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. Author manuscript; available in PMC: 2021 Feb 1.
Published in final edited form as: Psychol Addict Behav. 2019 Jun 20;34(1):175–181. doi: 10.1037/adb0000471

Intelligence moderates the relationship between delay discounting rate and problematic alcohol use

Allen J Bailey 1, Kyle Gerst 2, Peter R Finn 3
PMCID: PMC6923633  NIHMSID: NIHMS1026659  PMID: 31219266

Abstract

Background:

Individuals with problematic alcohol use discount larger delayed rewards at higher rates relative to smaller immediate rewards compared to healthy controls. Lower executive function ability, including lower general intelligence (IQ), is associated with both high delay discounting rates and more lifetime alcohol-related problems. Although, problematic alcohol use, delay discounting rates, and IQ are all significantly associated, we know little about the nature of their inter-relationships. This study tests the hypothesis that IQ moderates the association between delay discounting rates and measures of problematic alcohol use.

Methods:

Lifetime alcohol-related problems, drinking levels over the past 2 weeks, IQ, and delay discounting were assessed in a sample of 617 young adults (303 female).

Results:

Higher delay discounting rates were associated with more lifetime alcohol problems, more recent alcohol use, and lower IQ. However, analyses also revealed that IQ moderated the association between delay discounting rates and lifetime alcohol problems as well as high levels of recent alcohol use. Delay discounting rates were more strongly associated with both lifetime alcohol problems and higher levels of recent alcohol consumption for those with higher IQ compared with those with lower IQ.

Conclusion:

Results indicate that discounting rewards at higher rates may indicate an important risk factor for problematic alcohol use in high IQ individuals, whereas this association may be blunted in low IQ individuals due to their uniformly elevated discounting rates and higher problematic alcohol use.

Keywords: delay discounting, decision-making, alcohol use disorder, intelligence, alcohol use

Introduction

Individuals with problematic drinking patterns and/or Alcohol Use Disorder (AUD) have higher delay discounting rates, reflected in a more pronounced preference for immediately available smaller rewards over larger delayed rewards compared to healthy controls (Bailey, Gerst, & Finn, 2018; Bjork, Hommer, Grant, & Danube, 2004; Bobova, Finn, Rickert, & Lucas, 2009.; Field, Christiansen, Cole, & Goudie, 2007; Finn, Gunn, & Gerst, 2015; Mitchell, Fields, D’Esposito, & Boettiger, 2005; Myerson, Baumann, & Green, 2016.; Petry, 2001a; Vuchinich & Simpson, 1998). Studies have also demonstrated an association between higher delay discounting rates and increased severity of problematic alcohol use (Bobova et al., 2009; Finn et al., 2015; Kollins, 2003; MacKillop et al., 2010; Takahashi, Ohmura, Oono, & Radford, 2009). This preference for immediate rewards, reflected by a high delay discounting rate, has been hypothesized to be the result of an overly active approach system that is thought to underlie poor self-regulation in those with AUDs, which leads to choices that fail to maximize long-term positive outcomes (Bickel & Marsch, 2001). Furthermore, this preference appears consistent with the excessive drinking behavior of individuals with an AUD, who choose smaller immediate rewards (e.g. enjoyment of intoxication) in lieu of larger long-term positive life outcomes such as academic and career goals.

In addition to strong approach tendencies, the poor self-regulation in those with an AUD is associated with executive function deficits, including low IQ (Finn, 2002; Finn & Hall, 2004; Giancola & Mezzich, 2003; Tarter, Jacob, & Bremer, 1989; Wallace Deckel & Hesselbrock, 1996). Evidence both from clinical research (Bobova et al., 2009; Brown, Tapert, Granholm, & Delis, 2000; Finn et al., 2009; Mazas, Finn, & Steinmetz, 2000) and epidemiological studies (Mortensen, Jensen, Sanders, & Reinisch, 2006; Sjölund, Hemmingsson, & Allebeck, 2015) shows a small to moderate negative association between IQ and problematic drinking patterns. This indicates that lower intelligence may be an important risk factor for problematic drinking.

