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Published in final edited form as: Addict Behav. 2021 Aug 10;124:107083. doi: 10.1016/j.addbeh.2021.107083

Emotion-Based Decision-Making as a Predictor of Alcohol-Related Consequences in College Students

Lauren M McClain a, Brittney A Hultgren a, Irene M Geisner a, Kimberly A Mallett b, Rob Turrisi b, Mary E Larimer a
PMCID: PMC8565452  NIHMSID: NIHMS1736768  PMID: 34464916

Abstract

Student drinking during the college years can result in many adverse outcomes. Emotion-based decision-making (EBDM), or the use of emotional information to influence future plans and behavior, may lead to increased harmful consequences of alcohol. The current study examined both the number of types and total frequency of alcohol consequences as a function of EBDM. Undergraduate students from three large universities (n=814) were assessed on EBDM and typical weekly drinking during their 2nd year of college, and alcohol-related consequences during their 2nd, 3rd, and 4th years. Alcohol-related consequences were operationalized both as unique types of consequences and total consequences experienced in the previous year. Latent growth modeling used EBDM in year 2 to predict unique and total alcohol consequences in years 2, 3, and 4. Students who endorsed higher levels of EBDM experienced a significantly increased total frequency of consequences over the three years, without differences in trajectory between students high and low on this construct. Participants with higher levels of EBDM experienced a significantly greater number of unique consequences at all time points, but these consequences increased at a significantly lower rate than individuals lower on this construct. Findings of this study indicate Emotion-Based Decision-Making may be a useful predictor of harmful consequences of student drinking over time.

Keywords: Emotion-Based Decision-Making, Alcohol Use, Alcohol-Related Consequences, College Student Drinking

1. Introduction

Despite the number of intervention programs that have been developed and utilized on college campuses, college students continue to experience many negative alcohol-related consequences, including lost productivity, alcohol dependence, and death (Hingson, Zha, and Smyth, 2017). Prior research suggests the association between amount of alcohol consumed and experience of alcohol-related consequences is only moderate (Prince, Pearson, Bravo, & Montes, 2018), indicating much of the risk for experiencing these consequences remains unexplained. Investigation of additional factors that contribute to alcohol-related consequences is needed to improve prevention efforts (Perkins, 2002, Prince et al., 2018).

There is a growing body of literature showing both affect and decision-making factors are associated with drinking and alcohol-related consequences (Cooper, Frone, Russell, & Mudar, 1995; Park & Levenson, 2002, Shim & Maggs, 2005; Bø, Billieux, & Landrø, 2016). However, the degree to which individuals tend to incorporate emotions in their general decision-making has yet to be assessed as a predictor of alcohol-related consequences. Emotion-based decision-making may be an important individual difference to target within alcohol interventions, and/or may serve as a moderator of intervention efficacy. The current study aimed to address this gap by assessing the longitudinal association between emotion-based decision-making and alcohol-related consequences in college students.

1.1. The Role of Emotion in Alcohol-Related Consequences

Early research on emotions and alcohol use led to the development of the tension reduction hypothesis, which posits alcohol is reinforcing as a result of reducing tension and stress (Conger, 1956). Research on tension reduction led to investigation of the role of other negative reinforcement factors in experiencing alcohol-related consequences, such as the broader field of drinking motives research (Cooper et al., 1995; Kuntsche, Knibbe, Gmel, & Engels, 2005). Alcohol-related consequences can vary widely based on motives for drinking (Ham & Hope, 2003), and the motive of drinking to cope is particularly associated with increased negative outcomes (Merrill & Read, 2010) and increased negative affect over time (Armeli, Sullivan, & Tennen, 2015). This could contribute to a vicious cycle of worsening mood leading to increased alcohol use and alcohol-related problems (Holahan, Moos, Holahan, Cronkite, & Randall, 2001). Drinking to cope may be particularly harmful because it represents a lack of more beneficial and adaptive coping strategies, as well as possible deficits in emotion regulation.

