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. Author manuscript; available in PMC: 2021 Apr 1.
Published in final edited form as: Psychopharmacology (Berl). 2020 Jan 3;237(4):1081–1090. doi: 10.1007/s00213-019-05437-3

Effort-based decision making varies by smoking status.

Merideth A Addicott 1, Margaret C Wardle 2, James P Selig 3
PMCID: PMC7125005  NIHMSID: NIHMS1561413  PMID: 31900525

Abstract

Rationale:

A reduced willingness to perform effort based on the magnitude and probability of potential rewards has been associated with diminished dopamine function and may be relevant to chronic drug use.

Objectives:

Here, we investigated the influence of smoking status on effort-based decisions. We hypothesized that smokers would make fewer high-effort selections than ex-smokers and never-smokers.

Methods:

Current smokers (n = 25), ex-smokers (≥ 1 year quit, n = 23), and never-smokers (n = 19) completed the Effort Expenditure for Rewards Task in which participants select between low-effort and high-effort options to receive monetary rewards at varying levels of reward magnitude and probability.

Results:

Overall, participants selected more high-effort options as potential reward magnitude and expected value increased. Smokers did not make fewer high-effort selections overall, but smokers were less sensitive to the changes in magnitude, probability, and expected value compared to never-smokers. Smokers were also less sensitive to the changes in probability and expected value, but not magnitude, compared to ex-smokers. Among smokers and ex-smokers, less nicotine dependence was associated with an increased likelihood of high-effort selections.

Conclusions:

These results demonstrate the relevance of smoking status to effort-based decisions and suggest that smokers have diminished sensitivity to nondrug reward value. Among ex-smokers, greater pre-existing sensitivity to reward value may have been conducive to smoking cessation, or sensitivity was improved by smoking cessation. Future prospective studies can investigate whether effort-related decision making is predictive of smoking initiation or cessation success.

Implications:

Willingness to perform effort to achieve a goal and sensitivity to changes in reward value are important aspects of motivation. These results showed smokers have decreased sensitivity to changes in effort-related reward probability and expected value compared to ex-smokers and never-smokers. Potentially, improved sensitivity to rewards among ex-smokers may be a cause or consequence of smoking cessation. These findings may help explain why some smokers are able to achieve long-term abstinence.

Keywords: tobacco, smoking cessation, effort, reward, decision making

1.0. Introduction.

An important feature of healthy decision making is the willingness to exert effort (i.e., physical or mental energetic costs) to obtain rewards. Effort-based decision making weighs the value of a reward, as well as the likelihood of receiving it, against the amount of effort needed to obtain it (Salamone et al. 2016). Effort expenditure is costly and rewards may become devalued if the effort cost to obtain them is too high (Klein-Flugge et al. 2015, Inzlicht et al. 2018). Such cost/benefit analyses are an important process of goal-directed motivation, which allows individuals to overcome challenges in their environment in order to obtain goals (Bailey et al. 2016). There are individual differences in effort-based decision making that have important implications for psychiatry (Treadway et al. 2012, Barch et al. 2014, Hartmann et al. 2015), but how and why these individual differences contribute to psychiatric disorders is poorly understood.

Effort-based decision making is typically studied in both animal models and humans using paradigms that offer a choice between a low-effort/low-reward option and a high-effort/high-reward option (Salamone et al. 2007). For example, the effort expenditure for rewards task (EEfRT), a measure of willingness to exert effort in humans, presents a choice between a low-effort button-pressing task for a small monetary reward and a high-effort button-pressing task for a larger monetary reward. The probability of receiving a reward upon successful task completion, and the magnitude of the high-effort reward, vary across trials so that the selection of high-effort tasks is informed by the expected value (probability x magnitude) of the reward (Treadway et al. 2009). Thus, this task provides measures of effort-related sensitivity to expected reward values.

