Research report 659
Delay and probability discounting of multiple commodities in smokers and never-smokers using multiple-choice tasks Dmitri V. Poltavski and Jeffrey N. Weatherly The purpose of the present study was to investigate temporal and probabilistic discounting in smokers and never-smokers, across a number of commodities, using a multiple-choice method. One hundred and eighty-two undergraduate university students, of whom 90 had never smoked, 73 were self-reported light smokers (< 10 cigarettes/day), and 17 were heavy smokers (10 + cigarettes/day), completed computerized batteries of delay and probability discounting questions pertaining to a total of eight commodities and administered in a multiple-choice format. In addition to cigarettes, monetary rewards, and health outcomes, the tasks included novel commodities such as ideal dating partner and retirement income. The results showed that heavy smokers probability discounted commodities at a significantly shallower rate than never-smokers, suggesting greater risk-taking. No effect of smoking status was observed for delay discounting questions. The only commodity that was probability discounted significantly less than others was
‘finding an ideal dating partner’. The results suggest that probability discounting tasks using the multiple-choice format can discriminate between non-abstaining smokers and never-smokers and could be further explored in the context of behavioral and drug addictions. Behavioural c 2013 Wolters Kluwer Health | Pharmacology 24:659–667 Lippincott Williams & Wilkins.
Introduction
Studies using DD tasks have demonstrated that cigarette smokers discount the value of delayed monetary rewards more than their nonsmoking counterparts (e.g. Bickel and Marsch, 2001; Baker et al., 2003; Reynolds, 2006). Furthermore, several investigators reported that smokers showed more pronounced DD of cigarettes compared with monetary reinforcers and health consequences (Bickel et al., 1999; Mitchell, 2004a; Estle et al., 2007; Odum and Baumann, 2007), which suggests that delayed drugs lose their subjective value to a greater extent than other stimuli.
A growing number of studies using behavioral, neurobiological, and imaging techniques have established a strong link between impulsivity and drug abuse, including cigarette smoking (Perry and Carroll, 2008). In reference to drug addiction, impulsivity has been defined as preference for immediate over delayed gratification without forethought or consideration of outcomes including health (Mitchell, 2004a). In other words, drug abuse may be explained by discounting of the value of the delayed benefits of abstinence in favor of the immediate drug effects. One way to study this aspect of impulsivity in humans and animals is by using delay discounting (DD) procedures, during which a subject is typically asked to choose between a small reinforcer delivered immediately and a larger reinforcer delivered after a delay (Smith and Hantula, 2008). With human participants the typical procedure for measuring DD is to present them with a hypothetical binary choice (e.g. Would you prefer $95 now or $100 in 1 year?). The reinforcers are typically monetary, although hypothetical health outcomes (e.g. the onset of a serious drug-related illness), food, and drug use (e.g. cigarettes) have also been used (e.g. Estle et al., 2007; Odum and Baumann, 2007).
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Behavioural Pharmacology 2013, 24:659–667 Keywords: delay discounting, human, multiple-choice format, nicotine addiction, probability discounting, smokers Department of Psychology, University of North Dakota, Grand Forks, North Dakota, USA Correspondence to Dmitri V. Poltavski, PhD, Department of Psychology, University of North Dakota, 319 Harvard St. Stop #8380, Grand Forks, ND 58202, USA E-mail: [email protected] Received 19 September 2012 Accepted as revised 24 September 2013
Some studies, however, have not found differences in DD for cigarettes compared with monetary rewards (Field et al., 2006). These inconsistent findings may be related to the lack of control over nicotine dependence in smokers, as greater discounting of monetary gains has been correlated with the number of daily cigarettes (Ohmura et al., 2005) and the level of nicotine dependence (Heyman and Gibb, 2006). Smoking abstinence has also been shown to produce increases in cigarette discounting and discounting of monetary rewards compared with conditions of ad lib cigarette smoking (smoking at a habitual rate; Mitchell, 2004b; Field et al., 2006). While temporal discounting in drug-dependent and nondependent populations has been studied extensively, less is known about probability discounting in special populations (e.g. smokers, gamblers, etc). Probability discounting refers to the reduction of the subjective value of a probabilistic outcome as the probability of the outcome DOI: 10.1097/FBP.0000000000000010
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660 Behavioural Pharmacology 2013, Vol 24 No 8
decreases. In typical human probability discounting studies, participants are asked to choose between a large, probabilistic outcome and a smaller, certain outcome. Conceptually, this type of discounting is similar to temporal discounting (the reduction of the present, subjective value of a delayed outcome as a function of the delay), and a number of researchers have speculated that temporal and probability discounting are related (Stevenson, 1986; Prelec and Loewenstein, 1991; Rachlin et al., 1991; Green and Myerson, 1996). Specifically, studies have shown that, similarly to temporal discounting, probability discounting can be described by a hyperbolic curve Shead and Hodgins (2009). Richards et al. (1999) and Myerson et al. (2003) also reported a correlation between rates of temporal and probability discounting, but Myerson et al. (2003) suggested the possible presence of different underlying mechanisms behind probability and DD. Similarly, when probability discounting tasks are used with smokers, the support for the common process hypothesis has so far been mixed. Some reports indicated that temporal discounting differences observed between smokers and nonsmokers were also found with probability discounting (Mitchell, 2004a and 2004b; Reynolds et al., 2004; Yi et al., 2007), while others (Mitchell, 1999; Ohmura et al., 2005) did not find any differences between smokers and nonsmokers in probability discounting. In addition, among smokers, Yi and Landes (2012) recently reported a divergence between temporal and probability discounting as a function of smoking abstinence. In their study, greater DD of monetary gains and losses was observed after smoking abstinence compared with ad lib smoking, with no changes found in probability discounting of monetary gains, losses, and cigarettes. The researchers suggested that smokers’ decision making regarding probabilistic outcomes is not influenced by smoking abstinence, withdrawal, or craving. They further contended that, in contrast to theoretical modeling, probability discounting is not a measure of real-life risktaking or risk preference, as no studies so far have shown less discounting in risk-prone populations (Holt et al., 2003; Reynolds et al., 2004; Yi et al., 2007; Madden et al., 2009), often finding the opposite. In theory, risk-taking should be associated with less discounting of probabilistic gains, as individuals are taking greater risks to obtain them in the face of an increasing possibility of getting nothing. Another issue in investigations using temporal and probability discounting procedures is the choice of commodities. To date, few studies have reported inclusion of nondrug specific, nonmonetary commodities such as health consequences (e.g. Johnson et al., 2007), healthy behaviors (e.g. Bradford, 2010), and food (e.g. Odum and Baumann, 2007). Some researchers have suggested that cigarettes and monetary gains seem to fall into one domain of discounting while other nonconsumable commodities (i.e. ideal dating partner, medical treatment,
