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. Author manuscript; available in PMC: 2024 Jul 1.
Published in final edited form as: Drug Alcohol Depend. 2023 Apr 26;248:109899. doi: 10.1016/j.drugalcdep.2023.109899

Cognitive preoccupation with drinking and behavioral effects of alcohol as predictors of current consumption patterns

Holley C Allen 1, Mark T Fillmore 1
PMCID: PMC10330465  NIHMSID: NIHMS1900987  PMID: 37178640

Abstract

Background

Acute alcohol responses such as tolerance to alcohol-induced motor impairment and heightened sensitivity to alcohol-induced disinhibition are associated with heavier drinking. Additionally, certain cognitive characteristics of drinkers may also indicate problem-drinking. For example, cognitive and emotional preoccupation (CEP) with alcohol is associated with heavier drinking. However, it is not clear if cognitive markers have value as predicators of heavier drinking beyond that of well-established alcohol response markers. The current study sought to test the predictive potential of CEP in the context of two well-documented alcohol response markers of heavy drinking.

Methods

Data aggregated from three studies comprised a sample of 94 young adult drinkers with no history of alcohol use disorder. Participants’ motor coordination (grooved pegboard) and behavioral disinhibition (cued go/no-go) were assessed following consumption of 0.65 g/kg alcohol and a placebo. CEP was measured via the Temptation and Restraint Inventory (TRI).

Results

Drinkers who expressed both alcohol response markers reported drinking higher doses regardless of their level of CEP. Among drinkers who expressed low sensitivity to both disinhibition and motor impairment, higher CEP was associated with higher typical quantities. Low sensitivity to motor impairment functioned as a standalone marker of heavier drinking.

Conclusions

The findings suggest that a combination of tolerance to motor impairment and high alcohol-induced disinhibition may be sufficient to promote heavier consumption even in the absence of cognitive markers associated with problem drinking. Results also suggest that cognitive characteristics may drive early drinking and contribute to the development of tolerance to acute alcohol effects.

Keywords: Disinhibition, Motor Impairment, Markers of Alcohol-Related Problems, Acute Alcohol Effects, Cognitive and Emotional Preoccupation, Alcohol Tolerance, Alcohol Sensitivity

1. Introduction

Researchers have posited a range of biological, environmental, social, and personality markers associated with heavy drinking. Markers considered as important indicators of alcohol-related problems have shifted over time as is evident across versions of the Diagnostic and Statistical Manual of Mental Disorders (DSM; Nathan et al., 2016). For the first half of the 20th century, diagnoses of “alcoholism” were based largely on alcohol consumption criteria (American Psychiatric Association [APA], 1952, 1968). For example, getting intoxicated 4+ times a year met criteria for an alcoholism diagnosis of “episodic excessive drinking,” and getting drunk 12+ times a year met criteria for an alcoholism diagnosis of “habitual excessive drinking” (APA, 1968).

Later, interest shifted to biological and behavioral responses to alcohol, such as tolerance and withdrawal symptoms (APA, 1980). The addition of “alcohol abuse” as a diagnosis in the DSM-III-R (1987) was defined as a continued desire to drink despite alcohol-related problems, or drinking in dangerous situations, such as in conjunction with driving (APA, 1980). This indicated a marked shift in the field’s conceptualization of alcohol use disorders characterized by movement away from heavy alcohol consumption as the primary indicator towards markers, such as acute alcohol responses (i.e., tolerance) and cognitive characteristics of the drinker (Brown et al., 1985; Newlin & Thomson, 1990; Collins & Lapp, 1992; Schuckit & Smith, 1996; Leigh & Stacy, 2004; Newlin & Renton, 2010; King et al., 2011).

As an acute alcohol response marker, alcohol-induced motor impairment is perhaps the most studied. Individual differences in sensitivity to alcohol-induced motor impairment show robust relationships with drinking habits. Tolerance to this effect is associated with increased risk for AUD (Schuckit, 1985, 1994; Lex et al., 1988; Evans and Levin, 2003). Moreover, the ability to maintain motor coordination may perpetuate a drinking episode as the drinker retains the physical capacity to continue drinking, thereby increasing the quantity of alcohol they consume (Fillmore & Weafer, 2012).