Emotion regulation is the ability to modify the intensity or duration of aversive affective experiences through the use of regulatory skills and strategies (Linehan, 1993). Research suggests some individuals use alcohol consumption as a means to regulate social and affective experiences (Klanecky, Ruhnke, & Meyer, 2019; Hull & Slone, 2004). Previous studies have shown deficits in emotion regulation for individuals with alcohol use disorders, most particularly in the presence of negative emotionality (Bradizza et al, 2017). These results indicate at least some individuals use alcohol in a problematic manner as a result of insufficient healthy coping strategies for managing emotional distress. Additionally, it has been shown that increasing the ability to regulate affect through targeted training can improve outcomes for individuals in treatment for alcohol use disorders (Stasiewicz, Bradizza, & Slosman, 2018). If adaptive internal regulatory strategies are not available, individuals may utilize external strategies such as consuming alcohol in order to attempt to regulate their emotional states, increasing their risk of alcohol-related consequences.

While prior research has investigated how emotion, particularly negative emotionality, can relate to alcohol use, the current study extends this work by investigating emotion-based decision-making and its relation with alcohol consumption and consequences. Emotion-based decision-making is an individual difference factor that describes the tendency to use emotions rather than logic to guide plans and behavior (Barchard, 2001). Emotion-based decision-making is the ability to understand and incorporate internal emotional information to influence both thinking and actions. Individuals with higher levels of this construct prioritize their emotions as drivers of their actions and future goals, as opposed to pure logic and rationality. Emotion-based decision-making is most commonly assessed with the Iowa Gambling Task, which provides an experiential measure of how individuals incorporate emotional information into their decisions (Bechara et al., 1994). Impairments in performance on the Iowa Gambling Task are frequently seen in individuals with heavy alcohol use (Dom et al., 2006; Kovács et al., 2017) and can predict later heavy alcohol use (Goudriaan, Grekin, & Sher, 2011). However, the Iowa Gambling Task does not capture the degree to which individuals consciously incorporate emotional data into their decision-making process. Thus, to expand upon this research, the current study assesses emotion-based decision-making through individuals’ self-perception of the degree to which they prioritize emotions in making their decisions. This construct is distinct from negative and positive urgency (Cyders & Smith, 2007), as incorporating emotional content into decision-making does not necessarily imply impulsive action. Individuals with higher levels of emotion-based decision-making may be more sensitive to affect regulation deficits and thus potentially more likely todrink to cope with negative emotions. As they have increased likelihood of using emotional content in decision-making, and thus may experience increased risks in the presence of negative emotions. Emotion-based decision-making may represent a mechanism through which negative emotionality and inability to regulate affect may lead to increased alcohol-related consequences. The current research attempts to further investigate this possibility by using emotion-based decision-making as a predictor of future negative consequences, over and above the contribution of alcohol consumption, in a longitudinal study of college students. We hypothesized that individuals higher in emotion-based decision-making would experience greater consequences from alcohol use across three annual assessments, controlling for their initial level of drinking.

2. Method

2.1. Participants

The sample was comprised of 814 students recruited from three large public universities in the northwest, northeast, and southern United States, collected as part of a larger study of parent-student dyads. Students were eligible to participate if they were in their freshman year of college, enrolled full-time, between the ages of 18–19, and had a parent or guardian who could also complete study measures. Participants’ relevant demographic characteristics are summarized in Table 1.

Table 1.

