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. Author manuscript; available in PMC: 2024 Feb 1.
Published in final edited form as: Exp Clin Psychopharmacol. 2022 Jan 27;31(1):228–237. doi: 10.1037/pha0000544

Effort-related Decision Making and Cannabis Use among College Students

Samuel F Acuff 1, Nicholas W Simon 2, James G Murphy 3
PMCID: PMC10071406  NIHMSID: NIHMS1883211  PMID: 35084912

Abstract

Cannabis exerts an indirect effect on dopamine (DA) output in the mesolimbic projection, a circuit implicated in reward processing and effort expenditure, and thus may be associated with aberrant effort-based decision making. The “amotivation syndrome” hypothesis suggests that regular cannabis use results in impaired capacity for goal-directed behavior. However, investigations of this hypothesis have used divergent methodology and have not controlled for key confounding variables. The current study extends these findings by examining the relation between cannabis use and effort-related decision making in a sample of college students. Cannabis using (n = 25; 68% meeting criteria for Cannabis Use Disorder) and non-cannabis using (n = 22) students completed the Effort Expenditure for Rewards Task (EEfRT). In generalized estimating equation models, reward magnitude, reward probability, and expected value predicted greater likelihood of selecting a high-effort trial. Further, past-month cannabis days and cannabis use disorder symptoms predicted the likelihood of selecting a high-effort trial, such that greater levels of both cannabis use days and symptoms were associated with an increased likelihood after controlling for ADHD symptoms, distress tolerance, income, and delay discounting. The results provide preliminary evidence suggesting that college students who use cannabis are more likely to expend effort to obtain reward, even after controlling for the magnitude of the reward and the probability of reward receipt. Thus, these results do not support the amotivational syndrome hypothesis. Future research with a larger sample is required to evaluate possible associations between cannabis use and patterns of real-world effortful behavior over time.

Keywords: cannabis, effort expenditure, effort-related decision making, college students, amotivational hypothesis

Introduction

Cannabis (i.e., marijuana) is the most commonly used illicit substance in the United States, and, as cannabis becomes legalized, prevalence rates appear to be increasing (National Survey on Drug Use and Health, 2018; Schulenberg et al., 2018)Past-year use by young adults is currently at its highest ever rate (37.5%), and rates have grown across all age groups since the early 2000s(Schulenberg et al., 2018). College students are a high-risk group for initiation of cannabis use, as approximately 8.5% of all students who had not previously used report initiating use during their first year (Suerken et al., 2014). Despite the fact that most young adults perceive cannabis to be relatively harmless (Hanauer et al., 2019), frequent cannabis use is associated with a range of consequences in college students, including academic impairment (Arria et al., 2015; Phillips et al., 2015), internalizing spectrum disorders (Feingold et al., 2016; Keith et al., 2015; Lev-Ran et al., 2013), respiratory difficulties (Tetrault et al., 2007), motor vehicle accidents (Rogeberg & Elvik, 2016), and neurocognitive impairment (Becker et al., 2014; Broyd et al., 2016; Grant et al., 2003). Thus, there is a need for more research to characterize the decision-making processes that are associated with cannabis use during this high-risk developmental period.

There is a public perception that cannabis use also results in a lack of motivation. This perception was born out of the amotivation syndrome hypothesis (Smith, 1968), which suggests that regular cannabis use results in impaired executive functioning, arousal, and affective reactivity leading to reduced capacity for goal-directed behavior other than drug seeking. Although Tetrahydrocannabinol (THC), the primary psychoactive substance in cannabis, exerts its primary influence on CB1 cannabinoid receptors throughout the nervous system (Kano et al., 2009), THC also appears to have an indirect effect on dopamine (DA) output in the mesolimbic projection (French et al., 1997; Oleson & Cheer, 2012), a circuit implicated in reward processing. Studies suggest that DA transmission in the mesolimbic projection plays a role in regulating effort-related decision making, which is the degree of willingness to expend effort to receive a reward (Salamone et al., 2009; Treadway, Buckholtz, et al., 2012). Indeed, regular cannabis use has been linked to downregulation of dopaminergic transmission in the mesolimbic projection (Bloomfield et al., 2017; Volkow et al., 2014), findings that are similar to the outcomes of regular use of other substances. Further, substances with downregulation of dopaminergic transmission also have related syndromic presentations, such as reward deficiency syndrome among heavy drinkers and stimulant users, and post-withdrawal syndrome among heroin users (Rovai et al., 2013), which has also been directly tied to dopamine deficiency in the mesolimbic system (Di Chiara, 1997; Tanda et al., 1997).

