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. Author manuscript; available in PMC: 2020 Dec 1.
Published in final edited form as: Psychol Addict Behav. 2019 Oct 10;33(8):677–684. doi: 10.1037/adb0000522

Alcohol Administration Reduces Attentional Bias to Alcohol-related But Not Food-related Cues: Evidence for a Satiety Hypothesis

Ramey Monem 1, Mark T Fillmore 2
PMCID: PMC6888930  NIHMSID: NIHMS1051450  PMID: 31599605

Abstract

Attentional bias to alcohol is a well-documented effect whereby drinkers allocate greater visual attention towards alcohol-related stimuli rather than non-appetitive, neutral stimuli. Some recent research has shown that acute administration of alcohol temporarily reduces attentional bias to alcohol cues, possibly because alcohol consumption satiates the motivation to drink. However, the specificity of this effect has not been tested and so it is unclear whether reduced attentional bias following alcohol is specific to alcohol-related stimuli or if attention to other appetitive stimuli is also reduced (e.g., food). This study tested the degree to which acute alcohol administration selectively reduced attentional bias to alcohol-related but not to food-related cues in a group of 23 healthy young adults who reported alcohol consumption of roughly twice per week. Attentional bias to alcohol-related and food-related cues was tested using visual dot probe tasks following two active doses of alcohol, 0.30 g/kg and 0.65 g/kg, and a placebo. Results showed that attentional bias, measured as fixation time to stimuli on the visual probe tasks, to alcohol cues declined in a dose dependent manner, while attentional bias to food cues was unaffected by the doses. The evidence suggests that alcohol consumption specifically reduces attentional bias to alcohol-related stimuli while bias to other appetitive stimuli remains intact. Evidence that alcohol consumption reduces attentional bias specifically to alcohol cues lends further credibility to the satiation theory and to the utility of attentional bias as an indicator of acute and transient changes in an individual’s motivation to use alcohol.

Keywords: attentional bias, alcohol, food, satiation, specificity

Attentional bias to alcohol refers to the allocation of heightened attention to alcohol-related stimuli (Goldstein & Volkow, 2011). Attentional bias is believed to be the result of classical conditioning in heavy drinkers due to their history of consumption (Field & Cox, 2008). Associations with alcohol use occur alongside the presence of alcohol-related cues, including the alcohol itself. As the associative pairing between the alcohol effects and alcohol cues develops, these cues begin to acquire incentive salience, increasing the likelihood that drinkers will attend to these cues and initiate or continue a drinking episode in their presence (Franken, 2003).

Attentional bias and the importance of attentional bias to alcohol has been the focus of several lines of research in the field of alcohol abuse. The theory that attentional bias is a result of classical conditioning is likely the reason that alcohol abusers pay more attention to alcohol-related stimuli over those who do not drink or are not heavy drinkers (Marczinski, Combs & Fillmore, 2007). This bias, then, could be considered a cognitive indicator of a pattern of heavy drinking and potential alcohol abuse. If an individual has an attentional bias to alcohol, it is possible they are at a greater risk than others to develop or perhaps already have a substance use disorder. Beyond this, attention allocated to alcohol has been shown to be related to craving (Field & Cox, 2008). This suggests that attentional bias may actually serve a role in motivating alcohol consumption.

Alcohol-related stimuli have been theorized to activate an automatic process that elicits an individual to begin consumption regardless of whether that was their intention (Stacy & Wiers, 2010). Indeed, this motivation to drink driven by a process such as attentional bias can be so powerful it might even overcome active efforts to avoid alcohol use. From this perspective, attentional bias could play a role as a powerful contributor to the initiation of alcohol consumption. Because of this potential consequence of attentional bias, the process has been identified as a target for research considering how to clinically approach alcohol abuse (Cox, Hogan, Kristian & Race, 2002; Schoenmakers et al., 2010). Taken together, attentional bias to alcohol could be conceptualized as either a cognitive indicator of alcohol abuse or a potential risk factor for alcohol abuse, or possibly both at once.