Studies also suggest a modest, but consistent association between delay discounting rates and IQ. A meta-analysis by Shamosh and Gray (2007) revealed that across 24 eligible studies there was a small to moderate negative association between discounting rates and IQ. Metcalfe and Mischel (1999) hypothesized that executive functioning, including general intelligence, may serve to regulate approach motivational systems. General intelligence may serve to regulate approach tendencies towards immediate rewards therefore increasing the probability of less impulsive performance on delay discounting tasks. Substantial empirical evidence supports this idea including studies revealing that lower executive functioning is associated with disinhibited performance on decision making tasks (Barry & Petry, 2008; de Wit, Flory, Acheson, McCloskey, & Manuck, 2007; Finn, et al, 2014; Fridberg, Gerst, & Finn, 2013) and a study by Frederick (2005) that showed higher intelligence was related to employing a more deliberate decision making strategy on delay discounting tasks.

Although problematic alcohol use, delay discounting rates, and IQ are all significantly associated, we know little about the nature of their relationships. For instance, IQ might mediate the association between alcohol use severity and higher discounting rates. IQ may also moderate the association, such that the association between discounting rates and alcohol use severity might be stronger for those with high IQ, and much weaker for those with low IQ. The reason being that for those with low IQ, alcohol use severity and discounting rates might be uniformly elevated, blunting the association between delay discounting rates and severity. It might be that for those with low IQ, alcohol use severity might be associated with a range of executive cognitive deficits and achievement related problems. While for those with high IQ who would have an overall higher executive cognitive capacity and higher achievement, alcohol problems might be more specifically associated with a tendency to make more choices that are impulsive. This study aims to test the hypothesis that IQ will moderate the association between delay discounting rate and alcohol use severity, as measured by lifetime alcohol problems and recent drinking quantity, such that the association between discounting rate and alcohol use severity will be stronger in high IQ individuals compared to low IQ individuals.

Methods

Sample characteristics

The sample consisted of 617 young adults (M age = 21.60, SD = 2.54), with a wide range of alcohol use and alcohol related problems. The sample was predominantly Caucasian/White 83.3%, 5.7% African-American/Black, 7.5% Asian, and 3.5% endorsing another ethnic group (e.g. multiracial, Native American). Table 1 lists sample characteristics broken down by IQ tertiles.

Table 1.

Sample Characteristics

Characteristic Low IQ Medium IQ High IQ
n (male/female) 206(105/101) 206(104/102) 205(105/100)
n (%) with Alcohol Use Disorder 143(69%) 133(65%) 110(53%)
Age
M (± SD)		 21.43(2.58) 21.54(2.48) 21.83(2.57)
Education in Years
M (± SD)		 13.83(1.67) 14.28(1.53) 14.68(1.62)
Intelligence Quotient (IQ)
M (± SD)		 102.03(6.25) 113.17(2.45) 122.69(4.36)
Delay Discounting rate (log10 k)
M (± SD)		 −1.28(1.01) −1.58(1.05) −1.79(.93)
Lifetime Alcohol problems
M (± SD)		 25.53(19.27) 21.26(19.13) 18.32(18.64)
# of Drinks in last 2 weeks
M (± SD)		 34.12(40.87) 35.64(37.13) 32.88(39.89)
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Low IQ = low IQ tertile, Medium IQ = medium IQ tertile, High IQ = high IQ tertile. Lifetime alcohol problem counts and AUD diagnosis status were assessed using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) interview (Bucholz et al., 1994). AUD diagnosis included individuals diagnosed with Alcohol Dependence/Alcohol Abuse using DSM-IV criteria or individuals with moderate/severe AUD using DSM-V criteria. Lifetime alcohol problems were the total number of positive responses to interview questions in the alcohol use disorder section related to adverse consequences from alcohol use. Total # of drinks was ascertained by summing reported alcohol consumption using a 2-week timeline follow back. IQ was measured with the Wechsler Abbreviated Scale of Intelligence (WASI).