Although several studies have demonstrated acute amotivational effects of cannabis use (Cherek et al., 2002; Foltin et al., 1989; Lawn et al., 2016; Miles et al., 1974), support for chronic amotivation effects of cannabis use has been mixed (Barnwell et al., 2006; Lawn et al., 2016; Mendelson et al., 1976; Musty & Kaback, 1995). One study found amotivational effects of regular cannabis use using tasks like solving anagrams (Creason & Goldman, 1981), and nonexperimental naturalistic research has prospectively connected cannabis use with diminished initiative, effort, and persistence. However, because between- and within-subject variance has not been adequately disaggregated, these results should be interpreted cautiously (Lac & Luk, 2018). This theory has been invoked to account, in part, for the fact that frequent cannabis use is associated with lower academic educational engagement and attainment (Arria et al., 2015; Suerken et al., 2016). There is also some evidence that cannabis use is associated with lower levels of other high–effort behaviors such as exercise (Ashdown-Franks et al., 2019; Meshesha et al., 2018).

However, it is difficult to disentangle the direct effects of cannabis on effort from risk factors for cannabis use that may also lead to lower effort or motivation. For example, cannabis use often co-occurs with depression (Degenhardt et al., 2003), a state which has also been tied to blunted effort-related decision making (Nelson & Sciences, 1995; Treadway et al., 2013), and research has explicitly tied this comorbidity to a genetic polymorphism related to dopamine transmission (Bobadilla et al., 2013). Indeed, several studies demonstrate that any amotivational effect is diminished after controlling for depression (Kupfer et al., 1973; Musty & Kaback, 1995). Other psychiatric conditions, such as ADHD have not been examined in conjunction with cannabis but are also associated with aberrant dopaminergic transmission (Volkow et al., 2011) and may in part account for some of the effort-related outcomes associated with frequent cannabis use. Effort expenditure requires focused behavioral allocation for an extended period of time, which introduces the confound of time to reward receipt. Effort expenditure can also be distressing, and thus a willingness to tolerate distress for extended periods of time to obtain the reward may be a confounding variable. Cannabis users report lower levels of distress tolerance (Buckner et al., 2007) and may report elevated delayed reward discounting, albeit the relationship is weaker compared to other substances (Strickland et al., 2020). Thus, both delay discounting and distress tolerance may play important roles in explicating the relation between effort-related decision making and cannabis use yet have not been included in previous studies.

Another reason for the inconsistent findings could be due to the lack of consensus on a validated measure of amotivation. Human behavioral researchers have attempted to capture reward motivation using the Effort Expenditure for Rewards Task (EEfRT), a task in which participants choose between a ‘high-effort’ task that requires pressing a button 100 times with the nondominant little finger for a large sum of money (high-effort/high-reward) or an ‘low-effort’ task that requires pressing a button 30 times with the dominant index finger for a smaller sum of money (low-effort/low-reward; Treadway, Buckholtz, Schwartzman, Lambert, & Zald, 2009). Although the EEfRT task is a valid and reliable index of effort-related decision making, the task takes approximately 20 minutes and typically requires in-person data collection to ensure that the participant does not cheat. However, the task has successfully differentiated between controls and groups with dysregulated DA output in dopaminergic projections, such as among individuals with Parkinson’s (Friedman et al., 2007), bipolar disorder (Johnson et al., 2015), multiple sclerosis (Tellez et al., 2008), and schizophrenia (Barch, Treadway, & Schoen, 2014). Further, administration of d-amphetamine, an indirect dopamine agonist, increased responding on the EEfRT task (Wardle et al., 2011), providing evidence of validity for the behavioral task.

Lawn et al., 2016 described two studies that examined acute and chronic effects of cannabis use on effort-based decision using the EEfRT task. In the first study, cannabis administration was associated with a lower likelihood of high-effort choices (high-effort/high-reward) compared to placebo, a finding consistent with other studies examining amotivational acute effects of cannabis. The second study compared heavy cannabis users with non-cannabis users and found no difference in effort-based decision making. This study also found group differences in depressive symptoms, a factor known to influence effort-related decision making (Treadway et al., 2013).