Demographics

n %
Sex
 Male 326 39.7
 Female 495 60.3
Race
 Caucasian/White 612 74.5
 Black or African American 36 4.4
 Asian 105 12.8
 Multiracial 40 4.9
 Native American 2 0.2
 Native Hawaiian or Pacific Islander 2 0.2
 Other 24 2.9
Ethnicity
 Hispanic 110 86.1
 Non-Hispanic 707 13.5
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2.2. Procedures

Students who met eligibility criteria for year in school, age, and full-time enrollment were selected randomly from a list obtained from the registrar’s database on each campus and invited to participate in the research study (N=5226). Potential participants received an invitation letter by both mail and email explaining the general purpose and procedures of the study, compensation ($30 per assessment), and a URL link and ID number with which to access the first questionnaire. Participants also received a maximum of six e-mail reminders to access the baseline survey. Baseline measures (T1) were completed by 2,320 students (44.4% response rate). Of those, 1431 students (61.7%) were eligible for continuation in the study based on having at least one parent or guardian consent to and complete study measures, since the original study investigated the effects of certain parental factors on student alcohol use. Eligible participants were invited to complete 3 additional annual surveys. There was a 90.4% retention rate at T2 (n=1294), at T3 a 94.7% retention rate from T2 and 85.6% retention rate from T1 (n=1226), and at T4 an 84.3% retention rate from T1 and a 93.0% retention rate from T3. A greater proportion of males were lost to attrition at T2, T3, and T4 (T2: χ2 (1, N=1431) = 10.50; p < .01; T3: χ2 (1, N=1431) = 21.24; p < .001); T4: T4 (χ2 (1, N = 1431) = 19.05; p < .001). There were no significant differences in race or ethnicity between those who completed surveys or did not complete at T2, T3, or T4. T-tests were performed to determine potential differences in drinking between participants lost to attrition and participants who completed T2, T3, or T4. Male and female participants were analyzed separately as males typically report higher drinking. No differences were found for males comparing completers and participants lost to attrition in weekly drinking based on the Daily Drinking Questionnaire (Collins, Parks, & Marlatt, 1985), estimated peak Blood Alcohol Content (BAC), or “binge” drinking (4+ drinks for women/5+ for men at least once in the past month). Female participants lost to attrition at T4 reported higher weekly drinking at T1 (mean difference = 1.92; t(116) = 2.26; p = .03) but did not differ on peak BAC or binge drinking. The local institutional review boards at each university approved all study procedures.

2.3. Measures

The current study utilized a planned subsample (n=814) of study participants who received the emotion-based decision-making measure during their second year (T2). Emotion-based decision-making at T2 was then used to predict development of alcohol-related consequences during second (T2) third (T3) and fourth (T4) years.

2.3.1. Demographics.

Participants reported their sex (male=1, female=0), race, and ethnicity (Non-Hispanic=0, Hispanic=1). Race was dichotomized as white (0) or nonwhite (1), as some racial categories contained extremely few participants. These demographic variables were included as covariates in all analyses.

2.3.2. Typical weekly drinking.

Participants indicated how many alcoholic beverages they drank on each day of a typical week in the prior month using the Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985). An alcoholic beverage was defined as 12 oz. beer, 10 oz. wine cooler, 4 oz. wine, or 1 oz. 100 proof (1 ¼ oz. 80 proof) liquor. Daily drinks were summed to create an index score of typical weekly drinking at T2.

2.3.3. Emotion-based decision-making (EBDM).

Students were assessed on emotion-based decision-making at T2 with a 10-item scale (Barchard, 2001) rating their level of agreement from “strongly agree” to “strongly disagree.” Five of the scale items were reverse coded, and all scale items were summed to give a composite score, where higher composite scores indicated higher emotion-based decision-making. Examples of scale items include “I listen to my heart rather than my brain” and “I listen to my feelings when making important life decisions” and a reverse coded item “I plan my life logically.” Evans and Barchard (2005) reported this scale demonstrated adequate reliability and validity (α = 0.77, test-retest reliability = 0.70). The current dataset had a Cronbach’s α of 0.84.