and retirement income) fall into another (Weatherly et al., 2010; Weatherly and Derenne, 2011). If commodities such as drugs fall within the same domain as those of monetary outcomes, then this result would potentially explain why some researchers have found differences in discounting between substance abusers and nonabusers for hypothetical monetary outcomes. Theoretically, an argument can be made that if one were able to alter how individuals discount one commodity within that domain, then one might also alter how those individuals discount other commodities within that domain, which may have therapeutic implications for cigarette smokers. When evaluating delay and probability discounting in normal and drug-dependent populations, procedural nuances may also play a role. Traditionally, DD in smokers has been assessed using binary-choice tasks that involve a series of evaluations of temporal trade-offs between two fixed reward amounts, one of which is available immediately while the other is delayed by some time period (e.g. Bickel et al., 1999; Bickel and Marsch, 2001; Mitchell, 2004b; Reynolds, 2006; Estle et al., 2007; Odum and Baumann, 2007). This methodology has been criticized for the presentation of the same reward sequences over a potentially extensive number of delay periods, which may result in a learned pattern of responding on the part of the participant, rather than reflecting the degree of his/her impulsivity (Smith and Hantula, 2008). The binary procedure also requires that participants make a large number of choices, which is time consuming and arguably leads to a reduction in ecological validity (i.e. organisms normally have a single choice to make; Weatherly and Derenne, 2011). In addition, in probability discounting studies comparing smokers and nonsmokers, group differences may not be apparent due to the ‘floor effect’ resulting from greater inclusion of low probabilities (Yi et al., 2007). Other alternatives to the presentation of multiple binary choices have been suggested, including the fill-in-the blank (FITB) method (Chapman, 1996) and the multiplechoice (MC) method (e.g. Beck and Triplett, 2009). The FITB procedure asks participants to indicate themselves the smallest value of some larger-later reward with which he/she would be content if it was available immediately. However, because the participant has to generate an answer, rather than choose between presented options, the FITB procedure could theoretically be a more cognitively challenging task than the binary-choice procedure (Smith and Hantula, 2008). With the MC method, on the other hand, the participant identifies the smallest subjective value from a limited list of choices. Weatherly and Derenne (2011) directly compared delay discounting rates across five commodities (finding one’s ideal dating partner, getting 100 packs of free cigarettes, winning the sum of $100 000, being owed the sum of
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Discounting in smokers Poltavski and Weatherly 661
$100 000, and obtaining one’s ideal body image through diet and exercise) using both the FITB and the MC method. They found that the FITB method was more sensitive to differences between outcomes than the MC method. In contrast, another interpretation was that the MC method produced more systematically similar rates of discounting across outcomes than did the FITB method. In their follow-up study, Weatherly and Derenne (2012) used seven different commodities (i.e. finding one’s ideal dating partner, winning the sum of $100 000, obtaining one’s ideal body image through diet and exercise, winning $1000, treating a serious medical condition, saving money for retirement, and passing educational reform legislation) to test whether differences in probability discounting would be observed for the FITB and MC methods of data collection. The results demonstrated that the FITB method produced steeper rates of discounting than the MC method, a result that was also found for DD. The researchers also reported good test–retest reliability for probability discounting, although to a potentially lesser extent than for rates of DD. On the basis of the above findings one could speculate that discounting measured by the FITB method maximizes the individual’s trait characteristics related to decision making whereas the binary-choice and MC methods maximize procedural control by the researcher (i.e. state characteristics). Finally, past discounting research has also suggested that the valence of the outcome (i.e. whether it is a gain or loss) is influential in what rate of discounting is observed (e.g. Hardisty and Weber, 2009). In their most recent study using both monetary gains ($1000 vs. 100 000) and equivalent monetary losses ($1000 vs. 100 000) presented using the MC method, Weatherly and Derenne (2013) reported significant magnitude effects (rate of discounting varies inversely with the magnitude of the outcome) for delayed discounting of gains and losses but not for probability discounting. Probabilistic gains, however, were discounted more steeply than probabilistic losses, suggesting that participants are more prone to risk with losses than with gains, more averse to risk with gains than with losses, or both. The purpose of the present study was to explore delay and probability discounting, across the range of commodities previously used in the studies by Weatherly and Derenne (2011, 2012), as a function of smoking status. We hypothesized that cigarette smokers would discount the value of monetary rewards more than their nonsmoking counterparts in both delay and probability discounting conditions using the MC method. We also hypothesized that they would discount cigarettes to a greater extent than monetary rewards.
Methods Participants
The participants were 182 undergraduate students from the University of North Dakota (151 women and 45 men),
with a mean age of 20 years. The majority of the participants were White (87.8%), single (57.7%) and had an annual income of less than $10 000 (85.2%). As part of voluntary extra credit opportunities available in conjunction with some of the participants’ undergraduate psychology classes, the students completed a packet of online instruments that included their demographic information, smoking status, the Minnesota Nicotine Withdrawal Scale (MNWS), the Tobacco Cravings Questionnaire (TCQ), and the Fagerstrom Test for Nicotine Dependence (FTND), as well as MC instruments listing eight commodities (four commodities per question type). There were 92 self-identified never-smokers (have never tried cigarettes, even one or two puffs) and 90 selfidentified smokers, 24 of whom reported smoking every day (26.9%). In addition, 11.1% (n = 10) of the selfidentified smokers also reported daily use of chewing tobacco. Nineteen percent of the smokers (n = 17) reported smoking more than 10 cigarettes/day.
Materials and procedure
Participants completed the materials online by the SONA Systems Ltd (Version 2.72; Tallinn, Estonia) experiment management system. The system ensured that participants who completed the study as a member of one particular course were not eligible to participate again if they were enrolled in another psychology course. Before proceeding to the questionnaires, each participant filled out an informed consent form as approved by the Institutional Review Board at the University of North Dakota and a demographic form that asked participants about the information reported in Table 1. The questionnaire battery consisted of the FTND, TCQ, MNWS, and an MC discounting questionnaire. The FTND (Heatherton et al., 1991) was used in the present study to assess smokers’ level of nicotine dependence. It is a six-item questionnaire that primarily emphasizes physical nicotine tolerance and is widely used in combination with other measures of nicotine dependence (Shiffman et al., 2005). The TCQ has been shown to be a reliable and valid tool in assessment of tobacco cravings (Heishman et al., 2003). The questionnaire comprises 17 items grouped around four separate factors that best describe tobacco cravings: (a) emotionality, or smoking in anticipation of relief from withdrawal symptoms or negative mood, (b) expectancy, or anticipation of positive outcomes from smoking, (c) compulsivity, or lack of control over tobacco use, and (d) purposefulness, or intention and planning to smoke for positive outcomes. Nicotine withdrawal was assessed using the MNWS (Hughes and Hatsukami, 1986), which is an eight-item scale listing only one item for each of the following eight withdrawal symptoms: irritability/anger, anxiety/tension,
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Table 1 Means and SDs for measures of nicotine dependence (Fagerstrom Test of Nicotine Dependence), withdrawal (Minnesota Nicotine Withdrawal Scale), and cigarette cravings (Tobacco Cravings Questionnaire) by smoking level (light vs. heavy) Variables
Light smoker (N = 73)
FTND total score Mean SD TCQ total score Mean SD TCQ emotionality scale Mean SD TCQ expectancy scale Mean SD TCQ purposefulness scale Mean SD TCQ compulsivity Mean SD MNWS total score Mean SD
1.35* 1.61
Heavy smoker (N = 17) 3.90 2.11
68.01 18.03
67.57 13.18
19.73 6.58
18.80 8.27
15.75 3.71
15.45 3.85
17.56 4.50
18.73 5.39
17.56 7.55
16.90 6.24
13.02 6.90
11.80 8.10
FTND, Fagerstrom Test of Nicotine Dependence; MNWS, Minnesota Nicotine Withdrawal Scale; TCQ, Tobacco Cravings Questionnaire. *P < 0.05.