Extensive research with these types of paradigms in humans and animals indicates that increasing or decreasing dopamine transmission enhances or diminishes (respectively) high-effort responding for rewards (Caul and Brindle 2001, Floresco et al. 2008, Assadi et al. 2009, Kurniawan et al. 2011, Wardle et al. 2011, de Jong et al. 2015, Salamone et al. 2016). In accordance with this research, psychiatric and neurological disorders characterized by altered dopamine function (e.g., Parkinson’s disease, schizophrenia, and depression) affect effort-based decision making. (Treadway et al. 2012, Barch et al. 2014, Hartmann et al. 2015, Le Heron et al. 2018). Tobacco use disorder has also been associated with decreased mesolimbic dopamine function (Dagher et al. 2001, Fehr et al. 2008, Ashok et al. 2019), and long-term tobacco abstinence appears to normalize dopamine function (Rademacher et al. 2016). If effort-based decision making is related to these smoking-related changes in dopamine function, then there may be deficits in effort selection among smokers compared to ex-smokers and never-smokers.

Few studies have investigated the effects of nicotine or tobacco on effort-based decision making. One study showed an acute dose of nicotine reduced cognitive high-effort selection among some rodents, depending on their baseline performance (Hosking et al. 2014). Another study compared performance on a modified EEfRT between smokers’ tobacco-satiated baseline and following 1-week of abstinence. Smokers made more high-effort selections during abstinence (although this may have been a learning effect) (Hughes et al. 2017). Others have reported no effects of smoking status (i.e., smokers versus non-smokers) or tobacco withdrawal on physical effort discounting for money (Mitchell 1999, Mitchell 2004). Related studies using an effortful sorting task completed under rewarded and non-rewarded conditions showed that abstaining smokers failed to increase their effort in response to financial reward (Al-Adawi and Powell 1997, Powell et al. 2002), suggesting diminished effort-moderated reward sensitivity.

Smoking has also been shown to affect performance on other types of reward-based decision tasks in ways that may be either a cause or consequence of smoking. For example, smokers exhibit steeper delay discounting of rewards (i.e., prefer smaller/sooner over larger/later rewards) (MacKillop et al. 2011, Bickel et al. 2014). Furthermore, ex-smokers discount delayed rewards similar to nonsmokers, suggesting that the effects of smoking on delay discounting is either reversible, or smokers with less discounting are more likely to quit successfully (Bickel et al. 1999, Sweitzer et al. 2008). Such differences in behavior between current and ex-smokers could be informative of mechanisms that promote cessation success. There is certainly a motivational gap between wanting to quit smoking and actually maintaining abstinence, and breaking a smoking habit takes effort. In addition, differences in sensitivity to non-drug rewards could help determine which smokers continue to use tobacco versus which smokers are able to quit. Potentially, decrements in motivation and effort exertion may subserve broader impairments in decision making and may have implications for tobacco use cessation.

Previously, smokers and never-smokers were shown to have similar effort discounting (Mitchell 1999), but to our knowledge, the effects of current and past tobacco use disorder on effort selections have not been studied. In this cross-sectional study, we investigated the effects of smoking status on effort-based decision making using groups of smokers, ex-smokers, and never-smokers. We hypothesized that smokers would make fewer high-effort selections than ex-smokers, and that ex-smokers’ selections would be similar to never-smokers.