Current Study

Using a diversity of measurement approaches, previous research has found mixed effects of regular cannabis use on effort-related decision making (Barnwell et al., 2006; Lawn et al., 2016; Mendelson et al., 1976; Musty & Kaback, 1995). A majority of these studies have not ruled out confounding co-occurring phenomena associated with diminished effort-related decision making, such as schizophrenia, depression, or ADHD, which may have influenced results. Further, studies have previously examined heterogenous community samples with a non-specific age range, or in-patient heavy cannabis users. Thus, the current preliminary study sought to test the amotivational hypothesis exclusively among non-treatment-seeking emerging adult cannabis users. This age group uses cannabis at higher rates than any other age demographic (National Survey on Drug Use and Health, 2018) but are less likely to have severe comorbidity or age-related variability that might make it difficult to isolate the role of cannabis use. We tested this hypothesis by examining differences in effort-related decision making, using the EEfRT, between cannabis and non-cannabis using college students, in models that controlled for relevant covariates (ADHD, distress tolerance, and delay discounting). Given the level of participant burden of the EEfRT task, we also created two novel tasks assessing effort expenditure. Evidence for ecological and concurrent validity in the current study would suggest that these measures are viable alternatives for measuring effort-related decision making in survey studies.

Method

Participants

Cannabis using (n = 26) and non-cannabis using (n = 22) college students were recruited from a large public university in the mid-South of the United States. One participant in the cannabis using group only participated in two trials of the EEfRT task and was therefore removed from further analyses, resulting in a final sample size for the cannabis using group of 25. We chose a sample size of at least 20 participants in order to establish a preliminary estimate of effect size for detecting group differences. We determined in post hoc power analysis that, based on our sample and effect size for the cannabis use group difference in likelihood of selecting high effort trials, we are 47% powered to detect a significant effect. Inclusion criteria for all participants were 1) age 18–25; and 2) English fluency. Exclusion criteria for all participants was self-reported history of depression, psychotic disorder, and alcohol or substance use disorder or treatment. In addition to the above, all cannabis using participants initially reported five or more cannabis use days in the past month.1 The control sample reported 1) less than five cannabis use days within the past year; 2) one or less binge drinking episode (4/5 drinks in one occasion for women/men) within the past month; and 3) no other drug use within the past month. We chose not to limit drinking days in cannabis users because those who use cannabis often also report alcohol use, and it would therefore be challenging to identify regular cannabis users who are alcohol abstinent. Further, doing so would create a group that is not representative of typical cannabis users.

Measures

Alcohol and Cannabis Use.

Participants reported the total number of days in which they used cannabis in the past month. Cannabis Use Disorder was assessed with 12 items that mirrored the DSM-5 criteria for cannabis use disorder (American Psychiatric Association, 2013). Participants reported whether they experienced each symptom over the preceding year. Alcohol use was assessed with the Daily Drinking Questionnaire (Collins et al., 1985). Participants reported how many drinks they consumed on each day of the week, for a typical week out of the last month. Typical drinks per day were summed to create an index of typical drinks per week.

Measures of Effort.
Effort-related Decision Making.

The Effort Expenditure for Rewards Task (EEfRT) was designed by Treadway et al. (2009) in order to measure effort-based decision-making. The task is a series of trials in which the participant is required to click a button a certain number of times in an abbreviated time frame. For each trial, the individual would choose whether they wanted to complete a low-effort task (low-effort/low-reward) or a high-effort task (high-effort/high-reward) for varying amounts of reward. The low-effort task requires the subject to make 30 button presses using the dominant index finger within 7 seconds for $1.00. The high-effort task requires the subject to make 100 button presses, using the non-dominant little finger within 21 seconds for between $1.24 and $4.30. In each trial, the participant first chooses the low-effort or the difficult task. The trial then begins, and after the button clicking, the participant is told whether or not they completed the task. Finally, participants are not guaranteed to win the reward. When the participant is choosing between the low-effort and the high-effort task, they are also shown the probability of actually winning the reward if the behavior is successfully completed. Four probabilities were used: low (12% of winning), moderate (50%), high (88%), and certain (100%; this probability is a novel addition to this task in the current study in order to better control for the effect of uncertainty). After the participant is told whether or not they completed the task, a screen shows whether or not the participant earned the reward. The low-effort EEfRT trials are traditionally much shorter than the high-effort trials; however, trial choices are then confounded with delay of reward receipt (during a high-effort, low-reward [for example, one worth $1.50], utility is more accurately maximized by selecting two easy trials that last a total of 14 seconds rather than one high-effort trial that lasts 20 seconds). Thus, the current study extended the time that the participant spent on the screen explaining whether the participant won the reward in order to match the total time the individual would spend in the low- and high-effort trials. Participants were informed that the durations of the low- and high-effort trials were equal.