2.3.4. Alcohol-related consequences.

The measure of alcohol-related consequences was a 39-item questionnaire adapted from multiple scales, with 35 items from the Young Adult Alcohol Consequences Questionnaire (YAACQ; Read et. al., 2006) which asked participants how often they had experienced specific consequences from drinking in the past year (0- No or not in the past year, to 11, Eleven or more times in the past year). Additionally, 4 items were adapted from the sex-related alcohol negative consequences subscale of the Young Adult Alcohol Problems Screening Test (YAAPST; Hurlbut & Sher 1992) to assess unwanted sexual consequences as a result of drinking, as has been done in prior studies (Larimer, Lydum, Anderson, & Turner, 1999; Wood, Read, Palfai, & Stenvenson, 2001). Both total frequency of alcohol-related consequences and number of unique types of consequences at T2, T3, and T4 were analyzed, as prior research has indicated that individuals who experience alcohol-related consequences across multiple categories are also likely to have multiple instances of the same types of consequences (Mallett et al., 2011). The unique consequences variable was constructed by binary coding of whether each consequences on the list had been experienced at all (maximum of 39). Total consequences were calculated from summing the reported frequency of each item. For items with a range (e.g. 1–2 times in the past year) the average of the range was taken before adding to the total, and the “11 or more” item was coded as 11.

2.4. Data Analytic Plan

Descriptive statistics and correlations were performed using SPSS version 27. Zero-order correlations between all variables can be seen in Table 2. To assess the association between EBDM and alcohol-related consequences across the three time points, latent growth models (LGMs) were performed using Mplus Version 8.6. Latent growth models are a class of models appropriate for longitudinal data with three or more time points, allowing for the assessment of relationship between time and other variables, as well as estimating differences in trajectories of change based on the covariates in the model. First, continuous predictors (i.e., weekly drinking and EBDM) were mean-centered prior to analyses. Next, a linear latent growth model was utilized to assess the association between EBDM and the initial value (intercept) and change over time (slope) of total frequency of alcohol-related consequences. A linear latent growth model using negative binomial was utilized to assess the association between EBDM and the initial value and change over time for unique alcohol-related consequences, with a log-link predicting latent intercepts and slopes, since the outcome was a discrete count variable (Greenwood et al., 2019; van der Ness et al., 2020). Given that there were only three time points available for analysis, a linear model was estimated for both models. Both models included sex, race, ethnicity, and weekly numbers of drinks as assessed at T2 as covariates. Finally, we explored the potential interaction effect between drinking and EBDM on both unique and total consequences. The χ2 statistic, comparative fit index (CFI), and root mean square error of approximation (RMSEA) determined the fit of the total consequences model. CFI and RMSEA are not provided for count models. The χ2 difference test for nested models was used to determined if model fits improved with the addition of EBDM and the interaction term between typical drinking and EBDM. .

Table 2.

Bivariate Correlations among All Study Variables.

Race Ethnicity Weekly Drinking (T2) EBDM Unique Consequences (T2) Total Frequency (T2) Unique Consequences (T3) Total Frequency (T3) Unique Consequences (T4) Total Frequency (T4)
Sex −0.058* −0.129** 0.179** −.166** 0.019 0.040 0.031 0.053 0.054 0.065*
Race 0.064* −0.153** 0.021 −0.133** −0.100** −0.132** −0.102** −0.148** −0.135**
Ethnicity −0.179** 0.044 −0.156** −0.143** −0.160** −0.148** −0.154** −0.148**
Weekly Drinking (T2) 0.005 0.642** 0.658** 0.571** 0.566** 0.531** 0.539**
Emotion Based Decision-Making 0.151** 0.104** 0.113** 0.078* 0.116** 0.083*
Distinct Alcohol-Related Consequences (T2) 0.890** 0.719** 0.668** 0.682** 0.646**
Total Frequency of Consequences (T2) 0.717** 0.617** 0.668**
Distinct Alcohol-Related Consequences (T3) 0.878** 0.726** 0.689**
Total Frequency of Consequences (T3) 0.633** 0.698**
Distinct Alcohol-Related Consequences (T4) 0.878**
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*

p < .05

**

p < .01.