Recruitment

Data for the current study was collected as part of three larger studies. The first study consisted of a single session ranging from 1 to 4 hours in duration and consisted of a diagnostic interview, a battery of self-report measures, and computer tasks (e.g. delay discounting) see Finn et al., 2015). Roughly 47% (n = 305) of the current sample was drawn from this study and participants were compensated at a rate for $10 per hour. The second study consisted of 4 sessions ranging from 1.5 to 3 hours and consisted of a wide variety of self-report measures, cognitive and decision-making tasks, and a diagnostic interview (see Bailey et al., 2018). Roughly 28% (n = 171) of the current sample was from this study. Participants in this study were also compensated at a rate of $10 per hour. Lastly, the third study consisted of 19 sessions ranging from 1 to 3 hours in duration. The study consisted of a battery of self-report measures, a diagnostic interview, and a set of repeated adaptive computerized cognitive tests meant to improve working memory (Gunn, Gerst, Wiemers, Redick, & Finn, 2018). Participants in this study were compensated at a rate of $12 an hour with incentive bonus for working memory improvement and on-time bonuses. Roughly 23% (n = 141) of the current sample was drawn from this study. Importantly, delay discounting rates from the third study were assessed before any working memory training. All three studies were approved by the Indiana University-Bloomington Institutional Review Board (IRB). Participants were recruited using flyers, advertisements in local newspapers, and business cards placed around the community, along with postings on the Indiana University-Bloomington student classifieds web page. The flyers and postings were designed utilizing the approach used by Finn and colleagues (Bobova et al., 2009; Finn et al., 2014) to prompt responses from individuals who vary in terms of levels of alcohol use and alcohol problems. The postings and flyers asked for “adventurous, daring” individuals, “impulsive individuals”, “heavy drinkers wanted for psychological research”, “more reserved and introverted type person”, “social drinkers”, persons who “got in a lot of trouble as a child” or “have trouble with the law and authority”, persons with “drinking problems”, and those who “drink modest amounts of alcohol” and “quiet reflective and introspective persons”.

Telephone screening interview

Those who responded to advertisements were screened via telephone to determine whether they met study inclusion criteria. Respondents who met study inclusion criteria could read and speak English, had at least a 6th grade education, did not report any history of severe head injuries, did not report a history of psychosis, had consumed alcohol on at least one occasion in their life, and were between ages 18 and 30. Participants were informed that they must abstain from using alcohol and other drugs for at least 12 hours before study sessions.

Test session exclusion criteria

Before every testing session participants were required to meet a set of criteria before proceeding. All participants were required to (1) have no self-reported use of drugs or alcohol within the past 12 hours prior to testing, (2) have gotten at least 6 hours of sleep the previous night, (3) have a breath alcohol level of 0.0% (tested with an AlcoSenor IV, Intoximeters Inc., St. Louis MO), and (4) not be experiencing symptoms of withdrawal or of any illness. Subjects were rescheduled if they did not meet this criteria.

Alcohol Use Measures

Lifetime alcohol problem counts and AUD diagnosis status were assessed using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) interview (Bucholz et al., 1994). Lifetime alcohol problems were the total number of positive responses to interview questions in the alcohol use disorder section related to adverse consequences from alcohol use. Quantity of alcohol use was ascertained by summing total reported alcohol use (in standard drinks) over the previous 2 weeks using a timeline follow back interview reviewing each day over the past 2 weeks.

Intelligence Measure

Intelligence Quotient (IQ) was measured using the full score of the Wechsler Abbreviated Scale of Intelligence – WASI (The Psychological Corporation, 1999). WASIs were administered by trained laypeople.