Attentional bias is commonly measured by the visual dot probe task (Field & Cox, 2008). In this task, participants view an alcohol-related and a neutral stimulus briefly presented side-by-side on a computer screen. Upon the offset of the image presentation, a probe target appears in place of one of the images and the participant makes a choice response indicating the location of the probe, the rationale being that responses will be faster to probes in locations already being attended, such as the location of an alcohol-related image. More recently, eye-tracking technology has been integrated into the administration of the visual dot probe task (Posner, Snyder & Davidson, 1980; Miller & Fillmore, 2010; Field & Cox, 2008; Weafer & Fillmore, 2012). Eye-tracking equipment monitors the amount of time an individual visually attends to each of these images in this task, with longer fixation times on alcohol-related images compared to the neutral images providing an indication of attentional bias (Miller & Fillmore, 2010).

Monitoring fixation time provides a more straightforward measure of attention (Henderson, 2003). Determining where an individual is looking is an unambiguous way to determine where they are choosing to allocate their attention as opposed to extrapolating from, for instance, how quickly he or she reacts to a probe. Eye-tracking also opens up a means to measure attentional bias without the need of using any performance-based tasks. Images can simply be presented to an individual and they could be asked to scan a scene, such as in the scene inspection paradigm (Weafer & Fillmore, 2012). Eye-tracking and fixation time as a measure of attentional bias has extended beyond alcohol research. There has been evidence to suggest that a dwell time measure on the visual dot probe was more reliable when compared to manual reaction times for food-related cues when assessing for attentional bias to food (van Ens, Schmidt, Campbell, Roefs & Werthmann, 2019)

The majority of studies on attentional bias to alcohol focus on differences in attentional bias between low- and high-risk drinkers. For example, Townshend and Duka (2001) found that heavy drinkers showed more attentional bias than light drinkers using the visual-probe task. Likewise, Johnsen et al. (1994) found that alcohol-dependent inpatients showed more attentional bias towards alcohol cues than did light drinkers. There have been findings to show that alcohol dependent drinkers demonstrated an attentional bias away from alcohol stimuli, however this occurred in a population undergoing inpatient treatment wherein alcohol and alcohol-related stimuli become negatively valanced and alcohol use is associated with negative outcomes (Townshend & Duka, 2007). Overall, studies into alcohol use and attentional bias to alcohol show that high-risk substance users typically show more attentional bias towards substance-related cues relative to their low-risk counterparts (e.g., Cox, Blount, & Rozak, 2000; Field, 2005; Ryan, 2002).

Despite the wealth of research on attentional bias to alcohol-related cues, relatively little is known about the acute effect of alcohol on such attentional bias. Weafer and Fillmore (2013) demonstrated that, in heavy drinkers, 0.65 g/kg alcohol yielding 80 mg/100 ml BAC reduced attentional bias compared with the administration of a placebo. Similarly, Duka and Townshend (2004) identified a negative correlation between the attentional bias under a 0.60 g/kg alcohol dose and drinking habits such that heavier drinkers demonstrated less attentional bias after alcohol consumption. Roberts and Fillmore (2015) demonstrated differences in attentional bias at different points of the BAC curve, where alcohol-induced reduction of attentional bias was most evident early after drinking, when BACs was ascending compared with the descending phase of the BAC curve.

The reasons why alcohol consumption might reduce attentional bias are not entirely clear. One possibility is that the diminished attentional bias reflects reduced incentive-motivational properties of alcohol-related stimuli (Weafer & Fillmore, 2013). Alcohol consumption could produce an acute reduction in attentional bias cues because its acute rewarding effects satiates the motivation to drink. Evidence for this “alcohol satiety hypothesis” would be important because it would demonstrate the ability of attentional bias measures to indicate acute and transient changes in an individual’s motivation to use alcohol or some other drug. However, another potential explanation for reduced attentional bias to alcohol during intoxication is that the drug acutely impairs attentional focus in general. A primary indicator of attentional bias is the ability to visually locate and maintain ocular fixation on the stimuli. Alcohol is well recognized for impairing this ability. Studies show that alcohol slows eye movements (i.e., saccades) and impairs the ability to visually locate and maintain ocular fixation toward objects during visual search tasks (i.e., Abroms, Gottlob, & Fillmore, 2006; Baloh, Sharma, Moskowitz, & Griffith, 1979; Buikhuisen & Jongman, 1972; Holdstock & de Wit, 1999; Katoh, 1988; Moskowitz, Ziedman, Sharma, 1976; Rohrbaugh et al., 1988). Any acute disruption of attention by alcohol could account for a transient loss of attentional bias during performance of the visual dot probe task.