difficulty concentrating, restlessness, increased appetite, depressed or sad mood, craving, and impatience. The scale ranges from 0 to 4 with 0 = ‘not present’ and 4 = ‘severe’. The summary score of the eight items minus the craving item score has been recommended to measure the participants’ overall withdrawal symptomatology (Hughes and Hatsukami, 1998). The MC discounting measure consisted of questions relating to probability discounting of four commodities and DD of four commodities. Probability discounting questions comprised a series of hypothetical questions about $1000 won, $100 000 won, ideal dating partner, and cigarettes. DD items were represented by questions concerning $1000 students were owed, $100 000 they were owed, retirement income, and effectiveness of medical treatment. Both types of questions were identical to those used in Weatherly and Derenne (2011, 2012); the exact wording for each question can be found in the appendix. Five delays were tested for each outcome (6 months, 1, 3, 5, and 10 years) of the delay discounting task. Thus, the participant answered five questions pertaining to each outcome. Each participant answered all five questions about a particular outcome before being presented with questions about another outcome. For each delay discounting question, the participant was asked to choose, from a finite number of choices (n = 51), the smallest number of the outcome she/he would accept rather than waiting a certain period of time for the full number. On the probability discounting task, participants were asked to select (from 51 possible choices) which dollar
amount or percentage they would be willing to accept rather than having a probabilistic chance for the full outcome. Five questions were asked about each outcome, with the probability of receiving the full outcome varying across questions. The five probabilities employed were 1, 10, 50, 90, and 99%. For both types of discounting questions, probabilities and delay intervals were randomly ordered. All participants then completed the questions for a particular set of commodities in the same random order. Data analysis
Using nonlinear regression, responses to the DD questions were fit to the following hyperbolic equation (e.g. Mazur, 1987): V ¼ A=ð1þkDÞ; ð1Þ where V represents the subjective monetary value of the delayed outcome, A represents the amount of the commodity, D represents the delay, and k is a free parameter. The value of k describes the rate at which discounting occurs, with higher values of k indicating steeper rates of (i.e. more) discounting. Similarly, on the probability discounting task the dollar amounts/percentages across the different probabilities were fitted to a hyperbolic function (Rachlin et al., 1991): V ¼ A=ð1þhYÞ; ð2Þ where V stands for the subjective value of the amount, A stands for the amount of the probabilistic outcome, Y stands for the odds against receiving the outcome [i.e. (1 – p)/p], and h is the free parameter that represents the rate of probability discounting and thus serves as the dependent measure of discounting. In Eq. (2), high values of h represent steep rates of discounting when the outcome is uncertain. Low values represent little discounting. Using probability plots available in SPSS 19.0 (IBM Corp. Released 2010. IBM SPSS Statistics for Windows, Version 19.0. Armonk, NY: IBM Corp.) distributions of k and h for all commodities showed deviations from normality. Deviations from the normality assumption warranted logarithmic transformations using the natural log (ln). For comparisons with discounting rates observed in other studies, descriptive statistics for untransformed k and h values, which also reflect differences in variability between smokers and nonsmokers, are presented in Table 3. R2 values were calculated for each participant to measure goodness of fit of the hyperbolic function for each discounting commodity. As can be seen in Table 2, mean R2 values varied from 0.51 (DD: $1000 owed) to 0.70 (probability discounting: $1000 won), while median values ranged from 0.51 (probability discounting: perfect mate) to 0.92 (probability discounting: $100 000 won).
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Discounting in smokers Poltavski and Weatherly 663
2 Table 2 Mean and median R values and 95% confidence intervals for R2 means, for hyperbolic curve fits across commodities
Question type Probability
Delay
Commodity
Mean
95% CI
Median
Ideal dating partner Cigarettes $100 000 won $1000 won $100 000 owed $1000 owed Retirement income Medical treatment
0.53 0.64 0.64 0.66 0.55 0.53 0.56 0.55
0.46–0.59 0.57–0.70 0.58–0.71 0.60–0.72 0.49–0.61 0.47–059 0.49–0.62 0.49–0.61
0.51 0.85 0.92 0.92 0.66 0.57 0.68 0.65
CI, confidence interval.
Means with 95% confidence intervals and medians with interquartile rangesa for ln-transformed k and h values for neversmokers (n = 92), light smokers (n = 74), and heavy smokers (n = 16) Table 3
Question type Probability
Delay
Variables
Mean
95% CI
Median
IQR
– 3.92 to – 2.5 – 4.65 to – 3.25 – 6.35 to – 1.33
– 3.08 – 4.93 – 5.63
5.04 4.09 6.51
– 1.69 to – 0.09 – 2.46 to – 0.44 – 4.82 to – 0.08
– 0.15 – 0.76 – 3.05
4.37 5.08 6.11
– 2.42 to – 1.00 – 2.58 to – 1.01 – 5.47 to – 1.48
– 0.54 – 0.76 – 4.69
4.56 4.98 5.98
– 1.96 to – 0.40 – 2.80 to – 1.22 – 4.34 to 0.07
– 0.31 – 1.24 – 2.43
3.94 4.56 5.18
– 5.52 to – 4.82 – 5.77 to – 5.06 – 5.68 to – 4.13
– 5.38 – 5.67 – 4.75
1.90 1.41 1.69
– 5.48 to – 4.56 – 5.31 to – 4.40 – 6.30 to – 3.48
– 5.14 – 5.12 – 5.36
1.88 1.53 2.63
– 5.32 to – 4.52 – 5.10 to – 4.20 – 5.62 to – 3.57
– 5.06 – 4.90 – 4.44
1.82 1.96 2.68
– 5.43 to – 4.73 – 5.57 to – 4.91 – 6.03 to – 4.47
– 5.09 – 5.18 – 5.44
1.64 1.65 1.04
ln h Ideal dating partner Never-smoker – 3.21 Light smoker – 3.95 Heavy Smoker – 4.17 Cigarettes Never-smoker – 0.89 Light smoker – 1.45 Heavy Smoker – 2.45 $100 000 won Never-smoker – 1.71 Light smoker – 1.79 Heavy smoker – 3.47 $1000 won Never-smoker – 1.18 Light smoker – 2.01 Heavy smoker – 2.14 ln k Retirement income Never-smoker – 5.17 Light smoke – 5.42 Heavy smoker – 4.90 $100 000 owed Nonsmoker – 5.02 Light smoker – 4.85 Heavy Smoker – 4.89 $1000 owed Never-smoker – 4.92 Light smoker – 4.65 Heavy Smoker – 4.60 Medical treatment Never-smoker – 5.08 Light smoke – 5.24 Heavy Smoker – 5.25
CI, confidence interval; IQR, interquartile range. a Difference between the 75th and 25th percentiles.
These values indicated that the hyperbolic function fit the data better for some commodities than for others. For this reason the area under the curve (AUC) (Myerson et al., 2001) was also calculated for each outcome type for each participant. The AUC is an atheoretical measure that is normally distributed and has been used as a measure of temporal and probability discounting for money, drugs, food, and other consumable outcomes (e.g. Odum and Rainaud, 2003; Field et al., 2006, 2007; Odum et al., 2006; Estle et al., 2007). The AUC is applicable to various issues in the study
of discounting, including the effects of multiple types of reward (e.g. money vs. cigarettes), different participant populations (e.g. smokers vs. nonsmokers), and variable reward amounts on the rate of discounting (Myerson et al., 2001). To calculate the AUC, the delays, probabilities, and indifference points were first normalized, and then the AUC was computed using the equation: ðx2 x1 Þ ½ðy1 þy2 Þ=2; where x1 and x2 are successive delays, and y1 and y2 are the subjective values associated with these delays/probabilities. This equation produces the AUC by summing the areas of the trapezoids calculated across the different delays/probabilities (see Myerson et al., 2001, for details). The AUC ranges from 1 to 0 (larger values indicate less discounting). Statistical analyses
Data were first separated by question type (delay vs. probability discounting). Within each discounting question type, separate 3 4 ANOVAs were conducted for each data-fitting model (hyperbolic function and AUC). Self-reported smoking status was a between-subjects variable (never-smoker, light smoker, heavy smoker) and delay/probability discounting was a within-subjects variable with four levels (probability discounting questions: ln-transformed h values and AUCs for ideal dating partner, cigarettes, $100 000 won, and $1000 won; DD questions: ln-transformed k values and AUCs for retirement income, $100 000 owed, $1000 owed, and medical treatment). Significant main effects for variables with more than two levels were programmed in SPSS 19.0 (IBM Corp. Released 2010. IBM SPSS Statistics for Windows, Version 19.0. Armonk, NY: IBM Corp.) for an automatic follow-up with the Bonferroni procedure (also known as Dunn–Bonferroni). This post-hoc procedure was chosen on the basis of its greater power and narrower 95% confidence intervals than other methods (e.g. Tukey’s HSD) when the number of comparisons equals or is less than 4 (Myers and Well, 2003).
Results Descriptive statistics
Overall, the sample of 90 smokers had a total score of 1.59 (SD = 1.86) on the FTND, indicating very low dependence (Heatherton et al., 1991). At the same time, heavy smokers had significantly greater nicotine dependence scores (mean = 3.90; SD = 2.11) than light smokers (mean = 1.35; SD = 1.61; t = – 4.42, P < 0.01), placing them in the low-moderate dependence category (Heatherton et al., 1991). There were no statistically significant differences between the two groups of smokers on measures of craving and withdrawal (see Table 1 for details). As a whole, the sample of smokers indicated moderate cigarette cravings [total TCQ score = 67.79 (SD = 17.28) out of maximum 119] and mild withdrawal [average total sum = 12.30 (SD = 6.97) out of maximum 36].