Delay Discounting Tasks

All delay discounting tasks in this study were administered via computer. Participants were presented with a choice between a specific amount of money “TODAY” or $50 “LATER” at one of six time delays (i.e. 1 week, 2 weeks, 1 month, 3 months, 6 months, 1 year). The immediate choice amount varied from $2.50 to $47.50 in $2.50 increments. Prior to doing the tasks, participants were informed that all money was hypothetical, but were instructed to choose as if they would receive their chosen value in the corresponding time delay. For this task, participants completed 6 blocks, one for each time delay (1 week, 2 weeks, 1 month, 3 months, 6 months, 1 year). Within each block there were ascending and descending value trials (both the order of the blocks and order of trial type was randomized). In the ascending trials, the immediate reward value began at $2.50 and then increased to a maximum of $47.50 in increments of $2.50. The ascending sequence of trials stopped when a participant switched from the delayed to the immediate reward value (or stopped at $2.50 if the immediate reward was chosen on the first trial). There was a total of 19 possible ascending trials for each of the 6 time delay lengths. The point at which participants switched from the delayed value ($50) to the immediate option was recorded as the switch point on the ascending trials. On the descending trials the immediate reward value began at $47.50 and decreased to a minimum of $2.50 in increments of $2.50. For the descending sequence trials, the task stopped when the participants switched from the immediate reward value to the delayed option. The point at which they switched from the immediate to the delayed option ($50) was recorded as their switch point for the descending sequence of trials. Again, there was a maximum of 19 possible trials in the descending sequence for each of the 6 time delay lengths. About half the participants received a shortened version of this task. The shortened version of the task was identical except immediately available options ranged from $5.00 to $45.00, and increased in the ascending trials and decreased in the descending trials by $5.00 increments. This makes for a maximum of 9 possible trials in each trial type (ascending/descending) at each of the 6 time delay lengths. This was done to reduce participant burden.

Estimation of discounting rate

A single-parameter hyperbolic function was used to estimate delay discounting rate (Mazur, 1987). The estimation of discounting rate was calculated using the following equation: Vp = V/(1 + k × dt), where Vp was the present (discounted/subjective) value (calculated as the average of the switch points for ascending and descending trials at a particular time delay), the constant V was the amount of the delayed reward ($50), dt was the length of the time the reward or loss is delayed in days, and k is the discounting rate. The estimated k values of each participant was log10 transformed and this transformed k was used in the subsequent analyses. The use of this hyperbolic model is a well-established approach to quantifying discounting rates in humans across a variety of commodities; after being found to account for significantly more variance than exponential function models (Bickel & Marsch, 2001; Kirby & College, 1997; Kirby & Herrnstein, 1995). In the current study, individuals who never discounted were retained in the sample and simply have a k value of 0.00 in the data set. Given the concerted effort required to choose the delayed reward of every trial of the ascending and descending trials, we believe this represents a legitimate decision making process. Similarly, individuals who displayed variability or inconsistency both across switch points (i.e. different time delays) and within switch points (i.e. ascending and descending trials) were also retained in the sample. Research has shown that those with externalizing disorders are more inconsistent in their switch points on a reward delay discounting task compared to controls (Dai, Gunn, Gerst, Busemeyer, & Finn, 2016). Thus the inconsistency is not necessarily reflecting a subject error or neglect in doing task, rather we find that it reflects a pattern that one often sees in samples that have high levels of externalizing problems. Lastly, results presented in the current study were the same when discounting rate was operationalized using the Area Under the Curve scoring method (AUC; Myerson, Green, & Warusawitharana, 2001). AUC is a theoretically agnostic scoring method that consists of simply summing the area under the line plotted through the observed switch points.

Data Analysis

R version 3.4.0 was used for these analyses (R Development Core Team, 2013); including the MASS package for negative binomial model analyses (Venables & Ripley, 2002). A negative binomial model was used to test the effect of IQ, delay discounting rates (log10 k) and their interaction on lifetime alcohol problem counts. A multiple regression model was used test the effect of IQ, delay discounting rates (log10 k) and their interaction on the quantity of alcoholic drinks consumed in the past two weeks.