3. Results

The latent growth model assessing the association between EBDM and total number of consequences was determined to have excellent fit (χ2 = 2.122, 7 df, p =0.953; CFI = 1.00; TLI = 1.00; RMSEA < 0.001). For total consequences Additionally, the model including EBDM was a fit significantly better fit to the data than the model without EBDM (χ2 = 24.944 2 df, p < 0.001), and the model with including the interaction term fita better fit to the data than the model without the interaction term additionally also significantly improved fit (χ2 = 6.375, 2 df, p < 0.05). For unique consequences, the model including EBDM fit significantly better than the comparison model without EBDM (χ2 = 8.754, 2 df, p < 0.05). However, the addition of the interaction term between drinking and EBDM did not significantly improve fit (χ2 = 1.189, 2 df, p = 0.552) The intercepts and slopes for total and unique alcohol consequences with covariates are provided in Table 3. Given that the purpose of the analysis was to assess the effect of EBDM on alcohol-related consequences, all inferences were drawn from the conditional model (including covariates). The slopes reported are all linear slopes. Both total frequency and unique consequences increased over time.

Table 3.

Emotion-Based Decision-Making as a Predictor of Alcohol Consequences at Time 2, Time 3 and Time 4

Total Frequency of Consequences
Intercept Slope
Estimate SE p Estimate SE p
Sex −4.654 2.070 0.025 −0.158 1.265 0.901
Race −0.456 2.353 0.846 −2.238 1.469 0.128
Ethnicity −3.087 1.656 0.062 −0.219 1.617 0.892
Weekly Drinking (T2) 2.875 0.195 <0.001 −0.116 0.120 0.333
Emotion-Based Decision-Making 0.643 0.158 <0.001 0.012 0.095 0.898
EBDMxDrinking 0.083 0.035 0.018 −0.005 0.015 0.758
Unique Alcohol-Related Consequences
Intercept Slope
Estimate SE p Estimate SE p
Sex −0.300 0.127 0.018 0.113 0.049 0.021
Race −0.482 0.158 0.002 0.024 0.055 0.664
Ethnicity −0.279 0.207 0.179 −0.029 0.081 0.725
Weekly Drinking (T2) 0.122 0.009 <0.001 −0.023 0.003 <0.001
Emotion-Based Decision-Making 0.058 0.011 <0.001 −0.010 0.004 0.006
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Sex was coded as female(0) or male(1), race was coded as white(0) or nonwhite(1), ethnicity was coded as non-hispanic(0) or hispanic(1)

For total frequency of consequences, emotion-based decision-making had a significant positive impact on the intercept at all time points, (fig. 1) but not on the slope. This indicates that EBDM affects the starting level of consequences, but the rate of change for individuals high and low on this construct did not differ. Individuals higher in EBDM had higher total frequency of consequences at T2, and these consequences increased at a similar rate to their peers, such that the individuals highest in EBDM had the highest rates of total consequences at all time points. While individuals low in EBDM had increasing consequences at a similar rate to those high on the construct, at all time points they had fewer consequences than participants higher on EBDM. Sex had a significant negative effect on the intercept only, such that males had lower initial levels of consequences. Weekly drinking had a significant positive effect on the intercept of total consequences such that individuals with higher alcohol consumption had higher initial levels of consequences, but similar to EBDM, weekly drinking at T2 was not associated with the trajectory of consequences over time. There was a significant interaction between EBDM and drinking on the intercept, such that individuals with higher EBDM and drinking had further higher intercept values, over and above the effects of each variable alone.

Figure 1.

Figure 1

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Changes in total consequences over time

For unique consequences, emotion-based decision-making had a significant positive impact on the intercept (fig. 2), and a significant negative impact on slope. For this model, individuals high in EBDM had higher initial levels of unique alcohol-related consequences, but their number of unique consequences increased at a lower rate than individuals lower on this construct. Importantly, their reported number of unique consequences at T4 were still higher than individuals lower on this construct, but they did not increase as rapidly across the 3 time points as did those lower in EBDM. Sex had a significant negative effect on the intercept and significant positive effect on slope, such that males had lower initial rates of unique consequences but their consequences increased more rapidly than females. Race had a significant negative effect on the intercept only, such that non-white individuals had lower initial levels of consequences. Weekly drinking had a significant positive effect on intercept of number of unique consequences and a significant negative effect on slope such that individuals with higher alcohol consumption at T2 had higher initial levels of consequences but increased at a lower rate over time than those with lower initial weekly drinking levels.