2.0. Methods.

2.1. Participants:

Participants were recruited from the Durham (n = 25) and Little Rock (n = 39) communities. Participants completed a phone interview and in-person screening session to determine eligibility. Eligible participants were between the ages of 18-55 years. Smokers smoked ≥ 10 cigarettes/day for ≥ 2 years and had an afternoon expired breath carbon monoxide (CO) concentration of ≥ 10 ppm (Vitalograph Inc, Lenexa, KS) or a morning urinary cotinine concentration > 100 ng/ml (NicAlert, Nymox Pharmaceutical Corporation, Hasbrouck Heights, NJ). Smokers did not report using electronic cigarettes or other nicotine/tobacco products. Ex-smokers smoked ≥ 10 cigarettes/day for ≥ 2 years, but reported no use of tobacco ≥ 12 months, and had breath CO ≤ 5 ppm and urinary cotinine < 100 ng/ml. Never-smokers smoked < 50 cigarettes in their lifetime, reported no use of tobacco or nicotine ≥ 6 months, and had breath CO ≤ 5 ppm and urinary cotinine < 100 ng/ml. Participants were screened using structured interviews for medical, psychiatric, and drug use history, and they were excluded if they reported serious health problems, or psychiatric problems, for which they were currently receiving treatment (e.g., liver-, lung-, heart-disease, depression, anxiety, obsessive-compulsive disorder, schizophrenia), a history of neurologic disorders or serious head trauma, and/or if they reported drug or alcohol dependence in the past 6 months (other than tobacco). Participants were also excluded if they tested positive for drugs (iCup, Alere Toxicology Services Portsmouth, VA), alcohol (Alco-Sensor III, Intoximeters Inc St. Louis, MO), or pregnancy (QuickVue+, Quidel Corporation San Diego, CA). Participants provided written informed consent and this protocol was approved by Duke University’s and University of Arkansas for Medical Sciences’ Institutional Review Boards.

2.2. Effort Expenditure for Rewards Task (EEfRT) (Treadway et al. 2009):

In each trial of the EEfRT, participants chose between two task options to earn money. Both task options consisted of repeated manual button presses within a short amount of time, and completed button presses were represented onscreen by the height of a vertical bar. The low-effort option required 30 button presses with the dominant index finger within 7 sec. The high-effort option required 100 button presses with the nondominant little finger within 21 sec. Participants were monitored during the task to ensure they used the correct finger.

In low-effort trials, participants could receive $1.00 if they completed the task on time. In high-effort trials, participants could receive a variable amount between $1.24 and $4.30 (i.e., reward magnitude). Across trials, the likelihood of receiving money upon successful completion of the task was either 12%, 50%, or 88% (i.e., reward probability). The probability level applied to both the low and high effort tasks. At the start of each trial, participants were shown the reward magnitude for both task options and the probability level. They had 5 sec to make a choice or else they would be randomly assigned to a task. Then, they completed the button press task and immediately received feedback informing them if the task was completed successfully or not, and whether they received money for that trial. Participants were told a single trial that resulted in money reward would be selected at random at the end of the EEfRT, and the participant would be given this amount as bonus pay.

Low-effort trials lasted approximately 15 sec, and high-effort trials lasted approximately 30 sec. Participants were told they had 20 min to play as many trials as possible. They were informed of the trade-off between choosing too many high-effort tasks early in the game then missing out on playing large-magnitude, large-probability trials later in the game to discourage the exclusive selection of either the low-effort or high-effort task. This also helped ensure that decisions were based on the expected value of the reward, and not based on a strategy to always select high-effort or low-effort trials. Participants were free to select as many high-effort or low-effort trials as they desired. Trials were presented in the same randomized order to all participants.

The EEfRT was programmed in Matlab (Mathworks Inc, Natick, MA) using the Psychtoolbox version 3.0.

2.3. Procedure.

As part of eligibility screening, participants completed a smoking history questionnaire, including current and past cigarettes per day and time to first cigarette in the morning (which is the most valid single measure of cigarette dependence severity)(Fagerstrom 2003, Baker et al. 2007). Time to first cigarette was measured using a Likert scale (1=within 5 min, 2=6-30 min, 3=31-60 min, 4=after 60 min). Smokers were asked to smoke as usual prior to the study visit. Participants completed a 3-hour study visit that included other behavioral measures that will be reported elsewhere. The EEfRT was administered at the end of the visit. At the start of the study session and immediately prior to the EEfRT, participants completed the Shiffman-Jarvik Withdrawal Scale (SJWS; (Shiffman and Jarvik 1976). This 32-item questionnaire assesses six common symptoms of nicotine withdrawal: craving, negative affect, arousal, somatic symptoms, appetite, and habit withdrawal. Instructions were to answer the questions according to how you feel during the present moment. As part of the EEfRT training, study staff read through the instructions with the participants, and then participants played 4 practice trials consisting of at least 1 low-effort and 1 high-effort task. After addressing any questions, participants commenced playing the EEfRT.