Results

IQ moderating association of discounting rate and alcohol problems

Table 2 presents the correlations of variables used in the subsequent analyses. A Negative binomial model was used to test the effects of IQ, discounting rate, and their interaction on lifetime alcohol problem counts. Odds ratios (OR) are presented instead of beta coefficients for ease of interpretation. The analysis revealed a significant main effect of discounting rate (OR = .18, 95% CIs [.06, .54], z = −3.34, p < .001), but not of IQ (OR = 1.01 [.99, 1.02], z = 1.60, p = .11). As hypothesized, there was a significant interaction of IQ and discounting rate (OR = 1.02 [1.01, 1.03], z = 3.63, p < .001). Due to the significant interaction of IQ and discounting rate, the conditional main effects of IQ and discounting rate should be interpreted with caution. An IQ tertile split was used to further examine the interaction of IQ and discounting rate. Sample characteristics of these tertiles are presented in Table 1. The three IQ groups created by IQ tertile split did not differ significantly in terms of gender, age, or two-week alcohol quantity. There was however a significant effect of IQ group on lifetime alcohol problem counts (F(2,14) = 3.85, p = .022). Post hoc comparisons using Tukey HSD test indicated the mean alcohol problems for the High IQ group (M = 18.34, SD = 18.34) was significantly lower than that in the Low IQ group (M = 23.53, SD = 19.27). The Medium IQ group did not differ significantly from the Low/High IQ groups. Follow up analyses found a significant effect of discounting rate on lifetime alcohol problems in the High IQ group (OR = 1.41 [1.19, 1.67], z = 3.87, p < .001), which signifies for each 1 unit increase in discounting rate there is an expected 41% increase of lifetime alcohol problems. A 1 unit increase is roughly 1 standard deviation of delay discounting rates. There was not a significant effect of discounting rate in the Medium IQ group (OR = 1.16 [.98, 1.36], z = 1.87, p = .06), or the Low IQ group (OR = .99 [.85, 1.15], z = −.17, p = .86). These findings support our hypothesis that delay discounting rates would have a stronger association with alcohol problems in high IQ individuals. Figure 1 displays the interaction of delay discounting rate and IQ on alcohol problems.

Table 2.

Bivariate Correlations of delay discounting rates, IQ, alcohol problems, and drinking quantity (N = 617)

DD Rate IQ Alc. Problems Quantity
DD Rate 1 - - -
IQ −.226** 1 - -
Alc. Problems .212** −.117** 1 -
Quantity .188** −.009 .692** 1
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Spearman correlation table of DD rate: log10 k delay discounting rate; IQ: Intelligence Quotient measured using Wechsler Abbreviated Scale of Intelligence (WASI); Alc. Problems: life time alcohol problems assessed by summed positive responses to questions from the Alcohol Use Disorders section of the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA); Quantity: # of drinks consumed in the last two-weeks assessed by summing reported alcohol consumption using a 2-week timeline follow back.

**

indicates p <.01.

Figure 1.

Figure 1.

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Mean lifetime alcohol problems divided by IQ tertiles cross tabulated with delay discounting rate tertiles. Error bars represent +/− SEM. * = p < .05, *** = p <.001.

IQ moderating the effect of discounting rate on quantity

A multiple regression model was used to test the effect of IQ, discounting rate, and their interaction on recent two-week alcohol consumption in standard drinks. The results of the regression indicate the two predictors and interaction term explained 4% of the variance (F(3, 613) = 9.01, R2=.04, p <001). The analysis revealed a significant main effect of discounting rate (β = −54.84 [−89.35, −20.34], p = .002) and a significant main effect of IQ (β = 1.02 [.46, 1.57], p < .001). As hypothesized, there was a significant interaction between IQ and discounting rate (β = .55 [.24, .85], p < .001). Again, IQ tertile groups were used to examine this interaction. There was a significant effect of discounting rate in the High IQ group (F(1, 203) = 20.10, R2 = .09, β = 12.87 [7.21, 18.53], p < .001), meaning for every 1 unit increase in discounting rate the quantity of alcohol consumed in the past two-weeks is expected to increase by about 13 standard drinks. There was also a significant effect of discounting rate in the Medium IQ group (F(1,204) = 5.23, R2 = .03, β = 5.58 [.76, 10.40], p = .02), meaning for every 1 unit increase in discounting rate the quantity of alcohol consumed in the past two weeks is expected to increase by about 6 standard drinks. Conversely there was not a significant effect of discounting rate in the Low IQ group (F(1,204) = .01, β = −.22 [−5.82, 5.38], p = .94). These results support our hypothesis that delay discounting rates would be more highly associated with quantity of alcohol use in high IQ individuals compared to low IQ. Figure 2 displays the interaction of delay discounting rate and IQ on quantity.

Figure 2.

Figure 2.

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Mean total drinks consumed in past 2-weeks broken down by IQ tertiles cross tabulated with delay discounting rate tertiles. Error bars represent +/− SEM. * = p < .05, *** = p <.001.