Sensitivity to the disinhibiting effect of alcohol is another possible alcohol response marker. Alcohol has well-established impairing effects on inhibitory control, which in turn increases likelihood of impulsive actions (Rose & Duka, 2007; Loeber & Duka, 2009; López-Caneda et al., 2014). Laboratory tasks like the cued go/no-go and stop-signal tasks measure behavioral disinhibition and are sensitive to the impairing effect of alcohol. Consumption of even moderate doses of alcohol (BACs ≤80 mg/dl) decrease drinkers’ ability to suppress unwanted action (e.g., Fillmore et al., 2005; McCarthy et al., 2012; Weafer & Fillmore, 2016). However, there are marked individual differences in sensitivity to alcohol-induced disinhibition. While some individuals show only subtle impairment of inhibitory control, others show significant disinhibition following the same dose of alcohol (Allen et al., 2021). As a marker for alcohol-related problems, heightened sensitivity to alcohol-induced disinhibition is associated of heavier drinking (Marczinski et al., 2007) and greater ad libitum alcohol consumption in the laboratory (Gan et al., 2014; Weafer & Fillmore, 2008).

In addition to alcohol responses, certain cognitive characteristics may also serve as markers of problem-drinking. Cognitive markers are specific thoughts or cognitive patterns that serve as antecedents for the initiation, maintenance, and escalation of drinking (Tiffany, 1990). One such marker, cognitive and emotional preoccupation (CEP), encompasses thoughts related to a perceived inability to control drinking, drinking to attenuate negative affect, and intrusive drinking-related thoughts. CEP is typically measured using the Temptation and Restraint Inventory (TRI; Collins & Lapp, 1992), where higher scores on the CEP scale are associated with heavier drinking in the laboratory (Collins et al., 1996; Fillmore, 2001; Marczinski et al., 2005) and heavier self-reported use (e.g., Collins et al., 2000; Connor et al., 2004; MacKillop et al., 2006).

Cognitive markers have several advantages over alcohol response markers, namely their ease of assessment and potential to inform preventative action. Markers like CEP can be measured prior to an individual’s first drink and in a sober state. Markers that precede drinking have potential as targets for prevention. To the extent that such cognitions elicit alcohol-seeking behavior, efforts to reduce their occurrence could reduce alcohol consumption. Indeed, assessment of attitudes and thoughts about alcohol among adolescents may help identify at-risk individuals who would benefit from cognitive-based preventative interventions (Krank & Robinson, 2017; Payne et al., 2016). Despite their advantages, it is unclear if cognitive markers have value as predictors of heavier drinking beyond that of well-established alcohol response markers. The current study sought to test the predictive potential of one cognitive marker (CEP) in the context of two well-documented alcohol response markers of heavy drinking (tolerance to motor impairment, sensitivity to disinhibition).

The study examined individual differences in the expression of alcohol-related markers in relation to typical drinking habits. To do this, a large sample size was needed. Laboratory experiments examining acute alcohol effects are time and cost intensive, and they are typically limited to small sample sizes. Small sample sizes preclude testing the possibility that alcohol response markers interact with each other as well as other types of markers to predict drinking habits. To overcome this limitation, data were aggregated from three experiments previously conducted in the authors’ laboratory using common methodology (Miller & Fillmore, 2014; Roberts et al., 2013; Weafer & Fillmore, 2012). This comprised a sample of 94 participants, providing adequate statistical power to test interactions between markers as predictors of drinking habits.

The study measured individual differences in the acute disinhibiting and motor impairing effect of a controlled dose of 0.65 g/kg alcohol in a large sample of young adult drinkers with no history of AUD. Participants’ CEP was measured using the TRI. We hypothesized that high CEP, combined with low sensitivity to alcohol-induced motor impairment, and high sensitivity to alcohol-induced disinhibition would be associated with higher typical quantities of alcohol consumed. Additionally, by examining the unique predictive potential of each marker, analyses tested the degree to which CEP could predict drinking quantities beyond that already predicted by the well-documented alcohol response markers.