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664 Behavioural Pharmacology 2013, Vol 24 No 8
Statistical analyses Hyperbolic curve
A significant main effect of commodity type was found for DD questions using ln-transformed k-values [F(3, 435) = 3.15, P < 0.025]. Pairwise comparisons using the Bonferroni adjustment showed significant differences between the ln-transformed k mean for $1000 owed (Mln k = – 4.73) and corresponding means for medical treatment (Mln k = – 5.24; P < 0.02) and retirement income (Mln k = – 5.17; P < 0.02). The difference between discounting rates for $1000 owed and $100 000 owed (Mln k = – 5.01) was in the expected direction but did not reach statistical significance (P = 0.06). These results suggest steeper DD for $1000 owed than for medical treatment, retirement income, or $100 000 owed. There was no significant main effect of smoking status and no significant commodity type by smoking status interaction. For probability discounting a significant main effect was found for commodity type [F(3, 474) = 14.72, P < 0.01] and smoking status [F(1, 158) = 2.95, P < 0.05]. Pairwise comparisons with the Bonferroni adjustment revealed that the commodity with a significantly shallower rate of discounting compared with all others was ‘finding an ideal dating partner’ (Mln h = – 3.71, P < 0.01). If we assume, after Shead and Hodgins (2009), that risk-seeking individuals exhibit shallower discounting of gains (they tend to overweight the possibility of a large gain while underweighting the possibility of no gain), then the participants exhibited significantly more risk in choosing the odds of finding a perfect mate than winning different amounts of money or obtaining cigarettes. Using the Bonferroni test to follow up on the significant main effect of smoking status, it was found that heavy smokers exhibited a significantly shallower rate of discounting of probabilistic gains (Mln h = – 2.95, SE = 0.58; P < 0.05) than never-smokers (Mln h = – 1.66, SE = 0.25), suggesting a greater level of risk-taking. The discounting pattern of light smokers fell somewhere in between, but failed to reach statistical significance when compared with either group (Mln h = – 2.20, SE = 0.25). Ln-transformed k and h values for all commodities are presented in Table 3.
Area under the curve
A 4 (commodity type) 3 (smoking status) mixed ANOVA for DD questions, using mean AUC values, did not show significant main effects for either the type of commodity or smoking status. Their interaction was also not significant. For probability discounting questions a significant main effect was found for commodity type [F(3, 537) = 37.41, P < 0.01] and smoking status [F(1, 158) = 2.93; P < 0.05]. Pairwise comparisons with the Bonferroni adjustment revealed that the commodity with a significantly greater mean AUC compared with all others was ‘finding an ideal dating partner’ (MAUC = 0.64, P < 0.01). Figure 1 illus-
trates these findings. A post-hoc Bonferonni test used to break down the significant main effect of smoking status showed that heavy smokers (10 + cigarettes/day) had a significantly higher mean AUC (mean = 0.541, SE = 0.06) than never-smokers (mean = 0.41), suggesting greater risktaking while overestimating the probability of a gain (shallower discounting). These findings are illustrated in Fig. 2.
Discussion One of the most robust findings of the present study, supported by the AUC analyses, concerned a significantly smaller overall rate of probability discounting of an ideal dating partner both by smokers and never-smokers. This finding is in agreement with our previous work evaluating DD across a number of commodities (Weatherly et al., 2010). In that study, participants discounted dating partner the least, compared with money, cigarettes, and ideal body image. It is likely that among young adults (mean age of 20 years in our study), including nondeprived drug-dependent individuals, the incentive salience of a dating partner is quite strong, as most young people may still be searching for their perfect mate. Indeed, in our study close to 58% reported being single (not in a relationship). As a result they may overweigh the possibility of finding their ideal dating partner and underestimate the probability of finding no partner. In addition, probability discounting questions that included cigarettes, monetary rewards, and ideal dating partner showed greater sensitivity to smoking status, with heavy smokers displaying significantly lower rates of probability discounting than never-smokers. The obtained results, however, are novel, in that when probability discounting questions were used in previous studies including monetary gains and losses, smokers tended to probability discount more than nonsmokers (e.g. Reynolds et al., 2004; Yi et al., 2007; Yi and Landes, 2012). At the same time, greater risk-seeking in drugdependent populations has been theoretically suggested to show shallower probabilistic discounting, which corresponds to placing relatively less weight on the uncertainty and more on the amounts involved, regardless of whether the outcomes are gains or losses (Shead and Hodgins, 2009). One of the possible reasons why the pattern of the results in this study is more consistent with the theoretical model of drug dependence and probability discounting than previously shown is that in the present study the MC method was used, compared with the binary-choice procedures used in other studies (Reynolds et al., 2004; Yi et al., 2007; Yi and Landes, 2012). Yi et al. (2007) suggested that procedural nuances could indeed play a role in producing statistically significant between-group (smokers vs. nonsmokers) differences, as they are likely
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Discounting in smokers Poltavski and Weatherly 665
Fig. 1 0.7000
∗ 0.626
Mean AUC
0.6000
0.5000
0.407 0.4000
0.405
0.402
0.3000 $100 000 won
$1000 won
Cigarettes Ideal dating partner
Error bars: 95% Cl
Area under the curve (AUC) means and 95% confidence intervals (CIs) for discounting rates of probability commodities collapsed over smoking status.*Significantly different from discounting rates for other commodities at a = 0.05.
Fig. 2
Mean AUCs across probability commodities
completion of the instruments. As a result, smokers in the present study showed only mild withdrawal on the MNWS. Yi and Landes (2012) recently reported significantly greater DD of monetary gains and losses after 24-h smoking abstinence compared with ad lib smoking. At the same time, the authors suggested that smokers’ decision making regarding probabilistic outcomes is not influenced by smoking abstinence, withdrawal, or craving.
c∗ 0.60 b a 0.40
0.20
0.409
0.472
0.541
0.00 Never smoker
Light smoker Heavy smoker Smoking status Error bars: 95% CI
Means and 95% confidence intervals (CIs) for area under the curve (AUC) across four probability discounting commodities by smoking status.*AUC mean for smoking status marked ‘c’ is significantly different from ‘a’ but not ‘b’ at a = 0.05.
