in primary care. 2. Geneva: World Health Organisation; 2001. [Google Scholar]
  3. Barry AE, Stellefson ML, Woolsey CL. A comparison of the responsible drinking dimensions among underage and legal drinkers: examining differences in beliefs, motives, self-efficacy, barriers and intentions. Substance Abuse Treatment, Prevention, and Policy. 2014;9:1–5. doi: 10.1186/1747-597X-9-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bohn MJ, Krahn DD, Staehler BA. Development and initial validation of a measure of drinking urges in abstinent alcoholics. Alcoholism: Clinical and Experimental Research. 1995;19:600–606. doi: 10.1111/j.1530-0277.1995.tb01554.x. [DOI] [PubMed] [Google Scholar]
  5. Christiansen P, Schoenmakers TM, Field M. Less than meets the eye: Reappraising the clinical relevance of attentional bias in addiction. Addictive Behaviors. 2015;44:43–50. doi: 10.1016/j.addbeh.2014.10.005. [DOI] [PubMed] [Google Scholar]
  6. Cox WM, Yeates GN, Regan CM. Effects of alcohol cues on cognitive processing in heavy and light drinkers. Drug and Alcohol Dependence. 1999;55:85–89. doi: 10.1016/S0376-8716(98)00186-0. [DOI] [PubMed] [Google Scholar]
  7. Curtin JJ, Barnett NP, Colby SM, Rohsenow DJ, Monti PM. Cue reactivity in adolescents: Measurement of separate approach and avoidance reactions. Journal of Studies on Alcohol. 2005;66:332–343. doi: 10.15288/jsa.2005.66.332. [DOI] [PubMed] [Google Scholar]
  8. Drummond DC, Glautier S. A controlled trial of cue exposure treatment in alcohol dependence. Journal of Consulting and Clinical Psychology. 1994;62:809–817. doi: 10.1037/0022-006X.62.4.809. [DOI] [PubMed] [Google Scholar]
  9. Fadardi JS, Cox WM. Reversing the sequence: reducing alcohol consumption by overcoming alcohol attentional bias. Drug and Alcohol Dependence. 2009;101:137–145. doi: 10.1016/j.drugalcdep.2008.11.015. [DOI] [PubMed] [Google Scholar]
  10. Field M, Marhe R, Franken IH. The clinical relevance of attentional bias in substance use disorders. CNS Spectrums. 2014;19:225–230. doi: 10.1017/S1092852913000321. [DOI] [PubMed] [Google Scholar]
  11. Field M, Mogg K, Zetteler J, Bradley BP. Attentional biases for alcohol cues in heavy and light social drinkers: The roles of initial orienting and maintained attention. Psychopharmacology. 2004;176:88–93. doi: 10.1007/s00213-004-1855-1. [DOI] [PubMed] [Google Scholar]
  12. Field M, Munafo MR, Franken IHA. A meta-analytic investigation of the relationship between attentional bias and subjective craving in substance abuse. Psychological Bulletin. 2009;135:589–607. doi: 10.1037/a0015843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Franken IHA. Drug craving and addiction: Integrating psychological and neuropsychopharmacological approaches. Progress in Neuro-Psychopharmacology & Biological Psychiatry. 2003;27:563–579. doi: 10.1016/S0278-5846(03)00081-2. [DOI] [PubMed] [Google Scholar]
  14. Glautier S, Drummond DC. A conditioning approach to the analysis and treatment of drinking problems. British Medical Bulletin. 1994;50:186–199. doi: 10.1093/oxfordjournals.bmb.a072877. [DOI] [PubMed] [Google Scholar]
  15. Judd CM, Kenny DA, McClelland GH. Estimating and testing mediation and moderation in within-subject designs. Psychological Methods. 2001;6:115–134. doi: 10.1037/1082-989X.6.2.115. [DOI] [PubMed] [Google Scholar]
  16. Kamboj SK, Massey-Chase R, Rodney L, Das R, Almahdi B, Curran HV, Morgan CJ. Changes in cue reactivity and attentional bias following experimental cue exposure and response prevention: a laboratory study of the effects of D-cycloserine in heavy drinkers. Psychopharmacology. 2011;217:25–37. doi: 10.1007/s00213-011-2571-2. [DOI] [PubMed] [Google Scholar]