The participants had 20 minutes to complete as many trials as possible. At the beginning of the task, participants were told that two random trials during the task will be selected and the monetary value paid out. The EEfRT results in several indices representing different aspects of effort-related decision making, including reward magnitude (size of reward for each task), reward probability (likelihood of receiving the reward), expected value (magnitude x probability), and choice type (low- or high-effort). Although reliability for this task has not been established among individuals who use cannabis, the task demonstrates excellent four-week test-retest reliability and adequately differentiates populations with theoretically compromised DA output (Reddy et al., 2015; Treadway et al., 2009; Treadway, Bossaller, et al., 2012).

Effort Discounting.

We created two tasks paralleling the 27-item delay discounting task (Kirby et al., 1999) to assess discounting as a function of effort expenditure, similar to tasks created by Ostaszewski et al. (2013). The first task, called the cognitive effort discounting task, asked participants to choose between a smaller amount of money for no effort, or a larger amount of money for reading pages in a book (e.g., “would you rather receive $20 and do nothing, or receive $100 and read 50 pages?”). The second task, called the physical effort discounting task, asked participants to choose between a smaller amount of money for no effort, or a larger amount of money for climbing varying amounts of flights of stairs (e.g., would you rather receive $20 and do nothing, or receive $100 and climb 30 flights of stairs?). Outcome data for both effort discounting tasks were calculated using the 27-item macro (Kaplan et al., 2016). For the cognitive effort discounting task, the length of delay was directly translated to effort (e.g., “receive $55 in 117 days” changed to “receive $55 and read 117 pages in a book”). For the physical effort discounting task, values were multiplied by .66 (e.g., “receive $55 in 117 days” changed to “receive $55 and climb 77 flights of stairs”) to create a set of values that was less likely to result in a floor effect.

Attention Deficit/Hyperactivity Disorder (ADHD).

The ADHD Self-Report Scale (ASRS) is a brief, self-report measure of symptoms related to ADHD. We used the 6-item version which has strong internal consistency and outperforms the full measure in predicting ADHD (Kessler et al., 2005).

Distress Tolerance.

The Distress Tolerance Scale was designed to assess the capacity to withstand negative psychological states using 15 statements (e.g., “Feeling distressed or upset is unbearable to me”; “I can tolerate being distressed or upset as well as most people”). Participants rate each statement on a scale from 1 (Strongly Agree) to 5 (Strongly Disagree). Items are summed to create a total score. Internal consistency was excellent in the current sample (Cronbach’s α = .91). Higher scores indicate greater distress intolerance.

Delay Discounting.

To assess delay discounting, we used the 27-item Monetary Choice Questionnaire (Kirby et al., 1999). For each item, participants choose between smaller, immediate rewards or larger, delayed rewards. Each choice contributes to the calculation of the participant’s overall discounting parameter (k), or the rate at which rewards are discounted as a function of delay in reward receipt. Outcome data for delay discounting was calculated using the 27-item macro (Kaplan et al., 2016). Higher k values are indicative of greater discounting of delayed rewards, or greater impulsivity.

Procedures

All procedures received approval from research ethics committees for the proper execution of the research. Participants who met criteria for either group were contacted by study personnel and invited into a lab for a 1-hour session. All study sessions took place in a private research lab. Participants were asked to abstain from using cannabis the night before and the day of the study. When the participant arrived study personnel then asked participants whether they had used cannabis in the 24 hours preceding the appointment. Participants reporting past 24-hour cannabis use were rescheduled (n = 1). Participants provided informed consent, and study personnel provided instructions for the EEfRT paradigm. Each participant then interacted with the EEfRT paradigm for 20 minutes. The study personnel facilitating the study sessions were blind to group membership for each participant before and during the session. Following completion of the task, participants took a 30-minute survey with questions about cannabis use, alcohol and other drug use, effort discounting, and distress tolerance. Upon completion of the survey study personnel provided debriefing for the participant, answered any questions, and compensated participants with either $20 or credit towards a research course. Further, two successfully earned trials from the EEfRT task were randomly selected, and the monetary values were given to the participant, between $2 and $8.75.