to be affected by magnitudes and probabilities included in the procedure. In contrast to some of the previous reports (e.g. Bickel et al., 1999; Mitchell, 2004a; Odum and Baumann, 2007) and our original hypothesis, cigarette smokers in our study did not differ from nonsmokers in DD of monetary rewards, retirement income, or medical treatment. One of the reasons for the null findings could be the fact that we did not require nicotine deprivation before the
Furthermore, even our heavy smokers had a fairly low score on the FTND (mean = 3.90). This could be problematic, as in the study by Sweitzer et al. (2008) low-dependence smokers were more similar to neversmokers than to high-dependence smokers in their discounting of delayed rewards, implying that the smoking/discounting relationship may be driven by a subgroup of highly dependent smokers. In their study the number of cigarettes smoked per day was not significantly predictive of the smokers’ delayed discounting. In contrast, another explanation of the null findings on the DD task may involve the shape of the distribution of the DD data. Typically, binary measures of temporal discounting in both normal and drug-dependent populations yield data that can be well described with a hyperbolic curve, in which reported mean and median R2 are often greater than 0.80 (e.g. Sweitzer et al., 2008; Shead and Hodgins, 2009; Yi and Landes, 2012). In the present study mean R2 values were between 0.53 and 0.66 for probability discounting questions, and between 0.53 and 0.56 for DD questions (see Table 2 for details).The observed poor hyperbolic model fit may be related to increased variability in the data (random responding), which in the present study may have been more likely in the group of smokers. Specifically, never-smokers showed a significantly greater proportion of variance accounted for (R2 = 0.71, SD = 0.30) than smokers (R2 = 0.61, SD = 0.31) on probability discounting questions (t = 1.98, P = 0.05). The trend was similar on DD questions (R2never-smokers = 0.60 vs. R2smokers = 0.52) but did not reach statistical significance (t = 1.51, P = 0.13). Thus, to verify the obtained results after fitting the data to a hyperbolic curve, we also performed analyses using AUC, which is thought to be an atheoretical measure of discounting capable of accounting for all observed data (Myerson et al., 2001). While in most instances the results of the analyses of the AUCs were congruent with those of transformed k and h parameters, for DD questions, we observed a loss of sensitivity, resulting in nonsignificant findings. This result was not unexpected. Yi and Landes (2012) recently reported a similar loss of test sensitivity when contrasting primary analyses with those of AUCs. Overall, delay discounting tasks showed steeper discounting of the smaller monetary reward compared with a larger monetary reward, retirement income, and medical treatment. Steeper discounting of smaller monetary
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rewards compared with larger ones is one of the more robust findings in the literature known as the magnitude effect (Thaler, 1981). The present data showed a similar trend, but this difference did not reach statistical significance, which could be partly due to the fact that monetary rewards on the DD task were presented in terms of money owed rather than pure monetary gains (e.g. money received/won). Although a statistically significant magnitude effect on the same measures has previously been observed by our group (e.g. Weatherly and Derenne, 2013), framing of the monetary reward may matter, as a number of studies showed much more reliable magnitude effects with conventionally framed delayed and probabilistic monetary gains compared, for example, with monetary losses (Thaler, 1981; Benzion et al., 1989; Estle et al., 2006). Study limitations
This study does have a number of limitations. First, in addition to the aforementioned lack of control for smoking abstinence, smoking status itself was selfreported but not verified with measurements of expired carbon monoxide. Second, there were only 17 smokers who reported smoking over 10 cigarettes/day, which increased the 95% confidence intervals for this group on measures of discounting, perhaps rendering important group differences statistically nonsignificant. In conjunction with this issue of low statistical power, a low number of heavy smokers prevented us from excluding individuals with potentially random patterns of responding. Third, to avoid some of the issues associated with binary-choice procedures (e.g. learned patterns of responses), we limited the number of commodities for each discounting procedure to 4, with probability and DD questions containing a different set of commodities. The tradeoff, however, was our inability to directly compare the results between the two methods. Conclusion
Overall, the results of the present study showed that the use of the MC methodology in combination with probability discounting may be a sensitive measure of smoking status, even in ad lib smokers with low nicotine dependence. On these tasks, heavy smokers displayed greater risk-seeking characterized by shallower probabilistic discounting (they disregarded increasing uncertainty of the odds to win a larger monetary reward), which supports the theoretical model of addiction. More research, however, is needed to further understand the contextual utility of probability discounting. For example, future studies may compare delay and probability discounting of monetary as well as cigarette gains and losses in nicotine-deprived vs. ad lib heavy smokers, using both binary and MC procedures. Administration of probability discounting questions for various commodities based on the MC procedure could also be explored further in drug-dependent and clinical populations,
including individuals with behavioral addictions such as problem gambling.
Acknowledgements Conflicts of interest
There are no conflicts of interest.
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Delay and probability discounting of multiple commodities in smokers and never-smokers using multiple-choice tasks Dmitri V. Poltavski and Jeffrey N. Weatherly The purpose of the present study was to investigate temporal and probabilistic discounting in smokers and never-smokers, across a number of commodities, using a multiple-choice method. One hundred and eighty-two undergraduate university students, of whom 90 had never smoked, 73 were self-reported light smokers (< 10 cigarettes/day), and 17 were heavy smokers (10 + cigarettes/day), completed computerized batteries of delay and probability discounting questions pertaining to a total of eight commodities and administered in a multiple-choice format. In addition to cigarettes, monetary rewards, and health outcomes, the tasks included novel commodities such as ideal dating partner and retirement income. The results showed that heavy smokers probability discounted commodities at a significantly shallower rate than never-smokers, suggesting greater risk-taking. No effect of smoking status was observed for delay discounting questions. The only commodity that was probability discounted significantly less than others was
‘finding an ideal dating partner’. The results suggest that probability discounting tasks using the multiple-choice format can discriminate between non-abstaining smokers and never-smokers and could be further explored in the context of behavioral and drug addictions. Behavioural c 2013 Wolters Kluwer Health | Pharmacology 24:659–667 Lippincott Williams & Wilkins.
Introduction
Studies using DD tasks have demonstrated that cigarette smokers discount the value of delayed monetary rewards more than their nonsmoking counterparts (e.g. Bickel and Marsch, 2001; Baker et al., 2003; Reynolds, 2006). Furthermore, several investigators reported that smokers showed more pronounced DD of cigarettes compared with monetary reinforcers and health consequences (Bickel et al., 1999; Mitchell, 2004a; Estle et al., 2007; Odum and Baumann, 2007), which suggests that delayed drugs lose their subjective value to a greater extent than other stimuli.
A growing number of studies using behavioral, neurobiological, and imaging techniques have established a strong link between impulsivity and drug abuse, including cigarette smoking (Perry and Carroll, 2008). In reference to drug addiction, impulsivity has been defined as preference for immediate over delayed gratification without forethought or consideration of outcomes including health (Mitchell, 2004a). In other words, drug abuse may be explained by discounting of the value of the delayed benefits of abstinence in favor of the immediate drug effects. One way to study this aspect of impulsivity in humans and animals is by using delay discounting (DD) procedures, during which a subject is typically asked to choose between a small reinforcer delivered immediately and a larger reinforcer delivered after a delay (Smith and Hantula, 2008). With human participants the typical procedure for measuring DD is to present them with a hypothetical binary choice (e.g. Would you prefer $95 now or $100 in 1 year?). The reinforcers are typically monetary, although hypothetical health outcomes (e.g. the onset of a serious drug-related illness), food, and drug use (e.g. cigarettes) have also been used (e.g. Estle et al., 2007; Odum and Baumann, 2007).