One method to test the degree to which reduced attentional bias during intoxication represents changes in reward salience and not general ocular disruption is to test the specificity of attentional bias to alcohol versus some other appetitive cue (e.g., food cues). The specificity of attentional bias is still questionable because traditionally the neutral cue is the control, which is not an appetitive stimulus and is not itself motivating. A more stringent test would be to compare bias against something that also engenders attentional bias. To determine whether the reduced attentional bias following alcohol consumption is specific only to alcohol-related stimuli, the current study tested the acute effects of alcohol on attentional bias to both alcohol cues and appetitive food cues. Food stimuli were used as a comparator as research shows that food images evoke an attentional bias compared to non-appetitive “neutral” stimuli, particularly when participants are tested in a fasted state (Tapper, Pothos, & Lawrence, 2010; Werthmann et al., 2011). In order to more thoroughly assess for satiation to a specific stimulus, attentional bias must be evaluated in the context of another motivating cue for which would not anticipate participants to become satiated.

The present study aimed to determine the degree to which acute alcohol administration would selectively reduce attentional bias to alcohol-related but not to food-related cues in a group of healthy young adults. Attentional bias to alcohol-related and to food-related cues was tested in response to placebo and two active doses of alcohol, 0.30 g/kg and 0.65 g/kg, which yield peak BACs of 50 mg/100 ml and 80 mg/100 ml, respectively. The 0.30 g/kg and 0.65 g/kg doses were selected based on prior research showing that these doses reduce attentional bias to alcohol-related cues (Duka & Townshend, 2004; Roberts & Fillmore, 2015; Weafer & Fillmore, 2013). Over three testing sessions, participants were given one of three doses of alcohol (placebo, 0.30 g/kg and 0.65 g/kg). Following each dose, participants completed an alcohol and food version of the visual dot probe task. The alcohol satiety hypothesis would be supported by evidence that alcohol selectively reduces attentional bias to alcohol cues in a dose dependent manner, while having no effect on attentional bias to food cues.

Methods

Participants

Twenty-three adults (10 men and 13 women) between the ages of 21 and 34 years participated in this study (mean age = 24.6, SD = 3.9). The racial make-up was as follows: Asian (n = 1), African American (n = 2), Native Hawaiian/Pacific-Islander (n = 1), Caucasian (n = 17) and Other (n = 2). Volunteers responded to fliers or internet postings advertising for non-dependent drinkers interested in participating in a study examining the relation between alcohol use and mental and behavioral performance. Inclusion criteria included being of legal drinking age, reporting being a current, regular drinker with a drinking frequency of at least once per week over the past 90 days, and no history of alcohol use disorder or treatment for alcohol use. Individuals who reported any psychiatric disorder, CNS injury, or head trauma did not participate, nor did those who endorsed use of medications with known negative side effects when combined with alcohol or those reporting dependence on illicit drugs or with active or previous treatment for alcohol use. Participants were also excluded if they reported any eye or vision issues that would interfere with the eye-tracking monitor’s ability to track their eyes. Screening for pathological or risky drinking was done during initial telephone contact where participants were asked about their history of alcohol use treatment and were administered the AUDIT and SMAST (Saunders, Aasland, Babor, De la Fuente, & Grant, 1993; Selzer, Vinokur, & van Rooijen, 1975). If an AUDIT score of 8 or more was found in conjunction with an SMAST score of 5 or more or if the participant indicated any history of treatment for alcohol use, they were excluded from participating in this study. The University of Kentucky Medical Institutional Review Board approved the study (IRB Protocol 12-0737-F1V, Behavioral Dysregulation and Alcohol Sensitivity).

The sample size used in this study was based on previous work from our laboratory where samples of moderate to heavy drinkers using 20 participants have detected alcohol effects on comparable measures of attentional bias to alcohol with medium to large effect sizes (d = 0.81; partial η2 = 0.17 - 0.42) (Miller & Fillmore, 2011; Roberts & Fillmore, 2015).

Materials and Measures

Visual dot probe task.

The task was operated using E-Prime experiment generation software (Psychology Software Tools, Pittsburgh, PA) and was performed on a PC. Fixations were measured using a Tobii T120 Eye Tracking Monitor (Tobii Technology, Sweden). Stimuli were presented on the Tobii Monitor and dual embedded cameras tracked participants’ gaze locations. Participants were seated with their heads approximated 60 cm in front of the computer with a free range of head and neck motion. Gaze locations were sampled at 120 Hz and fixations were defined as gazes with standard deviations less than 0.5 degrees of visual angle for durations of 50 ms or longer. Two versions of the visual dot probe were used in the study: an alcohol version that measured bias to alcohol images and a food version that measured bias to food images.