Data Analysis

We examined correlations between study variables and differences between contrast groups. Generalized Estimating Equations (GEE) were used to assess factors associated with the likelihood that an individual would make a high-effort choice. First, we examined a model with only a priori covariates, which included race, sex, age, income, typical drinks per week, ADHD, delay discounting, distress tolerance, and trial number. Next, we individually examined the separate effects of magnitude, probability, and expected value on the likelihood of selecting a high-effort trial in a single GEE model. Next, we examined study group (cannabis v. control), number of past-month cannabis days, and cannabis use disorder symptoms in separate models to examine the association between cannabis and effort-related decision making after controlling for all aforementioned covariates. We examined cannabis variables in separate models because we wanted to see the unique association between each cannabis variable and effort-related decision making, rather than the effect after controlling for the other cannabis variables. Finally, we examined the interaction of each cannabis variable with reward magnitude, reward probability, and the expected value variable. All models used the exchangeable alternating logistic regression function (Carey et al., 1993). We used SAS 94 software for all analyses (SAS Institute Inc.; Cary, NC, USA). Finally, we examined the relations between the effort discounting measure and performance on the EEfRT, in addition to several indices of real-world effort expenditure. Data is available upon reasonable request. This study was not preregistered. We report how we determined our sample size, all data exclusions, and all manipulations. Data and study materials are available from the authors upon reasonable request. All measures collected during the course of the study can be found in the supplemental materials.

Results

Descriptive Statistics

Mean differences in study variables across groups are presented in Table 1. Cannabis users were less likely to be Caucasian compared to controls. Further, cannabis users reported a significantly greater number of alcoholic drinks in a typical week of the past month. No other differences existed between any other study variable. Cannabis using participants reported an average of 15.16 cannabis days in the past month (SD = 11.86; Range = 1 − 31) and an average of 3.00 Cannabis Use Disorder symptoms (SD = 2.81; Range = 0 – 9; 68% reported at least two symptoms). Cannabis users had a lower median income ($15,001 – $25,000) compared to nonusers ($35,001 - $50,000). Correlations for the full sample are reported in Table 2.

Table 1.

Differences in Study Variables between Cannabis Users and the Control Group

Variable Cannabis Users % Nonusers % X2 p Cohen’s d*
Sex (% female) 80 59.1 2.45 .12 .47
Race (% Caucasian, Black, Other) 8/64/72 31.8/50/18.2 4.29 .04 .63
M (SD) M (SD) t df p-value Cohen’s d
Age 20.28 (1.81) 20.55 (1.92) .49 45 .63 .14
% High-Effort Selected 49% (17%) 39% (20%) −1.87 45 .07 .54
Total trials 41.40 (3.55) 43.64 (3.74) 2.10 45 .04 .61
Number of low-effort trials 21.40 (8.34) 26.82 (10.01) 2.03 45 .049 .59
Number of high-effort trials 20.00 (5.67) 16.82 (7.68) −1.63 45 .11 .47
Percentage success in completing low-effort trials 95% (7%) 99% (2%) 2.43 27.45 .02 .78
Percentage success in completing high-effort trials 76% (32%) 72% (27%) .46 45 .65 .14
Delay discounting .0532 (.0701) .0608 (.0764) .34 40 .74 .10
Effort discounting -Physical .0265 (.0370) .0291 (.0551) .19 45 .85 .06
Effort discounting - Cognitive .0237 (.0434) .0247 (.0533) .07 45 .95 .02
Distress Tolerance 33.52 (10.61) 38.95 (12.86) 1.59 45 .12 .46
ADHD 10.80 (3.76) 11.36 (2.52) .60 45 .56 .17
Typical Drinks per Week 5.12 (6.22) .86 (1.49) −3.32 27.10 .003 .94
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Note. ADHD = Attention Deficit/Hyperactivity Disorder.

*

Cohen’s d for sex and race were computed using the Chi-square test statistic (Rosenthal & DiMatteo, 2001)

Table 2.

Correlations among Study Variables.

1 2 3 4 5 6 7
1. EEfRT - % High-effort -
2. Distress Tolerance −.26 -
3. ADHD .04 41** -
4. Delay Discounting −.08 −.12 −.33** -
5. Cognitive Effort Discounting −.24 .10 −.13 .51** -
6. Physical Effort Discounting −.37* −.04 −.34* .46** −.27 -
7. Cannabis Days .34* −.22 −.03 .11 .04 .13 -
8. CUD .32* −.21 −.07 .18 .07 .16 81***
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Note. Reader identity and activity identity are dichotomous variables (0 = No, 1 = Yes). For cannabis variables, those in the non-cannabis using group were given values of zero, given that they did not fill out these measures in the survey (as they reported no past 6-month cannabis use). EEfRT = Effort Expenditure for Rewards Task; ADHD = Attention Deficit/Hyperactivity Disorder; CUD = Cannabis Use Disorder