All supplementary digital content is available directly from the corresponding author. c 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins 0955-8810
Behavioural Pharmacology 2013, 24:659–667 Keywords: delay discounting, human, multiple-choice format, nicotine addiction, probability discounting, smokers Department of Psychology, University of North Dakota, Grand Forks, North Dakota, USA Correspondence to Dmitri V. Poltavski, PhD, Department of Psychology, University of North Dakota, 319 Harvard St. Stop #8380, Grand Forks, ND 58202, USA E-mail: [email protected] Received 19 September 2012 Accepted as revised 24 September 2013
Some studies, however, have not found differences in DD for cigarettes compared with monetary rewards (Field et al., 2006). These inconsistent findings may be related to the lack of control over nicotine dependence in smokers, as greater discounting of monetary gains has been correlated with the number of daily cigarettes (Ohmura et al., 2005) and the level of nicotine dependence (Heyman and Gibb, 2006). Smoking abstinence has also been shown to produce increases in cigarette discounting and discounting of monetary rewards compared with conditions of ad lib cigarette smoking (smoking at a habitual rate; Mitchell, 2004b; Field et al., 2006). While temporal discounting in drug-dependent and nondependent populations has been studied extensively, less is known about probability discounting in special populations (e.g. smokers, gamblers, etc). Probability discounting refers to the reduction of the subjective value of a probabilistic outcome as the probability of the outcome DOI: 10.1097/FBP.0000000000000010
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660 Behavioural Pharmacology 2013, Vol 24 No 8
decreases. In typical human probability discounting studies, participants are asked to choose between a large, probabilistic outcome and a smaller, certain outcome. Conceptually, this type of discounting is similar to temporal discounting (the reduction of the present, subjective value of a delayed outcome as a function of the delay), and a number of researchers have speculated that temporal and probability discounting are related (Stevenson, 1986; Prelec and Loewenstein, 1991; Rachlin et al., 1991; Green and Myerson, 1996). Specifically, studies have shown that, similarly to temporal discounting, probability discounting can be described by a hyperbolic curve Shead and Hodgins (2009). Richards et al. (1999) and Myerson et al. (2003) also reported a correlation between rates of temporal and probability discounting, but Myerson et al. (2003) suggested the possible presence of different underlying mechanisms behind probability and DD. Similarly, when probability discounting tasks are used with smokers, the support for the common process hypothesis has so far been mixed. Some reports indicated that temporal discounting differences observed between smokers and nonsmokers were also found with probability discounting (Mitchell, 2004a and 2004b; Reynolds et al., 2004; Yi et al., 2007), while others (Mitchell, 1999; Ohmura et al., 2005) did not find any differences between smokers and nonsmokers in probability discounting. In addition, among smokers, Yi and Landes (2012) recently reported a divergence between temporal and probability discounting as a function of smoking abstinence. In their study, greater DD of monetary gains and losses was observed after smoking abstinence compared with ad lib smoking, with no changes found in probability discounting of monetary gains, losses, and cigarettes. The researchers suggested that smokers’ decision making regarding probabilistic outcomes is not influenced by smoking abstinence, withdrawal, or craving. They further contended that, in contrast to theoretical modeling, probability discounting is not a measure of real-life risktaking or risk preference, as no studies so far have shown less discounting in risk-prone populations (Holt et al., 2003; Reynolds et al., 2004; Yi et al., 2007; Madden et al., 2009), often finding the opposite. In theory, risk-taking should be associated with less discounting of probabilistic gains, as individuals are taking greater risks to obtain them in the face of an increasing possibility of getting nothing. Another issue in investigations using temporal and probability discounting procedures is the choice of commodities. To date, few studies have reported inclusion of nondrug specific, nonmonetary commodities such as health consequences (e.g. Johnson et al., 2007), healthy behaviors (e.g. Bradford, 2010), and food (e.g. Odum and Baumann, 2007). Some researchers have suggested that cigarettes and monetary gains seem to fall into one domain of discounting while other nonconsumable commodities (i.e. ideal dating partner, medical treatment,
and retirement income) fall into another (Weatherly et al., 2010; Weatherly and Derenne, 2011). If commodities such as drugs fall within the same domain as those of monetary outcomes, then this result would potentially explain why some researchers have found differences in discounting between substance abusers and nonabusers for hypothetical monetary outcomes. Theoretically, an argument can be made that if one were able to alter how individuals discount one commodity within that domain, then one might also alter how those individuals discount other commodities within that domain, which may have therapeutic implications for cigarette smokers. When evaluating delay and probability discounting in normal and drug-dependent populations, procedural nuances may also play a role. Traditionally, DD in smokers has been assessed using binary-choice tasks that involve a series of evaluations of temporal trade-offs between two fixed reward amounts, one of which is available immediately while the other is delayed by some time period (e.g. Bickel et al., 1999; Bickel and Marsch, 2001; Mitchell, 2004b; Reynolds, 2006; Estle et al., 2007; Odum and Baumann, 2007). This methodology has been criticized for the presentation of the same reward sequences over a potentially extensive number of delay periods, which may result in a learned pattern of responding on the part of the participant, rather than reflecting the degree of his/her impulsivity (Smith and Hantula, 2008). The binary procedure also requires that participants make a large number of choices, which is time consuming and arguably leads to a reduction in ecological validity (i.e. organisms normally have a single choice to make; Weatherly and Derenne, 2011). In addition, in probability discounting studies comparing smokers and nonsmokers, group differences may not be apparent due to the ‘floor effect’ resulting from greater inclusion of low probabilities (Yi et al., 2007). Other alternatives to the presentation of multiple binary choices have been suggested, including the fill-in-the blank (FITB) method (Chapman, 1996) and the multiplechoice (MC) method (e.g. Beck and Triplett, 2009). The FITB procedure asks participants to indicate themselves the smallest value of some larger-later reward with which he/she would be content if it was available immediately. However, because the participant has to generate an answer, rather than choose between presented options, the FITB procedure could theoretically be a more cognitively challenging task than the binary-choice procedure (Smith and Hantula, 2008). With the MC method, on the other hand, the participant identifies the smallest subjective value from a limited list of choices. Weatherly and Derenne (2011) directly compared delay discounting rates across five commodities (finding one’s ideal dating partner, getting 100 packs of free cigarettes, winning the sum of $100 000, being owed the sum of
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$100 000, and obtaining one’s ideal body image through diet and exercise) using both the FITB and the MC method. They found that the FITB method was more sensitive to differences between outcomes than the MC method. In contrast, another interpretation was that the MC method produced more systematically similar rates of discounting across outcomes than did the FITB method. In their follow-up study, Weatherly and Derenne (2012) used seven different commodities (i.e. finding one’s ideal dating partner, winning the sum of $100 000, obtaining one’s ideal body image through diet and exercise, winning $1000, treating a serious medical condition, saving money for retirement, and passing educational reform legislation) to test whether differences in probability discounting would be observed for the FITB and MC methods of data collection. The results demonstrated that the FITB method produced steeper rates of discounting than the MC method, a result that was also found for DD. The researchers also reported good test–retest reliability for probability discounting, although to a potentially lesser extent than for rates of DD. On the basis of the above findings one could speculate that discounting measured by the FITB method maximizes the individual’s trait characteristics related to decision making whereas the binary-choice and MC methods maximize procedural control by the researcher (i.e. state characteristics). Finally, past discounting research has also suggested that the valence of the outcome (i.e. whether it is a gain or loss) is influential in what rate of discounting is observed (e.g. Hardisty and Weber, 2009). In their most recent study using both monetary gains ($1000 vs. 100 000) and equivalent monetary losses ($1000 vs. 100 000) presented using the MC method, Weatherly and Derenne (2013) reported significant magnitude effects (rate of discounting varies inversely with the magnitude of the outcome) for delayed discounting of gains and losses but not for probability discounting. Probabilistic gains, however, were discounted more steeply than probabilistic losses, suggesting that participants are more prone to risk with losses than with gains, more averse to risk with gains than with losses, or both. The purpose of the present study was to explore delay and probability discounting, across the range of commodities previously used in the studies by Weatherly and Derenne (2011, 2012), as a function of smoking status. We hypothesized that cigarette smokers would discount the value of monetary rewards more than their nonsmoking counterparts in both delay and probability discounting conditions using the MC method. We also hypothesized that they would discount cigarettes to a greater extent than monetary rewards.
Methods Participants
The participants were 182 undergraduate students from the University of North Dakota (151 women and 45 men),
with a mean age of 20 years. The majority of the participants were White (87.8%), single (57.7%) and had an annual income of less than $10 000 (85.2%). As part of voluntary extra credit opportunities available in conjunction with some of the participants’ undergraduate psychology classes, the students completed a packet of online instruments that included their demographic information, smoking status, the Minnesota Nicotine Withdrawal Scale (MNWS), the Tobacco Cravings Questionnaire (TCQ), and the Fagerstrom Test for Nicotine Dependence (FTND), as well as MC instruments listing eight commodities (four commodities per question type). There were 92 self-identified never-smokers (have never tried cigarettes, even one or two puffs) and 90 selfidentified smokers, 24 of whom reported smoking every day (26.9%). In addition, 11.1% (n = 10) of the selfidentified smokers also reported daily use of chewing tobacco. Nineteen percent of the smokers (n = 17) reported smoking more than 10 cigarettes/day.