Discussion

The main purpose of this study was to test whether IQ moderated the association between delay discounting rates and measures of problematic alcohol use as measured by lifetime alcohol problems and current 2-week drinking quantity. The primary hypothesis was that IQ would moderate the association between delay discounting rate and problematic alcohol use such that the association between discounting rate and problematic alcohol use would be stronger in high IQ individuals compared to low IQ individuals. Our results supported this hypothesis, as there was a stronger association between lifetime alcohol problems and recent two-week drinking quantity with discounting rates in high IQ individuals compared to low IQ individuals. Given that individuals with lower intelligence tend to discount at higher rates regardless of substance use (Shamosh & Gray, 2008), it is possible that there is just less variability in this group to find strong associations with outside criteria of interest. However, it may also be the case that alcohol use severity is associated with a variety of executive functioning deficits in individuals with low IQ. Conversely, individuals with high IQ have an overall higher executive cognitive capacity and therefore problematic alcohol use may be more specifically associated with the tendency to make impulsive choices as modeled by a delay discounting task.

The results of this study should be interpreted in light of its limitations. First, the sample in this study was predominantly young Caucasian recruited from a large state university. Our sample also consisted of a relatively narrow age range (age 18 to 30) which may limit generalizability to individuals in later stages of problematic alcohol use. In this vein, more work needs to be done to better understand how discounting rates change across development. For example, a large longitudinal study found that discounting rates remained relatively consistent across early development and were more predictive of substance use onset than perhaps a consequence of substance use (Audrain-McGovern et al., 2009), while other studies mainly looking at tobacco use have observed evidence that discounting rates fluctuate with substance use behavior (Bickel, Odum, & Madden, 1999; Odum, Madden, & Bickel, 2002; Petry, 2001b; Yi et al., 2008). Another limitation was the small difference between increments used in the discounting tasks as half the participants received a task with $5.00 increments and about half received a task with $2.50 increments. As stated before, hyperbolic discounting rates (k value) are calculated by averaging the switch points between ascending and descending trials and therefore would be insensitive to small differences in the increments used. Lastly, given our sample consisted of a large proportion of college students our sample had a higher mean IQ than would be expected in a more representative sample.

Despite consistent associations between problematic alcohol use, higher delay discounting rates, and low IQ, no study has examined the exact nature of this relationship. Our results suggest that discounting rates have a stronger association to measures of problematic alcohol use in individuals of higher intelligence. This finding may inform the sometimes modest relationship between discounting rates and continuous measures of externalizing pathology (e.g. alcohol use), despite very reliable group differences between individuals with externalizing disorders and healthy controls. It appears problematic alcohol use in individuals with higher intelligence may be more directly associated with the tendency to make more impulsive decisions, whereas alcohol problems may be associated with a variety of executive functioning deficits in those of lower intelligence. Furthermore, some researchers have proposed delay discounting as a possible trans-externalizing (Bobova et al., 2009) and trans-disease process (Bickel, Jarmolowicz, Mueller, Koffarnus, & Gatchalian, 2012) that serves as marker of poor appetitive regulation. It is therefore of paramount importance to better understand other factors that may moderate the relationship between discounting rates and outside criteria of interest. Given the exploratory nature of the current study, future work should aim to replicate the current effect and examine the robustness of this interaction across differing samples and discounting tasks. In addition, future research may inquire whether IQ moderates the relationship between delay discounting rates and other continuous measures of poor self-regulation such as drug use or overeating.

Acknowledgments

This research was supported by National Institutes of Alcohol Abuse & Alcoholism grant R01AA13650 to Peter. R. Finn and training grant fellowships to Allen Bailey from the National Institute of Drug Abuse, T32 DA024628 and Kyle Gerst from the National Institute on Alcohol Abuse and Alcoholism, T32 AA07642. The authors report no conflicts of interest

Contributor Information

Allen J. Bailey, Psychological and Brain Sciences, Indiana University-Bloomington

Kyle Gerst, Psychological and Brain Sciences, Indiana University-Bloomington.

Peter R. Finn, Psychological and Brain Sciences, Indiana University-Bloomington.

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