2. Method

2.1. Participants

Data were aggregated from three prior studies (Miller & Fillmore, 2014; Roberts et al., 2013; Weafer & Fillmore, 2012) to comprise a large sample size (N = 94) to test these hypotheses. Participants between 21- and 33-years-old (M = 23.47, SD = 2.89) were recruited for participation in research projects assessing the effects of alcohol on behavioral and cognitive functions. All experiments measured CEP using the TRI and measured acute effects of alcohol on disinhibition and motor coordination. Studies included the two counterbalanced dose conditions (0.65 g/kg abs. alc. and a placebo). Participants in all studies were drawn from the same population using the same inclusion and exclusion criteria. These criteria included young adult participants between 21- and 35-years-old with no history of AUD. Participants were required to report drinking at least two alcoholic beverages once per month, and volunteers were excluded if they self-reported a history of head trauma or other central nervous system injury. Volunteers were also excluded if they reported a psychiatric disorder including SUD. To further screen for AUD, the Short-Michigan Alcohol Screening Test (S-MAST; Selzer et al., 1975) was administered, and volunteers with a score of 5+ were excluded. Volunteers who screened positive for recent drug administration using urinalysis were also excluded. No volunteers who were pregnant or breast feeding participated, as determined by self-report measures and urine human chorionic gonadotropin levels. The only participants included in the original three studies who were excluded from the current study were a clinical sample receiving medication for ADHD (n = 19) tested in the study by Roberts et al. (2013). All other participants from these studies were included.

Participants were recruited from the community using flyers, posters, and online and newspaper advertisements. Volunteers responded by calling the laboratory and completed a phone screen procedure. Participants provided informed consent prior to participation, and all studies were approved by the University of Kentucky Medical Institutional Review Board. Participants were compensated for their participation. The sample was comprised of 43 women and 51 men, and participants self-identified as White/Caucasian (n = 80), African American/Black (n = 10), or as other (n = 4).

2.2. Measures

2.2.1. Drinking Habits

Drinking habits were measured using the Personal Drinking Habits Questionnaire (PDHQ; Vogel-Sprott, 1992), a self-report questionnaire assessing participants’ typical drinking patterns. The PDHQ measured two aspects of current and typical drinking behavior including (1) weekly frequency, the typical number of drinking occasions per week and (2) weekly dose, the typical dose of alcohol consumed per drinking episode (mL absolute alcohol/kg body weight) multiplied by drinking frequency.

2.2.2. Disinhibition

A cued go/no-go reaction time task was used to measure participants’ response inhibition to no-go targets and their reaction time to go targets (e.g., Fillmore and Weafer 2004). The task required finger presses on a keyboard and measured the ability to inhibit prepotent behavioral responses of executing a key press. Cues (vertical and horizontal rectangles) provided preliminary information regarding the type of target stimulus (i.e., go or no-go) that was likely to follow, and the cues had a high probability of signaling the correct target. Participants were instructed to press the forward slash (/) key on the keyboard as soon as a go (green rectangle) target appeared and to suppress the response when a no-go (blue rectangle) target was presented. The go cue conditions were of particular interest. Go cues generate response prepotency which speeds response time to go targets. However, subjects must overcome this response prepotency to inhibit the response if a no-go target is subsequently displayed. Response inhibition was measured by the proportion of no-go targets in which subjects failed to inhibit a response (p-inhibition failures) during the test. Disinhibition was indicated by a higher proportion of inhibition failures (i.e., greater p-inhibition failure score). A test required approximately 15 min to complete. The validity of these types of tasks is well-established. Cued go/no-go tasks are sensitive to inhibitory deficits characteristic of dementia (O’Callaghan et al., 2013), ADHD (Tannock, 1998), and the impairing effects of alcohol (Fillmore & Weafer, 2012; Weafer & Fillmore, 2016).

2.2.3. Motor Coordination

Motor coordination was measured using a grooved pegboard task (Lafayette Instruments, Lafayette, IN). During this task, participants place pegs into keyhole-shaped holes. The grooved pegboard is a rectangular metal surface with 25 holes arranged in five rows of five holes. Each peg is 3 mm in diameter and 2.5 cm long and has a rounded side and grooved side. The pegs fit into the holes like a key would fit into a lock, and participants are required to pick up pegs one at a time and place them in holes. A trial on the task was completed when all 25 holes were filled. A grooved pegboard test consisted of four trials, and the time to complete each of the trials was averaged to calculate the measure of motor coordination. The grooved pegboard task is a standardized and well-validated measure of psychomotor performance (e.g., Gershon et al., 2010; Wang et al., 2011; Hamilton et al., 2017). Prior research shows that the task is sensitive to the impairing effects of moderate-high doses of alcohol (e.g., Brumback et al., 2007; Ramchandani et al., 2002; Weafer & Fillmore, 2012).