2.4. Data Analysis.

Participant demographics and location were analyzed using 1-way analysis of variance (ANOVA) with Bonferroni post hoc comparisons, Chi-square tests and independent-samples t-tests. Demographic variables that significantly differed between groups (i.e., age and race) were included as covariate/factor of no interest in subsequent analyses. EEfRT performance was analyzed with multivariate analysis of covariance (MANCOVA). The SJWS was analyzed using repeated-measures ANCOVAs.

EEfRT choice behavior was analyzed using Generalized Estimating Equation (GEE) models (Liang et al. 1986, Zeger and Liang 1986). GEE models take into account within-subject time-varying effects (e.g., changes in reward magnitude and probability across multiple trials) and between-subject fixed effects (e.g., smoking status). GEE models used an autoregressive working correlation matrix. The dependent variable was the selection of high- versus low-effort tasks and a binary logistic distribution modeled the probability of selecting the high-effort task. In all models, independent variables included reward magnitude, probability, expected value (magnitude x probability), and the trial number as a nuisance covariate to control for any effects of fatigue. Separate models tested the interactions between smoking status and magnitude, probability, and expected value. An additional model explored within-group individual differences in nicotine dependence among smokers and ex-smokers. The quasi likelihood under independence model criterion (QIC) was reported for model comparison (Pan 2001). Reward magnitude was divided into 4 blocks of equal trial number for illustration. Since participants could complete a variable number of trials during the 20 min of the EEfRT, only data from the first 50 trials was used for consistency. This analytical approach is similar to previous studies using the EEfRT (Treadway et al. 2012). Data was analyzed using SPSS v25 (Chicago: SPSS Inc).

3.0. Results.

Participant demographics and smoking histories are shown in Table 1. Groups did not differ in sex distribution, years of education or study location, although they did differ in age (F(2,66) = 8.5, p = .001) and racial distribution (Chi-square (4) = 13.8, p = .008). Never-smokers were younger than smokers and ex-smokers, and there were more African Americans among smokers than ex-smokers.

Table 1.

Participant demographics and smoking histories for smokers (SM), Ex-smokers (EX) and Never-smokers (NS), mean ± standard deviation, ns = not significant.

Smokers
(n=25)		 Ex-smokers
(n=23)		 Never-
smokers
(n=19)		 Significance
(p-value)
Sex (M/F) 8/17 11/12 10/9 chi-squared(2) = 2.2, p > .3
Age 41.2 ± 10 38.3 ± 8 29.7 ± 9 F(2,66) = 8.5, p = .001
NS < SM & EX
Years of education 13.8 ± 2 15.0 ± 3 15.3 ± 2 F(2,66) = 3.0, p > .05
Study location
(Durham/Little Rock)		 11/14 8/15 6/13 chi-squared(2) = .8, p > .6
Race
(White/African American/Asian)		 10/15/0 18/4/1 9/7/3 chi-squared(4) = 13.8, p = .008
African American SM > EX
Expired Breath CO (ppm) 20.0 ± 12 1.7 ± 2 1.5 ± 1 F(2,66) = 47.1, p < .001
SM > EX & NS
Urinary cotinine
(NicAlert score)		 4.4 ± 2 1.3 ± 1 0.9 ± 0.3 F(2,66) = 40.2, p < .001
SM > EX & NS
Cigarettes/day 16.6 ± 10 16.9 ± 11 (past) t(46) = 0.1, p > .9
Time to first cigarette in the morning* 1.8 ± 1 2.4 ± 1 (past) t(46) = 2.0, p > .05
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*

Time to first cigarette in the morning: 1=within 5 min, 2=6-30 min, 3=31-60 min, 4=after 60 min.