EEfRT performance

Participants completed an average of 42.45 trials over the duration of the EEfRT task (SD = 3.77, range = 32 – 51). No participant selected only high-effort or low-effort trials during the duration of the task. On average, participants selected the high-effort trials 46% of the time (SD = 19%). Participants successfully completed the high-effort trials 74% of the time (SD 29%), while they completed the low-effort trials 97% of the time (SD = 6%). Cannabis users completed fewer trials compared to nonusers (Cohen’s d = .61). Nonusers selected low-effort trials more often compared to cannabis users (Cohen’s d = .59), and nonusers also successfully completed low-effort trials more often compared to cannabis users (Cohen’s d = .78). There was not a significant difference in the number of high-effort trials selected, the chance of success in completing a high-effort trial, or the number of high-effort trials relative to low-effort trials (Cohen’s d = .54). Figure 1 demonstrates the percent selection of high-effort choices as a function of probability and reward magnitude, split by cannabis and non-cannabis using college students. Figure 2 demonstrates the percentage of high-effort trials selected, separately as a function of reward magnitude and probability, split by cannabis and non-cannabis using college students. All comparisons between those who use cannabis and those who do not in the percentage of high-effort choices at each magnitude and probability were nonsignificant.

Figure 1.

Figure 1

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Heat Plots of High-Effort Trials Chosen by Cannabis Use Group

Heat plot demonstrating the likelihood of selecting the high-effort choice as a function of reward magnitude and reward probability, split by study condition (cannabis using college students v. controls). Lighter colors represent greater likelihood of selecting the high-effort choice.

Figure 2.

Figure 2

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The Percentage of High-Effort Trials Chosen by Cannabis Use Group and both Trial Reward Magnitude and Probability.

Note. The average percentage of high-effort trials chosen as a function of (A) trial reward magnitude and (B) Trial reward probability, separated by cannabis use status. A repeated measures ANOVA examining probability and cannabis use groups as predictors of hard trial selection revealed a significant main effect for probability (F [3,135] = 12.349, p < .001), but no significant main effect for cannabis use group (F [1] = 2.28, p = .14). Similarly, another repeated measures ANOVA examining magnitude and cannabis use groups as predictors of hard trial selection revealed a significant main effect for magnitude (F [3,135] = 24.36, p < .001), but no significant main effect for cannabis use group (F [1] = 3.51, p = .07).

EEfRT GEE models

Results are presented in Table 3. The covariate model suggested that only trial number was associated with the chances of selecting a high-effort reward, suggesting that effort decreased as a function of the duration of the task. Separate predictor models suggested that reward magnitude, reward probability, and expected value predicted greater likelihood of selecting a high-effort trial. The study group demonstrated a nonsignificant trend-level effect on the likelihood of selecting a high-effort trial (p = .07). However, the number of cannabis use days was significantly associated with the likelihood of selecting a high-effort trial, such that greater levels of cannabis use was associated with an increased likelihood. Similarly, the number of cannabis use disorder symptoms was significantly associated with the likelihood of selecting a high-effort trial, such that greater levels of cannabis use disorder symptoms was associated with an increased likelihood. No interactions were significant.2

Table 3.

Generalized Estimating Equations Models Examining Cannabis Use Variables and Covariates in Predicting Likelihood of Selecting a ‘High-effort’ Trial

Beta SE p-value
Covariate Model (M1)
 Race (comparison group, 1) .06 .25 .83
 Sex (comparison group, 1) .25 .25 .32
 Age −.05 .07 .43
 Income .05 .08 .57
 Typical Drinks per Week .01 .11 .93
 ADHD .02 .04 .65
 Delay Discounting −1.17 1.74 .50
 Distress Tolerance −02 .01 .08
 Trial Number −.02 .006 .01
Effort Predictors + Covariates Model (M2)
 Magnitude .54 .09 < .001
 Probability .01 .003 < .001
 Expected value .06 .001 < .001
Below Predictor + M2
 Cannabis Use Group −.58 .32 .07
Below Predictor + M2
 Cannabis Use Days .03 .01 .003
Below Predictor + M2
 CUD Symptoms .10 .04 .02
Below Predictors + M2
 Cannabis Use Group × Magnitude .10 .17 .57
 Cannabis Use Group × Probability .003 .005 .51
 Cannabis Use Group × Expected Value .001 .002 .43
Below Predictors + M2
 Cannabis Use Days × Magnitude .006 .006 .50
 Cannabis Use Days × Probability −.0001 .0002 .72
 Cannabis Use Days × Expected Value .000 .000 .88
Below Predictors + M2
 CUD Symptoms × Magnitude .005 .03 .85
 CUD Symptoms × Probability .001 .001 .40
 CUD Symptoms × Expected Value .0002 .0004 .57
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Note. SE = Standard Error; ADHD = Attention Deficit/Hyperactivity Disorder; CUD = Cannabis Use Disorder. Analyses were first run in a covariate only model (M1). Next, the Effort predictors were included in addition to the covariate model (M2). Finally, individual models were run for each cannabis variable and for models including cannabis variables and their interaction effects.