  17. Kavanagh DJ, Andrade J, May J. Imaginary relish and exquisite torture: The elaborated intrusion theory of desire. Psychological Review. 2005;112:446–467. doi: 10.1037/0033-295X.112.2.446. [DOI] [PubMed] [Google Scholar]
  18. Miller MA, Fillmore MT. The effect of image complexity on attentional bias towards alcohol-related images in adult drinkers. Addiction. 2010;105:883–890. doi: 10.1111/j.1360-0443.2009.02860.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Miranda R, Ray L, Blanchard A, Reynolds EK, Monti PM, Chun T, Justus A, Swift RM, Tidey J, Gwaltney CJ, Ramirez J. Effects of naltrexone on adolescent cue reactivity and sensitivity: an initial randomized trial. Addiction Biology. 2013;19:941–954. doi: 10.1111/adb.12050. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Monti PM, Rohsenow DJ, Rubonis AV, Niaura RS, Sirota AD, Colby SM, Abrams DB. Alcohol cue reactivity: effects of detoxification and extended exposure. Journal of Studies on Alcohol. 1993;54:235–245. doi: 10.15288/jsa.1993.54.235. [DOI] [PubMed] [Google Scholar]
  21. Oh H, Taylor AH. A brisk walk, compared with being sedentary, reduces attentional bias and chocolate cravings among regular chocolate eaters with different body mass. Appetite. 2013;71:144–149. doi: 10.1016/j.appet.2013.07.015. [DOI] [PubMed] [Google Scholar]
  22. Oh H, Taylor AH. Self-regulating smoking and snacking through physical activity. Health Psychology. 2014;33(4):349–359. doi: 10.1037/a0032423. [DOI] [PubMed] [Google Scholar]
  23. Papachristou H, Nederkoorn C, Havermans R, Bongers P, Beunen S, Jansen A. Higher levels of trait impulsiveness and a less effective response inhibition are linked to more intense cue-elicited craving for alcohol in alcohol-dependent patients. Psychopharmacology. 2013;228:641–649. doi: 10.1007/s00213-013-3063-3. [DOI] [PubMed] [Google Scholar]
  24. Ramirez JJ, Miranda R., Jr Alcohol craving in adolescents: bridging the laboratory and natural environment. Psychopharmacology. 2014;231:1841–1851. doi: 10.1007/s00213-013-3372-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Ramirez JJ, Monti PM, Colwill RM. Alcohol cue exposure effects on craving and attentional bias among underage college student drinkers. Psychology of Addictive Behaviors. 2014 doi: 10.1037/adb0000028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Robinson TE, Berridge KC. The neural basis of drug craving: An incentive-sensitization view. Addiction. 1993;95:S91–S117. doi: 10.1016/0165-0173(93)90013-P. [DOI] [PubMed] [Google Scholar]
  27. Rose AK, Brown K, Field M, Hogarth L. The contributions of value-based decision-making and attentional bias to alcohol-seeking following devaluation. Addiction. 2013;108:1241–1249. doi: 10.1111/add.12152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Ryan F. Detected, selected, and sometimes neglected: Cognitive processing of cues in addiction. Experimental and Clinical Psychopharmacology. 2002;10:67–76. doi: 10.1037//1064-1297.10.2.67. [DOI] [PubMed] [Google Scholar]
  29. Schmukle SC. Unreliability of the dot probe task. European Journal of Personality. 2005;19:595–605. doi: 10.1002/per.554. [DOI] [Google Scholar]
  30. Schoenmakers TM, de Bruin M, Lux IF, Goertz AG, Van Kerkhof DH, Wiers RW. Clinical effectiveness of attentional bias modification training in abstinent alcoholic patients. Drug and Alcohol Dependence. 2010;109:30–36. doi: 10.1016/j.drugalcdep.2009.11.022. [DOI] [PubMed] [Google Scholar]