As expected, smokers had greater expired breath CO (F(2,66) = 47.1, p < .001) and urinary cotinine (F(2,66) = 40.2, p < .001) than ex-smokers and never-smokers. Ex-smokers’ past cigarettes/day and time to first cigarette in the morning (i.e., nicotine dependence severity) was similar to smokers’ current use. Ex-smokers reported quitting smoking an average of 9.2 years ago (SD = 7, range 1 to 23 years).

In the SJWS, a multivariate ANCOVA across all participants revealed a change in scores from baseline to pre-EEfRT (F(4,54) = 3.1, p = .023) and subsequent univariate tests revealed increased negative affect (F(1,54) = 4.3, p = .044) and increased appetite (F(1,54) = 8.4, p = .005) across all participants. There was no significant main or interaction effects of smoking status, nor were there any significant effects on arousal or somatic symptoms. Among smokers and ex-smokers, a multivariate ANCOVA revealed an effect of smoking status (F(2,40) = 36.0, p < .001) and subsequent univariate tests revealed greater craving among smokers than ex-smokers across both time points (F(1,41) = 47.3, p < .001). Smokers also reported a larger increase in craving from baseline to pre-EEfRT than ex-smokers (F(1,41) = 4.2, p = .048). There were no significant effects on habit withdrawal. (SJWS data for 1 ex-smoker was incomplete and excluded from the analysis.)

3.1. EEfRT performance.

Participants performed an average of 68 trials during the 20 min of the EEfRT (SD = 8, range 53-89 trials). Within the first 50 trials, participants made high-effort selections in 36% (SD = 14%) of trials, the ratio of high-effort tasks completed/selected was 73% (SD = 32%), and the ratio of low-effort tasks completed/selected was 95% (SD = 13%). None of the participants exclusively chose high-effort or low-effort tasks. MANCOVA analyses revealed groups did not differ on the number of trials (F(2,67) = 0.7, p > .4), high-effort trial selection (F(2,67) = 0.7, p > .5), low-effort trial selection (F(2,67) = 0.7, p > .5) ratio of high-effort tasks completed/selected (F(2,67) = 0.4, p > .6), or the ratio of low-effort tasks completed/selected (F(2,67) = 1.3, p > .2).

3.2. EEfRT GEE models.

Six independent GEE models were tested. Each model included reward magnitude, probability, expected value, trial number, and smoking status. Race and age were included as factor/covariate of no interest in all analyses because of group differences. An initial exploration of sex differences revealed no main effects of sex or sex x smoking status interaction; thus, sex was not included in the GEE analyses. Results are shown in Table 2.

Table 2.

GEE models of EEfRT choice behavior for smokers, ex-smokers, and never-smokers. Race and age were included in every model as a factor/covariate of no interest.