2.2.4. Cognitive and Emotional Preoccupation

The TRI is a 15-item self-report questionnaire comprised of two higher-order scales measuring CEP and cognitive and behavioral control (CBC). The CBC scale measures attempts to restrain alcohol consumption. It is comprised of two first-order subscales, Restrict (actual attempts to inhibit drinking) and Concern about Drinking (desire to stop drinking). The CEP scale measures respondents’ level of temptation to drink, and it is comprised of the three first-order subscales, Govern (perceived inability to control drinking), Emotion (drinking to mitigate negative affect), and Cognitive Preoccupation (intrusive drinking-related thoughts). The higher-order CEP scale is comprised of 9 items, and participants respond to items on a 9-point Likert scale from 1 (“Never” [representing a lack of preoccupation]) to 9 (“Always” [representing a high degree of preoccupation]). Item scores are summed to generate a total CEP scale score ranging from 0–81, where higher scores indicate greater CEP. The CEP scale of the TRI has demonstrated appropriate internal consistency, discriminant validity, and convergent validity in clinical and non-clinical samples (e.g., Collins et al., 2000; Connors et al, 1998; MacKillop et al., 2006), and it was the primary scale of interest for this study due to robust evidence that CEP serves as a cognitive marker of alcohol-related problems. Meanwhile, the CBC scale reflects behavioral attempts to restrain or control alcohol consumption rather than cognitive patterns. Moreover, extant literature suggests that CBC is not a reliable predictor of alcohol-related risk (Collins et al., 2000; MacKillop et al., 2006; Rinker & Neighbors, 2013), and high CEP has been shown to predict heavier drinking regardless of respondents’ degree of CBC (e.g., Collins et al., 2000; Connor et al., 2004; MacKillop et al., 2006).

2.3. Procedure

All studies employed common methodology and procedures for testing alcohol effects on disinhibition and motor coordination. Studies used a counterbalanced, repeated-dose design with two alcohol dose conditions (0.65 g/kg and placebo). All studies used the same time course of response testing under the doses. Sessions were conducted at the Human Behavioral Pharmacology Laboratory at the University of Kentucky between the hours of 10 AM-6 PM. Participants were required to abstain from alcohol for 24 h prior to each session and fast for 4 h prior to each session. Before testing sessions began, body weight was measured, a BAC of 0.0 mg/dl was verified using a breathalyzer [Intoxilyzer, Model 400 (CMI Inc., Owensboro, KY)], and urine samples were collected to ensure that participants were negative for recent drug use and pregnancy. Urine samples were tested for the presence of metabolites of amphetamine, methamphetamine, barbiturates, benzodiazepines, cocaine, opiates, methadone, phencyclidine, tricyclic antidepressants, and tetrahydrocannabinol (ICUP Drugscreen; Instant Technologies, Norfolk, VA). Volunteers who tested positive for cannabis use and reported use within the past 4 days and those who tested positive for any other drug metabolites were excluded from participation.

2.3.1. Familiarization

All participants attended a familiarization session, during which they were introduced to laboratory procedures, completed the TRI, and completed practice trials of the behavioral tasks. Participants also completed questionnaires including assessment of their substance use history, health status, and demographic information.

2.3.2. Test sessions

Participants were tested on two separate occasions under different doses of alcohol, 0.0 g/kg (placebo) and 0.65 g/kg. Sessions occurred on different days separated by a minimum of 1 day and a maximum of 7 days. The dose order was counterbalanced across participants.

The active dose (0.65 g/kg) was calculated based on participants’ weight and was divided equally into two glasses, each containing one part alcohol (96.4%) and three parts carbonated soda. Participants consumed the dose within 6 mins. This dose administration typically produces an average peak BAC of 80 mg/100 ml about 60–70 minutes after consumption (Marczinski & Fillmore, 2003). The placebo administration was identical except the beverage was comprised of 4 parts carbonated soda of equal volume to the active dose.