Figure 2.

Figure 2

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Changes in unique consequences over time

4. Discussion

The current study was designed to evaluate the extent to which emotion-based decision-making (EBDM) influences the development of future alcohol-related negative consequences among college students. The overall pattern of results supports the hypothesis that EBDM is predictive of more alcohol consequences up to two years later even after accounting for T2 alcohol use and demographic factors, and therefore this construct may be useful for identifying individuals at increased risk for problematic alcohol consequences within a college student population.

Individuals with a greater tendency towards emotion-based decision-making had higher alcohol-related consequences overall, indicating that EBDM may be a more general risk factor affecting decision-making in the context of drinking. This construct does not appear to influence trajectories of development of alcohol problems at the college level, but rather individuals higher on this construct start at a higher level of consequences and then increase at a similar rate to their peers lower on this construct. These findings suggest EBDM is a relatively stable predictor of higher alcohol-related negative consequences over time and may be a useful for identifying those at risk who would benefit from intervention. Given the interaction between EBDM and alcohol use, the combination of these factors might be especially useful in identifying individuals at significantly higher risk of negative outcomes.

With respect to the number of unique consequences reported, individuals with higher levels of EBDM had higher rates of unique consequences initially and across all time points, however the rate of increase in number of unique consequences was lower for individuals higher in EBDM compared to those lower in EBDM. Weekly drinking at T2 showed a similar pattern of predicting more unique consequences at T2 but a lower rate of increase in unique consequences across time. It is possible both findings represent a ceiling effect in possible growth on this outcome, given that the unique consequences outcome has less possible variability than the total frequency of consequences. Nonetheless, individuals higher in EBDM still had higher unique consequences across all time points than individuals lower on the construct, and thus EBDM may still be useful to assess as a predictor of later problems. Further research, especially with additional assessments and over longer time range, is needed to clarify the relation between EBDM and unique consequences, and to replicate or otherwise better understand this unexpectedsomewhat surprising finding.

One possible explanation for these findings may be that if these individuals use drinking as a means of regulating negative affect, EBDM could lead to coping motives for alcohol use, which are associated with increased consequences from drinking (Holahan et al., 2001). Emotion-based decision-making could be driving increased overall consequences simply due to alcohol being part of an individual’s emotion regulation behavioral repertoire, and emotional distress would then trigger drinking in contexts leading to higher consequences, or potentially influence this subset of individuals to engage in higher risk taking in the presence of alcohol. This might be similar to how drinking to cope predicts higher consequences even controlling for the effects of alcohol use. It is also possible that EBDM and coping motives might interact, such that individuals high in EBDM who drink to cope might experience dramatically higher consequences. Additionally, students high in EBDM may be more susceptible to positive emotions as well, and therefore may be likely to drink more on occasions that would enhance their positive emotions, such as times of celebration. Such enhancement motives for drinking are also associated with increased alcohol consumption and consequences (Merrill, Wardell, & Read, 2014; Stevenson et al., 2019). Increased emotion regulation (both positive and negative) and coping skills might decrease the potential negative effects of emotion-based decision-making by providing resources to decrease negative affect which are not dependent on drinking, or by encouraging healthy alternatives for increasing positive emotions without alcohol. If EBDM is stable over time but its effects on alcohol consequences can be mitigated through interventions, it may be useful in screening for high-risk individuals to provide preventative interventions. Future studies on this construct should also include evaluation of other drinking motives, in order to clarify whether the negative impacts of EBDM are associated primarily with drinking to cope or are equally associated with enhancement motives.