Predictors beta (b) SE Wald Chi-
squared p-
value		 Quasi
Likelihood
under
Independence
Model
Criterion
(QIC)
Model 1: Main effect of Smoking Status 4085.9
 Magnitude .283 .1017 .005
 Probability −.030 .4736 .949
 Expected Value .499 .1619 .002
 Trial Number −.016 .0028 < .001
 Smoking Status .051
Model 2: Smoking Status x Magnitude 4069.7
 Magnitude .269 .1442 .001
 Probability −.023 .4742 .961
 Expected Value .498 .1618 .002
 Trial Number −.016 .0028 < .001
 Smoking Status < .001
 Smoking Status x Magnitude .002
Model 3: Smoking Status x Probability 4062.2
 Magnitude .287 .1023 .005
 Probability .237 .6376 .848
 Expected Value .501 .1612 .002
 Trial Number −.016 .0028 < .001
 Smoking Status .002
 Smoking Status X Probability .002
Model 4: Smoking Status x Expected Value 4036.4
 Magnitude .295 .1022 .004
 Probability .026 .4711 .956
 Expected Value .538 .1690 .001
 Trial Number −.016 .0028 < .001
 Smoking Status < .001
 Smoking Status x Expected Value < .001
Model 5: Nicotine Dependence Severity 3001.3
 Magnitude .273 .1211 .024
 Probability .106 .5394 .844
 Expected Value .294 .1779 .099
 Trial Number −.012 .0026 < .001
 Smoking Status .835
 Nicotine Dependence Severity .169 .0841 .045
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The first model tested for main effects of reward magnitude, probability, expected value, trial number, and smoking status on high-effort selections. Across all participants, an increased likelihood of high-effort selections was predicted by larger magnitude (Wald Chi-squared (1) = 7.7, p = .005), and larger expected value (Wald Chi-squared (1) = 9.5, p = .002). Increasing trial number predicted a decreasing likelihood of high-effort selections (Wald Chi-squared (1) = 31.4, p < .001). Reward probability was not a significant predictor of high-effort selections.

There was a trend for a main effect of smoking status (Wald Chi-squared (2) = 6.0, p = .051). Follow-up analyses revealed that ex-smokers made more high-effort selections compared to never-smokers (EX > NS: b = .451, p = .022). Ex-smokers compared to smokers, and never-smokers compared to smokers, did not significantly differ.

The second model tested for the interaction between smoking status and reward magnitude. The interaction was significant (Wald Chi-squared (2) =12.0, p = .002), and follow-up analyses revealed the effect of magnitude on high-effort selections was greater among never-smokers compared to ex-smokers (NS > EX: b = .363, p = .030) and compared to smokers (NS > SM: b = .563, p < .001). Ex-smokers and smokers did not significantly differ. See Figure 1.

Figure 1.

Figure 1.

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Smoking status by reward magnitude. The graph shows changes in the percentage of high-effort selections across blocks of increasing reward magnitudes for the high-effort option (blocks of reward magnitude are for illustration only). Results varied by smoking status. The effect of reward magnitude on high-effort selections was greater among ex-smokers than smokers, p < .001. Error bars are standard error of the mean.

The third model tested for the interaction between smoking status and reward probability. The interaction was significant (Wald Chi-squared (2) = 12.2, p = .002) and follow-up analyses revealed that the effect of probability on high-effort selections was greater among never-smokers compared to smokers (NS > SM: b = 1.899, p = .001) and among ex-smokers compared to smokers (EX > SM: b = 1.167, p = .028). Never-smokers and ex-smokers did not significantly differ. See Figure 2.

Figure 2.

Figure 2.

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Smoking status by reward probability. The graph shows changes in the percentage of high-effort selections across increasing reward probability for both low- and high-effort options. Results varied by smoking status. The effect of reward probability on high-effort selections was greater among ex-smokers than smokers, p < .001. Error bars are standard error of the mean.

The fourth model tested for the interaction between smoking status and expected value. The interaction was significant (Wald Chi-squared (2) =15.9, p < .001), and follow-up analyses revealed that the effect of expected value on high-effort selections was greater among never-smokers compared to smokers (NS > SM: b = .817, p < .001) and among ex-smokers compared to smokers (EX > SM: b = .396, p = .019). Never-smokers and ex-smokers did not significantly differ.

The fifth and sixth models tested the main and interaction effects of nicotine dependence severity scores (i.e., time to first cigarette in the morning) among smokers and ex-smokers. There was a significant main effect of nicotine dependence (Wald Chi-squared (1) = 4.0, p = .045), revealing that an increased likelihood of making high-effort selections was predicted by less nicotine dependence. See Figure 3. Smoking status and nicotine dependence severity did not significantly interact.