Participants completed the behavioral tasks 30–35 min after alcohol consumption. At this time, participants’ BACs were measured using a breathalyzer, and BAC was also measured 60 min after administration when peak BAC (0.65 g/kg) was expected. All participants remained in the laboratory until their BAC fell below 20 mg/100 ml. Transportation home by taxi was provided after sessions. Participants were debriefed and paid for their participation following their final session.

2.4. Data Analyses

To confirm that the measures of disinhibition and motor impairment were sensitive to the effects of 0.65 g/kg alcohol, paired-samples t tests compared scores following alcohol and placebo. Hierarchical regression tested the degree to which CEP and alcohol responses predicted drinking habits. In step one of the hierarchical regression model, CEP, sensitivity to alcohol-induced disinhibition and motor impairment were entered as factors predicting drinking habits. Step two tested the degree to which all possible two-way interactions accounted for additional variance in drinking habits. Step three tested the degree to which the three-way interaction (i.e., CEP x motor impairment x disinhibition) accounted for additional variance in drinking habits. All analyses were tested using a significance threshold of p < 0.05.

To interpret significant three-way interactions, simple slopes were tested using the Process MACRO (Hayes, 2013) Model 3 in SPSS. Simple slopes tested the strength of relationships between CEP, acute alcohol effects, and drinking habits at high and low values of sensitivity to motor impairment and disinhibition. All other analyses were completed using SYSTAT13. Normality of all key variables was confirmed by analyses of skewness and kurtosis, which obtained values that did not violate the assumption of normality for (±2 skew, ±7 kurtosis; West et al., 1996). Outlier analyses were conducted, and no outlying values had a significant influence on the relationships of interest, therefore all subsequent analyses include these values.

3. Results

3.1. Drinking Habits

The sample reported a mean drinking frequency of 2.2 days per week (SD = 1.1), and a mean weekly dose of 1.6 mL/kg (SD = 0.5). The average dose per week was 2.6 mL/kg (SD = 1.8), which is akin to 11.6 standard drinks for an individual weighing 75 kg. Minimum and maximum values are reported in Table 1 and demonstrate marked individual differences in typical drinking habits.

Table 1.

Descriptive statistics of participants’ CEP and alcohol drinking habits (N = 94)

Variable Mean SD Minimum Maximum
Frequency 2.2 1.3 0.3 7.0
Dose per occasion (mL/kg) 1.2 0.5 0.2 2.4
Dose per week (mL/kg / week) 2.6 1.7 0.2 8.4
CEP 18.9 8.1 0.0 46.0
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Frequency, typical number of days alcohol consumed per week; Dose per occasion (mL abs alcohol/kg body weight), calculated based on number of standard drinks consumed during typical occasion and participants’ body weight; Dose per week, calculated by multiplying participants’ typical number of drinking occasions by their typical dose; CEP, cognitive and emotional preoccupation with alcohol.

3.2. Temptation and Restraint Inventory

The mean score on the CEP subscale of the TRI was 18.9 (SD = 8.1). Minimum and maximum values are reported in Table 1

3.3. Blood Alcohol Concentrations

The mean BAC 30 min after alcohol, at the time of task administration, was 69 mg/100 ml (SEM = 2.1). The mean peak BAC was 74 mg/100 ml (SEM = 1.2).

3.4. Acute Sensitivity

Figure 1A plots the mean time to complete the grooved pegboard task following placebo and alcohol beverages. The figure shows that alcohol increased time to complete the task compared with placebo. Results from a paired samples t test confirmed that participants took significantly longer to complete the task following alcohol (M = 55.1, SD = 7.4) than placebo (M = 51.8, SD = 6.2; t(93) = −7.5, p < 0.001).

Figure 1.

Figure 1.

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Measures under placebo (0.0 g/kg; dark gray box) and alcohol (0.65 g/kg; light gray box) dose conditions. Panel A reports mean pegboard completion times in seconds (s) for each condition. Panel B reports mean p-inhibition failures for each condition. The capped vertical lines show standard errors of the mean.

Figure 1B plots the mean p-inhibition failure following placebo and alcohol beverages. The figure shows that alcohol increased p-inhibition failure compared with placebo. Results from a paired samples t test confirmed that participants made significantly more inhibitory failures following alcohol (M = 0.14, SD = 0.14) than placebo (M = 0.08, SD = 0.11; t(93) = −4.7, p < 0.001).