Results from this study could be a basis for further investigation into EBDM and related constructs, which in turn might inform the creation of targeted interventions for college students high in emotion-based decision-making. Such individuals could potentially benefit from interventions aimed at enhancing self-regulation and coping skills in the presence of strong affect, such as mindfulness or elements of Dialectical Behavior Therapy (DBT). Emotion-based decision-making, in capturing the inclusion of both positive and negative emotions in decision-making, may be particularly connected to consequences as a result of high emotion precipitating impulsive decision-making. Thus, using emotion regulation interventions such as techniques from DBT may be particularly beneficial to these individuals through facilitating alternate methods of coping with negative emotions or regulating behavior in the presence of positive emotion.

This study was subject to limitations which should be addressed by future research. First, all data were based on self-report, which could be biased due to factors such as memory or intentional over- or under-reporting. Steps to improve validity of self-report in this study included using well-validated and reliable measures of alcohol use and consequences, providing assurances of confidentiality of reports, obtaining a certificate of confidentiality to protect data from subpoena, and assurances of no consequences for accurately reporting information on sensitive issues. These steps have typically been shown to improve reliability and validity of self-reported alcohol and substance use (Werch, 1990; Bates & Cox, 2008; Cohen & Vinson, 1995). To complement self-report, future work with this construct could compare self-reported EBDM with Iowa Gambling Task performance as predictors of subsequent alcohol-related consequences.

Another limitation involves inability to evaluate the role of emotion-based decision-making in the context of other related constructs such as negative affect, impulsivity, or distress tolerance. It is possible there may be overlap between emotion-based decision-making and these related constructs, so additional research is needed to assess how these may function differently in relation to drinking and alcohol-related consequences, and if interactions are present. Assessing impulsivity may be especially important: individuals who are high in impulsivity have higher alcohol consequences compared to those lower in the trait (Simons, Carey, & Gaher, 2004) and greater willingness to engage in impulsive alcohol consumption is also related to increased alcohol-related consequences (Wicki et al., 2018; Mallett et al., 2015). In particular, the negative urgency aspect of impulsivity should in future be assessed relative to this construct. Negative urgency, or acting impulsively in the presence of negative affect (Cyders & Smith, 2007), has in past studies been associated with increased alcohol-related consequences (Karyadi & King, 2011). It is also possible that impulsivity or low distress tolerance may interact with emotion-based decision-making and negative affect to increase risk for alcohol-related consequences. Future research is needed to evaluate emotion-based decision-making in the context of these related constructs to better address these questions. An additional limitation of this research is the lack of assessment of drinking motives in this study. Since there is a significant emotional aspect to drinking motives, particularly for the motives of enhancement (positive emotionality) and coping (negative emotionality), the possible interaction of these factors with EBDM may be an important future direction for this work. Finally, these results cannot generalize beyond a college student population.

Despite limitations, the current research identifies EBDM as another potentially modifiable factor leading to alcohol-related negative consequences and has implications for prevention and intervention approaches to reduce harmful consequences of drinking. Future directions of this research might involve examination of whether the effects of EBDM on consequences differ in the presence or absence of significant stressors, such as academic difficulties, social stressors, or mood or anxiety symptoms. At this point, it is also unclear whether, in the absence of intervention, EBDM is a stable trait or changes over time, and whether maturational effects might cause it to decrease. These questions would be important to understand in order to better develop appropriate intervention approaches and understand optimal timing of such approaches.

  • Student drinking during college can lead to significant negative consequences.

  • Emotion-based decision-making (EBDM) predicts negative consequences from alcohol.

  • Students with higher EBDM experienced more unique consequences from alcohol.

  • Students with higher EBDM experienced greater frequency of alcohol consequences.

Statement 1: Role of Funding Sources

Funding for this study was provided by NIH grant R01-AA012529. NIH had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication. Support for preparation of this manuscript was also provided by T32-AA007455–36.

Footnotes

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Statement 3: Conflict of Interest

All authors declare that they have no conflicts of interest to disclose.

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