Materials and procedure
Participants completed the materials online by the SONA Systems Ltd (Version 2.72; Tallinn, Estonia) experiment management system. The system ensured that participants who completed the study as a member of one particular course were not eligible to participate again if they were enrolled in another psychology course. Before proceeding to the questionnaires, each participant filled out an informed consent form as approved by the Institutional Review Board at the University of North Dakota and a demographic form that asked participants about the information reported in Table 1. The questionnaire battery consisted of the FTND, TCQ, MNWS, and an MC discounting questionnaire. The FTND (Heatherton et al., 1991) was used in the present study to assess smokers’ level of nicotine dependence. It is a six-item questionnaire that primarily emphasizes physical nicotine tolerance and is widely used in combination with other measures of nicotine dependence (Shiffman et al., 2005). The TCQ has been shown to be a reliable and valid tool in assessment of tobacco cravings (Heishman et al., 2003). The questionnaire comprises 17 items grouped around four separate factors that best describe tobacco cravings: (a) emotionality, or smoking in anticipation of relief from withdrawal symptoms or negative mood, (b) expectancy, or anticipation of positive outcomes from smoking, (c) compulsivity, or lack of control over tobacco use, and (d) purposefulness, or intention and planning to smoke for positive outcomes. Nicotine withdrawal was assessed using the MNWS (Hughes and Hatsukami, 1986), which is an eight-item scale listing only one item for each of the following eight withdrawal symptoms: irritability/anger, anxiety/tension,
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Table 1 Means and SDs for measures of nicotine dependence (Fagerstrom Test of Nicotine Dependence), withdrawal (Minnesota Nicotine Withdrawal Scale), and cigarette cravings (Tobacco Cravings Questionnaire) by smoking level (light vs. heavy) Variables
Light smoker (N = 73)
FTND total score Mean SD TCQ total score Mean SD TCQ emotionality scale Mean SD TCQ expectancy scale Mean SD TCQ purposefulness scale Mean SD TCQ compulsivity Mean SD MNWS total score Mean SD
1.35* 1.61
Heavy smoker (N = 17) 3.90 2.11
68.01 18.03
67.57 13.18
19.73 6.58
18.80 8.27
15.75 3.71
15.45 3.85
17.56 4.50
18.73 5.39
17.56 7.55
16.90 6.24
13.02 6.90
11.80 8.10
FTND, Fagerstrom Test of Nicotine Dependence; MNWS, Minnesota Nicotine Withdrawal Scale; TCQ, Tobacco Cravings Questionnaire. *P < 0.05.
difficulty concentrating, restlessness, increased appetite, depressed or sad mood, craving, and impatience. The scale ranges from 0 to 4 with 0 = ‘not present’ and 4 = ‘severe’. The summary score of the eight items minus the craving item score has been recommended to measure the participants’ overall withdrawal symptomatology (Hughes and Hatsukami, 1998). The MC discounting measure consisted of questions relating to probability discounting of four commodities and DD of four commodities. Probability discounting questions comprised a series of hypothetical questions about $1000 won, $100 000 won, ideal dating partner, and cigarettes. DD items were represented by questions concerning $1000 students were owed, $100 000 they were owed, retirement income, and effectiveness of medical treatment. Both types of questions were identical to those used in Weatherly and Derenne (2011, 2012); the exact wording for each question can be found in the appendix. Five delays were tested for each outcome (6 months, 1, 3, 5, and 10 years) of the delay discounting task. Thus, the participant answered five questions pertaining to each outcome. Each participant answered all five questions about a particular outcome before being presented with questions about another outcome. For each delay discounting question, the participant was asked to choose, from a finite number of choices (n = 51), the smallest number of the outcome she/he would accept rather than waiting a certain period of time for the full number. On the probability discounting task, participants were asked to select (from 51 possible choices) which dollar
amount or percentage they would be willing to accept rather than having a probabilistic chance for the full outcome. Five questions were asked about each outcome, with the probability of receiving the full outcome varying across questions. The five probabilities employed were 1, 10, 50, 90, and 99%. For both types of discounting questions, probabilities and delay intervals were randomly ordered. All participants then completed the questions for a particular set of commodities in the same random order. Data analysis
Using nonlinear regression, responses to the DD questions were fit to the following hyperbolic equation (e.g. Mazur, 1987): V ¼ A=ð1þkDÞ; ð1Þ where V represents the subjective monetary value of the delayed outcome, A represents the amount of the commodity, D represents the delay, and k is a free parameter. The value of k describes the rate at which discounting occurs, with higher values of k indicating steeper rates of (i.e. more) discounting. Similarly, on the probability discounting task the dollar amounts/percentages across the different probabilities were fitted to a hyperbolic function (Rachlin et al., 1991): V ¼ A=ð1þhYÞ; ð2Þ where V stands for the subjective value of the amount, A stands for the amount of the probabilistic outcome, Y stands for the odds against receiving the outcome [i.e. (1 – p)/p], and h is the free parameter that represents the rate of probability discounting and thus serves as the dependent measure of discounting. In Eq. (2), high values of h represent steep rates of discounting when the outcome is uncertain. Low values represent little discounting. Using probability plots available in SPSS 19.0 (IBM Corp. Released 2010. IBM SPSS Statistics for Windows, Version 19.0. Armonk, NY: IBM Corp.) distributions of k and h for all commodities showed deviations from normality. Deviations from the normality assumption warranted logarithmic transformations using the natural log (ln). For comparisons with discounting rates observed in other studies, descriptive statistics for untransformed k and h values, which also reflect differences in variability between smokers and nonsmokers, are presented in Table 3. R2 values were calculated for each participant to measure goodness of fit of the hyperbolic function for each discounting commodity. As can be seen in Table 2, mean R2 values varied from 0.51 (DD: $1000 owed) to 0.70 (probability discounting: $1000 won), while median values ranged from 0.51 (probability discounting: perfect mate) to 0.92 (probability discounting: $100 000 won).
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Discounting in smokers Poltavski and Weatherly 663
2 Table 2 Mean and median R values and 95% confidence intervals for R2 means, for hyperbolic curve fits across commodities
Question type Probability
Delay
Commodity
Mean
95% CI
Median
Ideal dating partner Cigarettes $100 000 won $1000 won $100 000 owed $1000 owed Retirement income Medical treatment
0.53 0.64 0.64 0.66 0.55 0.53 0.56 0.55
0.46–0.59 0.57–0.70 0.58–0.71 0.60–0.72 0.49–0.61 0.47–059 0.49–0.62 0.49–0.61
0.51 0.85 0.92 0.92 0.66 0.57 0.68 0.65
CI, confidence interval.
Means with 95% confidence intervals and medians with interquartile rangesa for ln-transformed k and h values for neversmokers (n = 92), light smokers (n = 74), and heavy smokers (n = 16) Table 3
Question type Probability
Delay
Variables
Mean
95% CI
Median
IQR
– 3.92 to – 2.5 – 4.65 to – 3.25 – 6.35 to – 1.33
– 3.08 – 4.93 – 5.63
5.04 4.09 6.51
– 1.69 to – 0.09 – 2.46 to – 0.44 – 4.82 to – 0.08
– 0.15 – 0.76 – 3.05
4.37 5.08 6.11
– 2.42 to – 1.00 – 2.58 to – 1.01 – 5.47 to – 1.48
– 0.54 – 0.76 – 4.69
4.56 4.98 5.98
– 1.96 to – 0.40 – 2.80 to – 1.22 – 4.34 to 0.07
– 0.31 – 1.24 – 2.43
3.94 4.56 5.18
– 5.52 to – 4.82 – 5.77 to – 5.06 – 5.68 to – 4.13
– 5.38 – 5.67 – 4.75
1.90 1.41 1.69
– 5.48 to – 4.56 – 5.31 to – 4.40 – 6.30 to – 3.48
– 5.14 – 5.12 – 5.36
1.88 1.53 2.63
– 5.32 to – 4.52 – 5.10 to – 4.20 – 5.62 to – 3.57
– 5.06 – 4.90 – 4.44
1.82 1.96 2.68
– 5.43 to – 4.73 – 5.57 to – 4.91 – 6.03 to – 4.47
– 5.09 – 5.18 – 5.44
1.64 1.65 1.04
ln h Ideal dating partner Never-smoker – 3.21 Light smoker – 3.95 Heavy Smoker – 4.17 Cigarettes Never-smoker – 0.89 Light smoker – 1.45 Heavy Smoker – 2.45 $100 000 won Never-smoker – 1.71 Light smoker – 1.79 Heavy smoker – 3.47 $1000 won Never-smoker – 1.18 Light smoker – 2.01 Heavy smoker – 2.14 ln k Retirement income Never-smoker – 5.17 Light smoke – 5.42 Heavy smoker – 4.90 $100 000 owed Nonsmoker – 5.02 Light smoker – 4.85 Heavy Smoker – 4.89 $1000 owed Never-smoker – 4.92 Light smoker – 4.65 Heavy Smoker – 4.60 Medical treatment Never-smoker – 5.08 Light smoke – 5.24 Heavy Smoker – 5.25
CI, confidence interval; IQR, interquartile range. a Difference between the 75th and 25th percentiles.