Figure 3.

Figure 3.

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Nicotine dependence severity by high-effort selections. The graph illustrates the increased likelihood of making high-effort selections predicted by less current and past nicotine dependence (i.e., increased time to first cigarette) among smokers and ex-smokers, respectively, p = .026.

4.0. Discussion.

This study investigated group differences between current smokers, ex-smokers and never-smokers in effort-based decision making. Current smokers did not make fewer high-effort selections as hypothesized, but current and past nicotine dependence severity among smokers and ex-smokers (respectively) was associated with fewer high-effort selections in partial support of our hypothesis. This suggests that smoking may negatively affect high-effort selections, but only at high levels of tobacco dependence. However, the lack of an interaction with smoking status indicates this association remains true in spite of smoking cessation and may be related to a behavioral trait rather than a smoking state. Smoking status did affect reward sensitivity, i.e., the degree to which changes in reward magnitude and probability influence high-effort selections. Smokers were less sensitive to reward magnitude, probability and expected value compared to never-smokers, and smokers were less sensitive to probability and expected value compared to ex-smokers. Since EEfRT performance measures did not vary between groups, it is unlikely that these differences are due to different ability levels. As previously reported, high-effort selections decreased across trials suggesting an effect of fatigue independent of group (Treadway et al., 2009; Wardle et al., 2011). Altogether, these results indicate that smoking is associated with less sensitivity to changes in non-drug reward value during effort-based decision making and that long-term smoking cessation is associated with more sensitivity to non-drug reward values, although it cannot be determined from this cross-sectional study whether these differences are a cause or consequence of long-term cessation.

Presumably, maintaining tobacco abstinence depends on the cost/benefit analysis of the difficulty of quitting smoking compared to alternative rewards, such as improved health and/or financial savings. Motivation to remain abstinent must also depend on one’s sensitivity to these alternative rewards and willingness to expend effort to obtain them. Prior studies with smokers suggest that poor cessation outcomes are associated with more symptoms of anhedonia (i.e., diminished interest in normally enjoyable activities) (Leventhal et al. 2014), and diminished brain responses to positive non-drug stimuli relative to cigarette-related stimuli (Versace et al. 2014). The results of this study indicate that ex-smokers were more sensitive to non-drug reward value than smokers, suggesting either better baseline sensitivity that supported long-term abstinence, or recovery of sensitivity as a result of abstinence. The relationship between greater nicotine dependence severity and fewer high-effort selections also supports this conclusion, since less nicotine dependence severity is a predictor of smoking cessation success (Kozlowski et al. 1994). However, longitudinal studies of smoking cessation are needed to fully investigate whether effort-based decisions and reward sensitivity are predictive of smoking cessation outcomes.

In previous studies using the EEfRT, participants with major depressive disorder were less likely to make high-effort selections (Treadway et al. 2012, Yang et al. 2014, Yang et al. 2016) and were less sensitive to reward magnitude and probability than non-depressed controls (Treadway et al. 2012). Participants with schizophrenia were also less likely to make high-effort selections (Huang et al. 2016) and were less sensitive to reward magnitude and probability (Barch et al. 2014, Treadway et al. 2015, Huang et al. 2016). There is a high rate of co-morbidity between smoking and depression (Kendler et al. 1993) and smoking and schizophrenia (de Leon and Diaz 2005). It is likely that some of the participants in these previous studies were also smokers. Since smoking status in healthy adults can affect effort-related reward sensitivity, the smoking status of individuals with psychiatric disorders should be considered in future research. It is possible that some of the similarities across these studies are due to high rates of smoking, although there are likely other shared neural mechanisms as well. Indeed, all three psychiatric disorders (tobacco use disorder, depression, and schizophrenia) have been linked to dopaminergic dysfunction (Davis et al. 1991, Nestler and Carlezon 2006, Pierce and Kumaresan 2006).