  31. Seo D, Jia Z, Lacadie CM, Tsou KA, Bergquist K, Sinha R. Sex differences in neural responses to stress and alcohol context cues. Human Brain Mapping. 2011;32:1998–2013. doi: 10.1002/hbm.21165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Sobell LC, Sobell MB. Timeline follow-back: A technique for assessing self reported ethanol consumption. In: Allen, Litten RZ, editors. Measuring alcohol consumption: Psychological and Biological Methods. Totowa, NJ: Humana Press; 1992. pp. 41–72. [Google Scholar]
  33. Szasz PL, Szentagotai A, Hofmann SG. Effects of emotion regulation strategies on smoking craving, attentional bias, and task persistence. Behaviour Research and Therapy. 2012;50:333–340. doi: 10.1016/j.brat.2012.02.010. [DOI] [PubMed] [Google Scholar]
  34. Szegedi A, Lorch B, Scheurich A, Ruppe A, Hautzinger M, Wetzel H. Cue exposure in alcohol dependent patients: preliminary evidence for different types of cue reactivity. Journal of Neural Transmission. 2000;107:721–730. doi: 10.1007/s007020070073. [DOI] [PubMed] [Google Scholar]
  35. Tapert SF, Cheung EH, Brown GG, Frank LR, Paulus MP, Schweinsburg B, Meloy MJ, Brown SA. Neural response to alcohol stimuli in adolescents with alcohol use disorder. Archives of General Psychiatry. 2003;60:727–735. doi: 10.1001/archpsyc.60.7.727. [DOI] [PubMed] [Google Scholar]
  36. Taylor AH, Oh H, Cullen S. Acute effect of exercise on alcohol urges and attentional bias towards alcohol related images in high alcohol consumers. Mental Health and Physical Activity. 2013;6:220–226. doi: 10.1016/j.mhpa.2013.09.004. [DOI] [Google Scholar]
  37. Thomas SE, Drobes DJ, Deas D. Alcohol cue reactivity in alcohol- dependent adolescents. Journal of Studies on Alcohol. 2005;66:354–360. doi: 10.15288/jsa.2005.66.354. [DOI] [PubMed] [Google Scholar]
  38. Thomas SE, Randall PK, Brady K, See RE, Drobes DJ. An acute psychosocial stressor does not potentiate alcohol cue reactivity in non-treatment-seeking alcoholics. Alcoholism: Clinical and Experimental Research. 2011;35:464–473. doi: 10.1111/j.1530-0277.2010.01363.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Tiffany ST, Conklin CA. A cognitive processing model of alcohol craving and compulsive alcohol use. Addiction. 2000;95(S2):S145–S153. doi: 10.1080/09652140050111717. [DOI] [PubMed] [Google Scholar]
  40. Townshend JM, Duka T. Attentional bias associated with alcohol cues: differences between heavy and occasional social drinkers. Psychopharmacology. 2001;157:67–74. doi: 10.1007/s002130100764. [DOI] [PubMed] [Google Scholar]
  41. Van Rensburg KJ, Taylor A, Hodgson T. The effects of acute exercise on attentional bias towards smoking-related stimuli during temporary abstinence from smoking. Addiction. 2009;104:1910–1917. doi: 10.1111/j.1360-0443.2009.02692.x. [DOI] [PubMed] [Google Scholar]
  42. Wechsler H, Lee JE, Nelson TF, Kuo M. Underage college students’ drinking behavior, access to alcohol, and the influence of deterrence policies. Findings from the Harvard School of Public Health College Alcohol Study. Journal of American College Health. 2002;50:223–236. doi: 10.1080/07448480209595714. [DOI] [PubMed] [Google Scholar]
  43. Wikler A. Recent progress in research on the neurophysiological basis of morphine addiction. American Journal of Psychiatry. 1948;105:329–338. doi: 10.1176/ajp.105.5.329. [DOI] [PubMed] [Google Scholar]
  44. Willner P, Field M, Pitts K, Reeve G. Mood, cue and gender influences on motivation, craving and liking for alcohol in recreational drinkers. Behavioral Pharmacology. 1998;9:631–642. doi: 10.1097/00008877-199811000-00018. [DOI] [PubMed] [Google Scholar]
  45. White HR, Labouvie ER. Towards the assessment of adolescent problem drinking in adolescence. Journal of Studies on Alcohol. 1989;50:30–37. doi: 10.15288/jsa.1989.50.30. [DOI] [PubMed] [Google Scholar]

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