For the alcohol version, participants viewed a neutral and alcohol image presented side-by-side on a computer monitor for 1000 ms. Upon offset of the images, a visual-probe appeared which participants responded to by pressing a key corresponding to the probe’s location. The pictures consisted of 10 alcohol-related images (alcohol beverages such as beer, wine and liquor) that were paired with 10 neutral images (non-alcohol beverages such as soft drinks, milk and juice). The task also included additional “filler” trials that consisted of 10 pairs of non-beverage neutral images to reduce the likelihood of habituation to the alcohol target stimuli (primarily consisting of office supplies). Each pair was presented 4 times, totaling 80 trials with 40 critical trials. For each critical trial where target images were presented, the total duration of all fixations directed towards each image type (i.e., alcohol or neutral images) was calculated. These values were averaged across trials to produce a mean fixation time for each image type. To ensure consistency with previous research, the same task has been used in previous research to show that acute administration of alcohol reduces attentional bias to alcohol-related images (Weafer & Fillmore, 2013; Roberts & Fillmore, 2015).

The food version of the visual dot probe task was comprised of an entirely different set of stimuli, but was operationally identical to the alcohol version of the task. The food version contained 10 target appetitive images of food (i.e., pizza, salad or chocolate cake), paired with 10 neutral, non-food images (i.e., Manila file folders with yellow paper, green and white paperclips or brown hand towels) that were designed to match the food images for size, color and complexity. The task also presented 10 additional “filler” image pairings.

Timeline Follow-Back (TLFB).

Participants’ drinking habits were assessed using the timeline follow-back procedure (Sobell & Sobell, 1992), which assessed daily drinking patterns over the past 3 months. Participants were asked to fill in a blank calendar dating back 90 days from the testing session. For each day, individuals were instructed to report how many standard alcohol drinks they consumed, the duration of their drinking episode, and whether or not they felt drunk that day. From this information, four measures of drinking habits were obtained: (1) total number of drinks consumed (total drinks), (2) total number of drinking days (drinking days), and (3) total number of days characterized by subjective drunkenness (drunk days).

Alcohol Use Disorders Identification Test (AUDIT).

The AUDIT is a screening instrument that is used to identify at-risk problem drinkers (AUDIT; Saunders et al, 1993). It was used in the current study to provide a brief assessment of problematic alcohol use. The 10-item self-report questionnaire consists of 10 items about drinking patterns, negative psychosocial outcomes, and other indicators of alcohol use disorder. Scores on this measure can range from 0 (no alcohol-related problems) to 40 (severe alcohol-related problems).

Subjective Effects Questionnaire.

Participants provided ratings of subjective states using a visual analogue scale (Van Dyke & Fillmore, 2014). They rated 15 items (I feel depressed; I have no motivation; I feel hungry; I am willing to drive a car; I feel sedated; I feel happy; I feel intoxicated; I find it hard to concentrate; I feel thirsty; I feel nervous; I feel irritable; I feel confident; I feel I am legally able to drive a car; I feel stimulated/alert; I feel down) by drawing vertical line on a 100mm long scale ranging from "not at all" at one end to "very much" at the other. Previous research has shown that these scales are sensitive to changes in subjective effects that occur over the time course of an alcohol dose (e.g., Fillmore, 2001). This questionnaire was administered as part of a standard test battery and in this study served as means to verify individuals were subjectively experiencing the effects of alcohol consumption. To this end, the items "I feel intoxicated" and "I feel hungry" were of primary interest in this study, as they have the greatest conceptual relevance to the idea of satiety to alcohol and food, respectively.