3.5. Acute Alcohol Effects and CEP in Relation to Drinking Habits

Acute sensitivity to alcohol-induced disinhibition was calculated for each participant by subtracting their p-inhibition failures following placebo from their p-inhibition failures following alcohol. Acute sensitivity scores to motor impairment were calculated by subtracting participants’ time to complete the pegboard following placebo from their time following alcohol. Larger scores indicated greater sensitivity to the disinhibiting and motor impairing effects of alcohol, and sensitivity scores were z-transformed prior to regression analyses.

Results of hierarchical regression analyses predicting weekly dose and frequency are reported in Table 2. Regarding dose, main effects of CEP and motor impairment were obtained. Higher CEP and low sensitivity to alcohol-induced motor impairment (LM) predicted higher dose. Two two-way interactions and a three-way interaction were also obtained. Simple slopes analysis tested the highest level (three-way) interaction. Table 3 reports these results and shows that high CEP predicted higher dose only among participants with low sensitivity to both motor impairment (LM) and disinhibition (LD). Figure 2 plots the simple slopes of the three-way interaction. Panel 2A plots participants with LM. It shows that high CEP predicts heavier consumption among those who also showed LD. Panel 2B plots participants with high sensitivity to motor impairment (HM) and shows that CEP did not predict drinking regardless of sensitivity to disinhibition. Taken together, these results indicate that CEP was only predictive of drinking in individuals with low sensitivity to both the motor impairing and disinhibiting effects of alcohol.

Table 2.

β-coefficients and statistics obtained from hierarchical regression of CEP, sensitivity scores to motor impairment, disinhibition (i.e., p-inhibition failures) and their interaction at times predicting typical dose and frequency per week.

Model R2, Adj R2 b SE t P
Dose Per Week
Step 1 – Main Effects 0.18, 0.15
 CEP 0.62 0.20 3.15 0.002*
 Motor impairment −0.57 0.19 −2.99 0.004*
 Disinhibition 0.06 0.21 0.27 0.787
Step 2 – Two-way Interactions 0.25, 0.20
 CEP x motor impairment −0.49 0.21 −2.34 0.022*
 CEP x disinhibition −0.42 0.30 −1.39 0.168
 Motor impairment x disinhibition −0.49 0.24 −2.03 0.046*
Step 3 – Three-way Interaction 0.28, 0.22
 CEP x motor x disinhibition 0.61 0.31 1.96 0.054*
Typical Weekly Frequency
Step 1 – Main Effects 0.15, 0.12
 CEP 0.33 0.13 2.54 0.013*
 Motor impairment −0.36 0.13 −2.84 0.006*
 Disinhibition −0.09 0.14 −0.61 0.544
Step 2 – Two-way Interactions 0.17, 0.11
 CEP x motor impairment −0.21 0.14 −1.47 0.145
 CEP x disinhibition −0.16 0.20 −0.80 0.424
 Motor impairment x disinhibition −0.18 0.16 −1.12 0.265
Step 3 – Three-way Interaction 0.18, 0.12
 CEP x motor x disinhibition 0.20 0.22 0.93 0.353
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*

Indicates a value of p < 0.05.

Table 3.

Statistics obtained from simple slopes analysis of three-way interaction (i.e., CEP x Sensitivity to disinhibition x Sensitivity to motor impairment) predicting dose where β-coefficients report the relationship between CEP and dose at high and low values of sensitivity to disinhibition and motor impairment.

Sensitivity to Disinhibition Sensitivity to Motor Impairment b SE t P
Low Low 1.87 0.47 3.99 0.001*
Low High 0.35 0.29 1.19 0.236
High Low 0.17 0.41 0.44 0.664
High High 0.40 0.51 0.79 0.434
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Sensitivity to disinhibition: Low = −0.80 SD; High = +0.80 SD; Sensitivity to motor impairment: Low = −0.89 SD; High = +0.89 SD. R2 = 0.28, MSE = 2.44, F = 4.82, df = 7, 86.

*

Indicates a value of p < 0.05.

graphic file with name nihms-1900987-f0002.jpg

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Table 2 also reports the results of the same regression model used to predict drinking frequency. Main effects of CEP and motor impairment were obtained. Higher CEP and LM were associated with more frequent alcohol consumption. No main effect of sensitivity to disinhibition and no significant interactions were obtained.