Relations between the EEfRT and Effort Discounting

Greater likelihood of choosing the high-effort trial in the EEfRT task was significantly correlated with physical effort discounting in the expected direction, suggesting that those with greater discounting of physical effort are less likely to select the high-effort trials. However, the relation with cognitive effort discounting was nonsignificant.

Discussion

The goal of the current preliminary study was to examine differences in effort-related decision-making processes between cannabis users and control participants in a sample of college students after controlling for relevant covariates. Contrary to the amotivational syndrome hypothesis, college students using more cannabis were more likely to select the high-effort choice option, regardless of the reward magnitude, probability, and expected value of the overall reward. Although there was not a significant difference between cannabis use groups, there was a medium sized effect, lending consistent support for an association between cannabis use and greater high-effort choices. These results are inconsistent with some previous research (Creason & Goldman, 1981; Lac & Luk, 2018; Lawn et al., 2016), which may be explained by differences in the operationalization of the effort-related decision-making construct and the inclusion of important covariates. One study using the EEfRT to examine effort-related decision making among marijuana users found no difference between individuals who smoked cannabis and the control group (Lawn et al., 2016). Although that study controlled for depressive symptoms, it did not control for other covariates, such as delay discounting, distress tolerance, and ADHD symptoms. The current study ruled out depression and included the other covariates in the GEE model, which may explain the difference in the results. Further, previous iterations of the EEfRT task confounded effort expenditure with delay, as completion of the low-effort trials only lasted 7 seconds while high-effort trials lasted 20, which mean that, theoretically, you could earn more money by selecting low-effort trials than high-effort in certain circumstances. Humans universally demonstrate a preference for immediate over delayed rewards, a concept known as delay discounting, suggesting that time introduces a “cost” that reduces the value of the delayed reward (Mazur, 1993). There are, however, individual differences in the degree that individuals discount delayed rewards, such that some individuals more steeply discount delayed rewards (Kirby et al., 1999). Steep delay discounting is a known risk factor for substance use generally (MacKillop et al., 2011), although the effect is much less robust in studies examining the relation between delay discounting and cannabis (Strickland et al., 2020). Our study extended the time following reward receipt resulting in a more temporally balanced task, which may also account for differences in the study findings.

Our results, which examined whether or not regular cannabis use is associated with greater effort expenditure, differ from those studies examining the acute effects of cannabis that indicate that cannabis intoxication results in diminished effort expenditure (Cherek et al., 2002; Foltin et al., 1989; Lawn et al., 2016; Miles et al., 1974). These divergent findings may highlight an ecological fallacy in understanding the effect of cannabis on effort-related decision making. In other words, while those who use cannabis may demonstrate higher levels of effort expenditure compared to those who do not (between-person effect), these individuals may expend less effort at any given time while under the influence of cannabis (within-person effect). Our between-person results are, however, inconsistent with large sample survey data demonstrating a negative association between cannabis use and academic engagement and exercise. Although conjecture, cannabis generally exerts dose-dependent biphasic effects, such as in the areas of memory, cognition, and anxiolytic effects (Calabrese & Rubio-Casillas, 2018; Rey et al., 2012). It is possible that the differences in results can be explained by relative THC dose and frequency of use across studies, although this will need to be explored explicitly in future research.

Importantly, these findings do not necessarily suggest that cannabis use associated with impairment-free goal-directed behavior. The results may support insensitivity to reward contingencies, as cannabis use was associated with greater selection of high-effort trials across all conditions. Further, it is possible that balancing low-effort with high-effort performance across the task may be the optimal, rational energy-expenditure strategy, which was more common among those who do not use cannabis. Interestingly, the results are also mostly consistent with recent studies comparing effort-related decision making in other substance using groups with controls. Stuppy-Sullivan et al., (2020) used the EEfRT to examine effort-related decision-making among those with no substance use disorder, those with a mild substance use disorder, and those with moderate to severe substance use disorder. They found that substance use interacted with expected value, such that individuals with a substance use disorder were more insensitive to reward contingencies (i.e., magnitude and probability). Another study compared effort-related decision making among individuals who smoked cigarettes, individuals who have quit smoking, and a control group using the EEfRT task (Addicott et al., 2020) and found that smoking status interacted with expected value as well; however, this study did not find that smoking was associated with a higher likelihood of selecting the high-effort trial. Thus, there is some precedence in claiming a positive relation between substance use and effort-related decision making.