Procedure

The study took place over the course of four test sessions at the Behavioral Pharmacology lab in the Psychology Department. Participants were instructed that they must abstain from alcohol consumption for 24-hours prior to each testing session. Additionally, participants were instructed to fast from food and any beverages apart from water for a period of 4 hours prior to a testing session. Fasting increases the likelihood that participants will display attentional bias to food-related images (Tapper, Pothos, & Lawrence, 2010; Werthmann et al., 2011). During the first session, informed consent was obtained, and a zero BAC was confirmed by breath analysis. Illicit drug use was assessed by urine analysis (ICUP Drug Screen, Instant Technologies). Positive screens for drugs other than tetrahydrocannabinol (THC) during a testing session that involved alcohol administration resulted in rescheduling of that session. Those whose urine tested positive for THC were allowed to continue the session only if they abstained from using THC for at least 24 hours prior to the sessions. Positive urine analysis for any substance other than THC resulted in discontinuation from the study. No female volunteers who were breast-feeding or who were pregnant participated in the research. Pregnancy was tested by urine analysis (Icon25 Hcg Urine test, Beckman Coulter). Screenings were followed by completion of questionnaires on demographics, general health status, drug use, and the TLFB and AUDIT. Participants were then acquainted with the alcohol and food version of the visual dot probe task through practicing full-length versions of each test in the absence of any active dose of alcohol.

Participants’ attended three test sessions to test the acute effects of alcohol on the measures of attentional bias to alcohol cues and to food cues. As in session 1, participants provided a breath sample to verify a zero BAC and a urine sample for illicit drug screening. After a zero-BAC and negative urine analysis were confirmed, participants were administered either a placebo, a 0.30 g/kg or 0.65 g/kg dose of alcohol. The 0.30 g/kg dose of alcohol was intended to produce an average peak BAC of 50 mg/100 ml, and the 0.65 g/kg alcohol dose was to intended to produce an average peak BAC of 80 mg/100 ml. All participants received one dose per session and received one of each possible dose across all three test sessions. Dose order was counterbalanced across participants, and participants were blind to dose order. Participants were not given any instruction as to whether or how doses would vary across the testing sessions. The alcohol beverage was served as one-part alcohol and three-parts carbonated mix divided equally into two glasses. The placebo consisted of four-parts carbonated mix that matched the volume of the 0.30 g/kg dose. Five milliliters of alcohol were floated on the top of each placebo glass, and the glasses were sprayed with an alcohol mist that resembles condensation and provides an alcohol odor. Participants drank the beverages within six minutes.

At 60 minutes post-administration, participants completed the alcohol and food versions of the visual dot probe task. Task order (alcohol, food) was counterbalanced across participants, with the task order consistent for a given participant across dose sessions. Testing was completed 75 minutes following dose administration in order to ensure all testing occurred during the peak and early descending portions of the BAC curve with both tasks being completed at comparable BACs. BAC was monitored throughout the session via breath analysis, starting at 25 minutes after administration with breath samples being gathered every 20 minutes during the ascending limb of the testing session. Participants remained in the lab until they were at or below a 20 mg/100 ml BAC level. The inter-session interval ranged from three to four days and all sessions were completed within two weeks. At the conclusion of the final testing session participants were paid and debriefed.

Criterion Variables and Data Analyses

On the visual dot probe tasks, an average per-trial fixation time (msec) was calculated across all forty critical target trials, where greater average fixation time to alcohol or food stimuli compared to neutral stimuli was indicative of attentional bias. Dose effects on attentional bias to alcohol images were analyzed by 2 Stimuli (alcohol vs. neutral) X 3 Dose (placebo, 0.30g/kg, 0.65g/kg) repeated measures analysis of variance (ANOVA) of fixation time. Dose effects on attentional bias to food images were also analyzed by 2 Stimuli (food vs. neutral) X 3 Dose (placebo, 0.30g/kg, 0.65g/kg) repeated measures analysis of variance (ANOVA) of fixation time. Simple effects were analyzed using paired-samples t tests to determine for which doses attentional bias was observed.

Additional Analyses

Attentional bias to alcohol and food also could be associated with participants’ explicit subjective ratings of their level of intoxication and hunger, respectively. Relationships between these subjective effects and attentional bias were examined by correlational analyses. Additionally, all analyses in this study were conducted to include sex as a between-subjects variable. These analyses found no significant effect of sex and did not change the significance level of other main effects or interactions. As such, reported analyses of attentional bias and other measures are collapsed across sex.

Results

Drinking and Demographic Information

Participants’ drinking habits and demographic information are presented in Table 1. Men and women did not significantly differ in their drinking habits. Some participants reported past month, but not daily use, of nicotine (n = 7). Past month use of marijuana (n = 6) and sedatives (n = 1) was also reported. Participants verbally confirmed a minimum 24-hour abstinence from these substances prior to each session and urine analyses were negative.

Table 1.