Preclinical research shows that diminished mesolimbic dopamine decreases high-effort responding for rewards because of increased sensitivity to response costs, not because of a loss of hedonic reward value (Salamone et al. 2016). For example, dopamine receptor antagonism shifts the preference from a high-effort/high-value reward option to a low-effort/low-value reward option, but does not alter the preference for the high-value reward when no effort is required (Yohn et al. 2015). Building on this line of research, dopamine is hypothesized to regulate the strength of the relationship between effort expenditure and expected reward value, and that diminished dopamine produces a stronger biasing of effortful behavior by expected rewards (i.e., greater reward sensitivity) (Beeler 2012, Beeler et al. 2012). For this reason, we hypothesized that chronic smoking, which is associated with diminished dopamine (Dagher et al. 2001, Fehr et al. 2008), would be related to fewer high-effort selections (although without a dopaminergic manipulation, this relationship is speculative). We found that smokers did not make fewer high-effort selections and they were less sensitive to reward value compared with ex-smokers and never-smokers, which appears inconsistent with the dopamine regulation of effort expenditure hypotheses (Beeler 2012, Beeler et al. 2012). One explanation for the discrepancy may be that not all rewards are the same, and smokers may have different effort selection and reward sensitivity for tobacco compared to non-drug rewards, e.g., (Bickel et al. 1999). Another explanation may be that dopaminergic manipulations in animal models are not entirely representative of psychiatric disorders characterized by dopaminergic dysregulation. The mechanisms underlying smoking status differences in effort-based decision making are unknown, and future human-subjects research should use nicotine or dopamine manipulations, in addition to different reward types (e.g., tobacco versus money) and recency of smoking (e.g., smoking satiated versus withdrawn), to investigate the relationship between smoking status, effort selection, and reward sensitivity.

In the current study, we limited the study session to 3 hours to minimize the effects of tobacco withdrawal. It is possible that smokers were experiencing the early stages of withdrawal during the EEfRT and this may have affected their performance. However, other than craving, smokers did not report larger increases in withdrawal-related symptoms than ex-smokers and never-smokers. These symptoms were likely due to nonspecific effects of study participation rather than tobacco withdrawal. Furthermore, smokers’ diminished sensitivity to reward during effort-based decision making is unlikely to be the result of withdrawal. One study showed smokers made more high-effort selections during a smoking-withdrawn quit attempt compared to satiety (Hughes et al. 2017), and another study found no effects of 24-hour withdrawal on effort discounting (Mitchell 2004).

This study has several limitations, in particular the differences between groups in demographic characteristics (race and age), although group differences in reward sensitivity were significant while statistically controlling for these variables. Secondly, the percentage of high-effort selections reported here is lower (36%) than in other community-based samples (61-73%) (Barch et al. 2014, Huang et al. 2016). While high-effort selections were sensitive to reward outcomes as expected, performance in our study may have been affected by preceding tasks and the length of the session. This may reduce the generalizability of our results. Lastly, the relationship between effort and dopamine in this study is speculative, and little is known about the relationship between effort and acetylcholine.

In summary, smoking status affects the sensitivity to changes in reward value during effort-based decision making. These results are similar to previous studies on the effects of depression and schizophrenia on effort-based decision making. Effort-based decision making may be predictive of quit outcomes or sensitivity to alternative reinforcers, but this remains to be tested. Future studies should further investigate the relationship between dopaminergic function, effort-based decision making, and drug use cessation.

Acknowledgements:

The authors would like to thank Dr. Michael Treadway for providing the EEfRT, and Bryana Roberts and Deborah Hodges for help with data collection.

Role of Funding Source: Funding provided by NIH NIDA K01 DA033347 and by the Arkansas Biosciences Institute, the major research component of the Arkansas Tobacco Settlement Proceeds Act of 2000.

Footnotes

Conflict of Interest: The authors declare no conflicts of interest.

References

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