However, some differences exist in these results, which might be explicable through differences in study design and focus. First, the studies examined different substances (one general, one cigarette, one cannabis) which have different mechanisms of action and may have differential effects on effort-related decision making. Second, these studies examined individuals at different developmental stages of addictive behavior, highlighting the dynamicity of regular substance use and its effect on decision making. Indeed those with mild substance use disorder severity (Stuppy-Sullivan et al., 2020), and those who had already quit smoking (Addicott et al., 2020) did not demonstrate aberrant decision making. Although conjecture, our results may highlight the effects of cannabis at a particular developmental stage of use in the progression of cannabis use disorder. Most of the cannabis using group did report cannabis use disorder symptoms and met criteria for cannabis use disorder; however, only 32% reported moderate or severe symptoms, suggesting the possibility that a more severe sample may demonstrate differential effort-related decision making.

Given the length and participant burden of the EEfRT task, a shorter, self-report measure that still provides valid measurement of effort-related decision making may help extend the utility of the effort-related decision-making construct. We created two measures, one for physical effort and one for cognitive effort, to examine the discounting of a reward due to effort required to receive it. The results suggest that cognitive and physical effort discounting may be divergent constructs, to the point where those more likely to discount physical effort would be more likely to identify as a reader. Further, the EEfRT task appears to assess physical effort rather than cognitive. However, cannabis use was not associated with any of the real-world indices of effort expenditure, highlighting the complexity of translating measures of effort to reflect more temporally extended and clinically relevant patterns of effortful behavior such as those required to successfully complete school, maintain a job, and complete other difficult tasks required sustained effort over time often organized around delayed reinforcement.

Limitations and Future Directions

Our study had several important strengths. First, we used a robust behavioral task that demonstrates strong validity with important constructs related to dopaminergic output. Second, we effectively controlled for covariates that had not previously been controlled for in other studies. However, there are a few limitations that should be considered when interpreting the results of the current study. First, the sample size was small, limiting power for interaction effects. Further, the college sample may artificially constrain variability in effort. Second, we did not actually measure depression but simply ruled out depression, which prevents us from making more concrete statements about the influence of depressive symptoms on our findings. Third, we did not include a cannabis administration condition, which limits our ability to ascertain between-person and within-person differences that may reveal an ecological fallacy. Fourth, our data was cross-sectional, and we cannot ascertain whether our findings are specific to this developmental period or to a particular stage of addictive behavior progression. Related, we cannot differentiate between levels of effort-related decision making at different severity levels in cannabis use. Fifth, the high-effort alternative is the large reward across all trials, which makes it difficult to determine whether the higher proportion of choices were indeed driven by lower sensitivity to effort costs in the cannabis users, or by heightened valuation for the larger reward by the cannabis users who had significantly lower incomes, or indeed a lower sensitivity to lack of reinforcement by the cannabis users who failed to complete more trials than nonusers. Sixth, we did not control for tobacco use which is common among individuals who use cannabis and may have contributed to our observed results.

Conclusion

The current study examined effort-related decision making among college students after controlling for previously potential confounds and using a valid measure of effort-related decision making. Our results suggest that college students who use cannabis may be more likely to expend effort regardless of the constraints placed on the choice context. Future research should explore changes in effort-related decision making as a function of changes in the progression of severity of cannabis use disorder.

Supplementary Material

Supplemental Material

Public Significance Statement:

Cannabis use is becoming increasingly tolerated, both culturally and legally; yet, the risks associated with cannabis use are still unclear. There is a perception among the general public that cannabis leads to amotivation and diminished effortful behavior. Our results do not support the amotivational hypothesis but, instead, a greater likelihood of selecting high effort trials.

Footnotes

1

Frequent cannabis use among college students is associated with adverse consequences but there is no established threshold for problem use so we elected to use more than weekly use as an inclusion criteria.

2

At the suggestion of a reviewer, we also ran all GEE analyses excluding the 100% probability trials. The results for cannabis use days and CUD symptoms were the same (i.e., still significant in a positive direction). However, when 100% probability trials were excluded cannabis group was a significant predictor of choice, such that being in the cannabis using group was significantly associated with increased likelihood of selecting the high-effort choice in a given trial (β = .69 [S.E. = .32], p = .03). Given the post hoc nature of these findings and to protect against researcher degrees of freedom, we elected to include this footnote rather than include them in the primary analyses. We thank the reviewer for their suggestion.

Contributor Information

Samuel F. Acuff, The University of Memphis

Nicholas W. Simon, The University of Memphis

James G. Murphy, The University of Memphis

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