Mean Drinking Habits and Demographics Measures by Sex

Group Contrasts
Women
 Men
M SD Min -
Max 		M SD Min -
Max
Drinking Habits
Total Drinks 89.1 78.6 15 - 263 102.8 70.5 10 - 209 ns
Drinking Days 25.2 14.7 6 - 51 33.0 20.6 10 - 69 ns
Drunk Days 8.2 7.5 1 - 23 5.5 5.3 0 - 16 ns
AUDIT 7.6 4.1 2 - 15 8.3 4.0 2 - 15 ns
Demographics
Age 24.5 4.4 21 - 34 24.9 3.4 21 - 31 ns
Height (cm) 164.8 4.7 159 - 174 176.6 6.7 166 - 188 ***
Weight (kg) 71.5 10.8 53 - 91 82.2 10.5 62 - 98 *
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Note. Group contrasts were tested by one-way between subjects ANOVAs. Total drinking, drinking days and drunks days measured over past 90 days by the Timeline Follow-Back.

*

p < .05

**

p < .01

***

p < .001

Blood Alcohol Concentrations

BACs at all time points for the active dose conditions are presented in Table 2. This table shows that following 0.30 g/kg alcohol, BAC declined slightly from 25-65 min post administration during the visual dot probe testing. The mean BAC during this interval was 30.8 mg/100 ml). Following 0.65 g/kg alcohol, BAC increased during this time and the mean BAC over the interval was 72.7 mg/100 ml. A 2 Active Dose (0.30, 0.65 g/kg) X 3 Time (25, 45, 65 min) ANOVA identified a main effect of dose, F(1,22) = 235.9, p < 0.001, ηp2 = 0.91, due to the overall higher BACs following 0.65 g/kg alcohol compared with 0.30 g/kg alcohol. No main effect of time was found, p > 0.05. A dose X time interaction was observed, F(1, 22) = 6.194, p = 0.004, ηp2 = 0.21, due to the decline in BACs for 0.30 g/kg alcohol compared to the rise observed following 0.65 g/kg alcohol. No detectable BAC was observed at any time point in the placebo condition.

Table 2.

Mean Blood Alcohol Concentrations for all dose conditions (BAC)

0.30 g/kg 0.60 g/kg
Minutes past dose 25 45 65 85 25 45 65 85
BAC
 M 34.3 30.8 25.9 22.6 68.9 72.7 79.8 66.6
 SD 9.5 8.5 7.4 6.5 20.9 14.5 12.3 12.4
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Note. All BACs are reported as mg/100 ml.

Attentional Bias to Alcohol

Figure 1 shows the fixation times to stimuli across alcohol doses on the alcohol version of the visual dot probe task. The figure shows longer fixation to alcohol compared with neutral stimuli, with the difference diminishing as a function of alcohol dose. A 2 (stimuli) × 3 (dose) ANOVA yielded main effects of dose, F(1,22) = 6.93, p = 0.015, ηp2 = 0.24, and stimuli, F(1,22) = 5.22, p = 0.009, ηp2 = 0.19. No interaction was found, F(2,44) = 1.05, p = 0.36. Figure 1 shows that the main effect of stimuli is attributable to consistently more fixation time spent on alcohol images compared to neutral images across all doses of alcohol. Furthermore, the main effect of dose is due to the overall decrease in fixation time as the dose of alcohol increases. Paired-sample t tests indicated significant attentional bias at placebo, t(22) = 1.99, p = 0.03, d = 0.44, and following 0.30 g/kg alcohol, t(22) = 1.89, p = 0.036, d = 0.23. However, following 0.65 g/kg alcohol, the magnitude of attentional bias was no longer significant, t(22) = 0.72, p = 0.241, d = 0.09.

Figure 1.

Figure 1.

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Fixation times (msec) to neutral stimuli in comparison with alcohol stimuli (shown left) and food stimuli (shown right), following three alcohol doses: placebo, 0.30 g/kg, and 0.65 g/kg.