4. Discussion

This study tested the degree to which a cognitive marker (CEP) predicted consumption of higher alcohol doses in the presence and absence of two well-documented alcohol response markers associated with heavier drinking (i.e., tolerance to motor impairment, high sensitivity to disinhibition). We found that the strength of the relationship between CEP and heavier drinking may depend on drinkers’ acute alcohol responses. More specifically, drinkers who expressed both alcohol response markers (LM and HD) reported higher doses regardless of their level of CEP. Whereas, for drinkers who expressed low sensitivities, higher CEP was associated with larger quantities of consumption. LM functioned as a stand-alone marker of higher dose and greater frequency of drinking.

A combination of LM and HD predicted higher typical dose, which is consistent with our previous research showing that this combination of alcohol responses have at least additive and over-additive predict heavier drinking (Allen et al., 2022). A combination of HD and LM may encourage heavier consumption such that drinkers experience impulsivity while maintaining the physical ability to act, allowing for the continuation of a drinking occasion (Allen et al., 2022). Moreover, our current findings suggest that this combination of acute alcohol responses may be sufficient to promote consumption of larger doses even in the absence of cognitive markers of problem drinking.

One explanation for the finding that high CEP predicted higher doses in the low alcohol sensitive drinkers is that CEP might contribute to the relationship between low level alcohol responses and heavier drinking. Cognitive markers, such as CEP might operate to increase drinking early on, and over time increased alcohol consumption results in the development of tolerance to its acute effects. Once initiated, these drinkers will need to consume greater quantities of alcohol over time to reach their desired level of intoxication. Such possibilities raise important questions about the temporal relationships between markers and the possibility that they do not emerge simultaneously or independently of each other. Future research is needed to elucidate the time course of markers including the potential for cognitive markers to influence the later expression of alcohol response markers. The cross-sectional nature of this study precluded directly testing these relationships, however the finding that CEP predicted higher doses only among those with low-level alcohol responses warrants future longitudinal research to clarify these relationships

In addition to its cross-sectional design, there are other limitations of this study that should be addressed. The validity of self-report measures depends on participants’ ability to accurately estimate their drinking habits and retroactively identify cognitive patterns. Related to this, the current sample reported relatively low levels of CEP overall. This is consistent with other studies which report similar levels of CEP among young adults without AUD (e.g., Bramm et al., 2013; Lyvers et al., 2014; Rose & Grunsell, 2008). However, among higher risk populations (i.e., AUD, DUI offenders,) CEP scores are markedly higher (Cohn et al., 2014; Miller & Fillmore, 2014). The limited range in this study likely reflect participants’ relatively limited drinking histories and low levels of AUD symptomology. Therefore, future research is needed to determine if these findings generalize to high-risk populations who tend to report greater levels of CEP. Additionally, our sample size precluded testing sex differences in the relationships between CEP, acute response markers, and drinking habits. Sex differences have been reported in sensitivity to alcohol-induced motor impairment and disinhibition (e.g., Wang et al., 2003; Miller et al., 2009; Weafer & de Wit, 2014), so it is possible that the combinations of markers that best predict at-risk drinking might differ based on sex.

Despite its limitations, the current study sought to answer novel questions about the co-expression of cognitive and alcohol response markers, which traditionally have been separate lines of research. Our findings underscore the importance of understanding how the expression of certain cognitive characteristics and alcohol responses may be a consequence of drinking history, a marker of problem drinking, and possibly contribute to escalated alcohol consumption.

Highlights.

  • Co-expression of cognitive and alcohol response markers linked to drinking habits

  • High disinhibition and low motor impairment associated with heavier drinking

  • This was true even in the absence of cognitive and emotional preoccupation (CEP)

  • For drinkers with low sensitivities, higher CEP was linked to heavier drinking

  • CEP may increase drinking and contribute to later expression of response markers

Role of funding source

This research was supported by the National Institute on Alcohol Abuse and Alcoholism Grant T32 AA027488. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institute on Alcohol Abuse and Alcoholism or the National Institutes of Health.

Footnotes

Conflict of Interest

The authors have no conflicts of interest to declare.

CRediT authorship contribution statement

Holley C. Allen: Conceptualization, Data curation, Formal analysis, Writing – Original draft preparation, Visualization.

Mark T. Fillmore: Conceptualization, Methodology, Writing – review and editing, Supervision.

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