Attentional Bias to Food

Alcohol effects on attentional bias to food are shown in Figure 1. This figure shows longer fixation to food compared with neutral stimuli, and that the magnitude of this attentional bias to food was consistent across all alcohol doses. A 2 (stimuli) × 3 (dose) ANOVA yielded a main effect of dose, F(1,22) = 17.61, p = 0.001, ηp2 = 0.44, and stimuli, F(1,22) = 3.25, p = 0.048, ηp2 = 0.13. There was no interaction, F(2,44) = .20, p = 0.82. The main effect of stimuli is due to greater fixation times to food stimuli compared with neutral stimuli and the main effect of dose was due to the overall decline in fixation times to both stimuli. Paired sample t tests showed that the stimulus difference in fixation time was significant at all doses, ts(22) = 2.45-4.19, ps < 0.05, ds = 0.34 – 0.48, indicating attentional bias for all doses. Thus, unlike attentional bias to alcohol-related stimuli, there was no loss of attentional bias to food-related stimuli as a function of alcohol dose.

Associations of Attentional Bias with Subjective Effects

Attentional bias to alcohol and food also could be associated with participants’ explicit subjective ratings of their level of intoxication and hunger, respectively. Table 3 details the subjective levels of intoxication and hunger for each of the three doses of alcohol administered in the study. As can be seen from Table 3, subjective intoxication climbed in a dose dependent matter, where participants endorsed greater intoxication at the higher doses of alcohol. A one-way ANOVA demonstrated a statistically significant dose effect on subjective intoxication, F(2,44) = 63.15, p < 0.001. No significant dose effect was observed for subjective hunger, F(2,44) = 2.63, p = 0.09.

Table 3.

Descriptive statistics and results of regression analyses for subjective effects questionnaire items subjective intoxication and subjective hunger and bias scores

Subjective Intoxication Subjective Hunger
M SD Min - Max r2 p M SD Min - Max r2 p
0.0 g/kg 10.83 16.56 0 – 60 0.02 0.58 66.78 26.23 19 – 100 0.01 0.73
0.30 g/kg 29.61 17.53 1 – 68 0.01 0.68 63.91 24.92 8 – 100 0.10 0.13
0.60 g/kg 63.91 20.19 21 – 100 0.06 0.26 53.00 32.14 0 – 100 0.10 0.13
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Note. Values for subjective intoxication and hunger reported on a 0 - 100 mm visual analog scale.

Relationships between these subjective effects and attentional bias were examined by correlational analyses. Regression analyses were performed using subjective effects measures of intoxication and attentional bias scores, calculated as the difference between fixation time to the target stimuli and the neutral stimuli on the visual dot probe for each dose. Table 3 shows the results of regression analyses between subjective intoxication as a predictor of attentional bias to alcohol during each dose condition, none of which was significant, ps > 0.05. Table 3 also shows regression analyses with subjective hunger as the predictor of attentional bias to food during each dose condition. No significant relationships were observed, ps > 0.05. Thus, subjective levels of intoxication or hunger did not predict the degree of attentional bias displayed to alcohol or food.

Order Effects

In this study, participants were administered the two versions of the visual dot probe task in one of two possible orders with either the alcohol version of the task being completed first or the food version of the task being completed first. This order was consistent across all testing sessions. To determine if the order in which the tasks were done had any effect on attentional bias score, a 2 (task order) × 2 (stimuli) × 3 (dose) ANOVA was performed which yielded no main effect of task order, F(1,21) = 3.127, p = 0.09, or interactions with task order Fs = 0.069 – 0.735, ps = 0.40 – 0.93. Additionally, a 6 (dose order) × 2 (stimuli) × 3 (dose) ANOVA was performed to determine if there was any effect of the order in which doses were consumed by participants during all three testing sessions. This ANOVA similarly yielded no main effect of dose order F(5,17) = 0.71, p = 0.63 or interactions with dose order Fs = 0.49 – 2.08, ps = 0.06 – 0.88. Taken together, the order in which participants completed the visual dot probe tasks or the order in which doses were administered did not impact attentional bias to either food or alcohol.

Discussion

Supporting the first hypothesis of this study, attentional bias to alcohol declined in a dose dependent matter on the visual dot probe task. Attentional bias to alcohol was observed following placebo, however, under the highest alcohol dose (0.65 g/kg), no observable attentional bias was found. The findings also supported the second hypothesis that attentional bias to food would be maintained under active doses of alcohol compared to placebo. Results indicate that significant attentional bias to food in the food version of the visual dot probe task was observed during all testing sessions and that the degree of attentional bias was unchanged across sessions. Overall, this study provides evidence for the specificity of alcohol’s effect on attentional bias to alcohol-related stimuli.

These findings replicate the dose-dependent reduction in attentional bias to alcohol-related cues that is consistent with findings from previous research (Duka & Townshend, 2004; Weafer & Fillmore, 2013). A key assumption of attentional bias in alcohol research is that it plays an important role in the desire to drink alcohol thereby motivating a drinking episode. Attenuation of attentional bias following alcohol administration suggests that there may be a satiation effect of alcohol consumption such that when an individual feels intoxicated, they are no longer motivated to drink and therefore do not attend to alcohol-related stimuli to the same degree as they do when sober. Findings from Roberts and Fillmore (2015) also provide evidence for this satiation hypothesis by demonstrating the differences in attentional bias at different points of the BAC curve. On the ascending limb, attentional bias was shown to be significantly lower than the degree of attentional bias observed under placebo. On the descending limb, however, the attentional bias increased to become equal to bias experienced at placebo. Taken together, these between- and within-dose examinations of attentional bias demonstrate the degree to which attentional bias can be highly responsive to the subjective state of the drinker, dynamically changing in magnitude in response to dose level and changing BACs.

A novel finding of this research is the maintenance of attentional bias to food under the same doses of alcohol in which bias to alcohol declined, which indicates the specificity of the alcohol consumption effect on the attentional bias to alcohol cues. Attentional bias to food cues was observed in all testing sessions, with no significant reduction following the same alcohol doses that reduced attentional bias to alcohol cues. These patterns of change in bias to alcohol and food cues following the doses also corresponded with changes in participants’ subjective reports of intoxication and hunger. The reduction of attentional bias to alcohol across doses coincided with an increase in self-reported intoxication as a function of dose. The consistent degree of attentional bias to food coincided with a fairly consistent self-reported level of hunger in each dose condition. However, it should be noted that, at an individual difference level, participants’ attentional bias to alcohol or food showed no significant relationship to their self-reports of intoxication and hunger following any dose. That is, those in the sample who showed the least attentional bias to alcohol did not necessarily report the most subjective intoxication and those who showed the most attentional bias to food did not necessarily report the most hunger. The failure to observe such relationships could simply be due to sample size that is insufficient to explore such correlational findings. However, null effects could also indicate a commonly observed dissociation between self-reported effects of drugs and the behavioral effects they engender (Weafer & Fillmore, 2008; Van Dyke & Fillmore, 2014).

This study also showed that overall fixation time to both target and neutral images dropped in a dose dependent manner for both the alcohol and food bias tasks. This observation is consistent with findings from previous research where general target non-specific declines in ocular fixations were observed following high doses of alcohol designed to result in a BAC of 80 mg/100 ml (Miller & Fillmore, 2011). It was concluded in that research that alcohol impaired oculomotor functions, which was observed as decreased accuracy and speed of visual saccades. Likewise, the current study similarly demonstrated decreased overall fixation times at higher doses of alcohol, likely as a result of the same oculomotor impairment. This study demonstrated a general dose-induced decline in fixation time consistent with findings from other research.

A limitation of the current study is that we did not include a parallel condition to test the effects of consuming food on attentional bias to food cues to determine if such bias would diminish. Such a design would provide a more stringent test of the specificity of satiety effects across alcohol and food. However, our aim was to demonstrate the effects of alcohol consumption on the specificity of attentional bias to alcohol versus another consumable appetitive cue also known to elicit attentional bias (i.e., food). Indeed, the study successfully used food as a cue capable of eliciting attentional bias at a magnitude comparable to the alcohol cues used in the study. To the extent that attenuation of attentional bias reflects a satiety effect, it seems reasonable that the consumption of food would also diminish attentional bias to food that had been previously demonstrated in the fasted state.

In conclusion, this study is the first to demonstrate the specificity of this attentional bias to alcohol-related stimuli. As demonstrated previously, this study observed a dose-dependent decline in the degree of attentional bias to alcohol using the visual dot probe task. The same doses of alcohol, however, had no effect on bias to food, indicating that alcohol consumption does not reduce attentional bias to all appetitive stimuli. This finding lends further credibility to the satiation theory of reduced attentional bias following alcohol consumption. Additionally, this study also provides evidence for the relationship between attentional bias and desire to drink, a popular theory behind the phenomenon.

Acknowledgments

This research was funded through National Institute on Alcohol Abuse and Alcoholism, R01 AA021722. The authors declare no conflict of interest

Contributor Information

Ramey Monem, Department of Psychology, University of Kentucky.

Mark T. Fillmore, Department of Psychology, University of Kentucky

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