These values indicated that the hyperbolic function fit the data better for some commodities than for others. For this reason the area under the curve (AUC) (Myerson et al., 2001) was also calculated for each outcome type for each participant. The AUC is an atheoretical measure that is normally distributed and has been used as a measure of temporal and probability discounting for money, drugs, food, and other consumable outcomes (e.g. Odum and Rainaud, 2003; Field et al., 2006, 2007; Odum et al., 2006; Estle et al., 2007). The AUC is applicable to various issues in the study
of discounting, including the effects of multiple types of reward (e.g. money vs. cigarettes), different participant populations (e.g. smokers vs. nonsmokers), and variable reward amounts on the rate of discounting (Myerson et al., 2001). To calculate the AUC, the delays, probabilities, and indifference points were first normalized, and then the AUC was computed using the equation: ðx2 x1 Þ ½ðy1 þy2 Þ=2; where x1 and x2 are successive delays, and y1 and y2 are the subjective values associated with these delays/probabilities. This equation produces the AUC by summing the areas of the trapezoids calculated across the different delays/probabilities (see Myerson et al., 2001, for details). The AUC ranges from 1 to 0 (larger values indicate less discounting). Statistical analyses
Data were first separated by question type (delay vs. probability discounting). Within each discounting question type, separate 3 4 ANOVAs were conducted for each data-fitting model (hyperbolic function and AUC). Self-reported smoking status was a between-subjects variable (never-smoker, light smoker, heavy smoker) and delay/probability discounting was a within-subjects variable with four levels (probability discounting questions: ln-transformed h values and AUCs for ideal dating partner, cigarettes, $100 000 won, and $1000 won; DD questions: ln-transformed k values and AUCs for retirement income, $100 000 owed, $1000 owed, and medical treatment). Significant main effects for variables with more than two levels were programmed in SPSS 19.0 (IBM Corp. Released 2010. IBM SPSS Statistics for Windows, Version 19.0. Armonk, NY: IBM Corp.) for an automatic follow-up with the Bonferroni procedure (also known as Dunn–Bonferroni). This post-hoc procedure was chosen on the basis of its greater power and narrower 95% confidence intervals than other methods (e.g. Tukey’s HSD) when the number of comparisons equals or is less than 4 (Myers and Well, 2003).
Results Descriptive statistics
Overall, the sample of 90 smokers had a total score of 1.59 (SD = 1.86) on the FTND, indicating very low dependence (Heatherton et al., 1991). At the same time, heavy smokers had significantly greater nicotine dependence scores (mean = 3.90; SD = 2.11) than light smokers (mean = 1.35; SD = 1.61; t = – 4.42, P < 0.01), placing them in the low-moderate dependence category (Heatherton et al., 1991). There were no statistically significant differences between the two groups of smokers on measures of craving and withdrawal (see Table 1 for details). As a whole, the sample of smokers indicated moderate cigarette cravings [total TCQ score = 67.79 (SD = 17.28) out of maximum 119] and mild withdrawal [average total sum = 12.30 (SD = 6.97) out of maximum 36].
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664 Behavioural Pharmacology 2013, Vol 24 No 8
Statistical analyses Hyperbolic curve
A significant main effect of commodity type was found for DD questions using ln-transformed k-values [F(3, 435) = 3.15, P < 0.025]. Pairwise comparisons using the Bonferroni adjustment showed significant differences between the ln-transformed k mean for $1000 owed (Mln k = – 4.73) and corresponding means for medical treatment (Mln k = – 5.24; P < 0.02) and retirement income (Mln k = – 5.17; P < 0.02). The difference between discounting rates for $1000 owed and $100 000 owed (Mln k = – 5.01) was in the expected direction but did not reach statistical significance (P = 0.06). These results suggest steeper DD for $1000 owed than for medical treatment, retirement income, or $100 000 owed. There was no significant main effect of smoking status and no significant commodity type by smoking status interaction. For probability discounting a significant main effect was found for commodity type [F(3, 474) = 14.72, P < 0.01] and smoking status [F(1, 158) = 2.95, P < 0.05]. Pairwise comparisons with the Bonferroni adjustment revealed that the commodity with a significantly shallower rate of discounting compared with all others was ‘finding an ideal dating partner’ (Mln h = – 3.71, P < 0.01). If we assume, after Shead and Hodgins (2009), that risk-seeking individuals exhibit shallower discounting of gains (they tend to overweight the possibility of a large gain while underweighting the possibility of no gain), then the participants exhibited significantly more risk in choosing the odds of finding a perfect mate than winning different amounts of money or obtaining cigarettes. Using the Bonferroni test to follow up on the significant main effect of smoking status, it was found that heavy smokers exhibited a significantly shallower rate of discounting of probabilistic gains (Mln h = – 2.95, SE = 0.58; P < 0.05) than never-smokers (Mln h = – 1.66, SE = 0.25), suggesting a greater level of risk-taking. The discounting pattern of light smokers fell somewhere in between, but failed to reach statistical significance when compared with either group (Mln h = – 2.20, SE = 0.25). Ln-transformed k and h values for all commodities are presented in Table 3.
Area under the curve
A 4 (commodity type) 3 (smoking status) mixed ANOVA for DD questions, using mean AUC values, did not show significant main effects for either the type of commodity or smoking status. Their interaction was also not significant. For probability discounting questions a significant main effect was found for commodity type [F(3, 537) = 37.41, P < 0.01] and smoking status [F(1, 158) = 2.93; P < 0.05]. Pairwise comparisons with the Bonferroni adjustment revealed that the commodity with a significantly greater mean AUC compared with all others was ‘finding an ideal dating partner’ (MAUC = 0.64, P < 0.01). Figure 1 illus-
trates these findings. A post-hoc Bonferonni test used to break down the significant main effect of smoking status showed that heavy smokers (10 + cigarettes/day) had a significantly higher mean AUC (mean = 0.541, SE = 0.06) than never-smokers (mean = 0.41), suggesting greater risktaking while overestimating the probability of a gain (shallower discounting). These findings are illustrated in Fig. 2.
Discussion One of the most robust findings of the present study, supported by the AUC analyses, concerned a significantly smaller overall rate of probability discounting of an ideal dating partner both by smokers and never-smokers. This finding is in agreement with our previous work evaluating DD across a number of commodities (Weatherly et al., 2010). In that study, participants discounted dating partner the least, compared with money, cigarettes, and ideal body image. It is likely that among young adults (mean age of 20 years in our study), including nondeprived drug-dependent individuals, the incentive salience of a dating partner is quite strong, as most young people may still be searching for their perfect mate. Indeed, in our study close to 58% reported being single (not in a relationship). As a result they may overweigh the possibility of finding their ideal dating partner and underestimate the probability of finding no partner. In addition, probability discounting questions that included cigarettes, monetary rewards, and ideal dating partner showed greater sensitivity to smoking status, with heavy smokers displaying significantly lower rates of probability discounting than never-smokers. The obtained results, however, are novel, in that when probability discounting questions were used in previous studies including monetary gains and losses, smokers tended to probability discount more than nonsmokers (e.g. Reynolds et al., 2004; Yi et al., 2007; Yi and Landes, 2012). At the same time, greater risk-seeking in drugdependent populations has been theoretically suggested to show shallower probabilistic discounting, which corresponds to placing relatively less weight on the uncertainty and more on the amounts involved, regardless of whether the outcomes are gains or losses (Shead and Hodgins, 2009). One of the possible reasons why the pattern of the results in this study is more consistent with the theoretical model of drug dependence and probability discounting than previously shown is that in the present study the MC method was used, compared with the binary-choice procedures used in other studies (Reynolds et al., 2004; Yi et al., 2007; Yi and Landes, 2012). Yi et al. (2007) suggested that procedural nuances could indeed play a role in producing statistically significant between-group (smokers vs. nonsmokers) differences, as they are likely
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Discounting in smokers Poltavski and Weatherly 665
Fig. 1 0.7000
∗ 0.626
Mean AUC
0.6000
0.5000
0.407 0.4000
0.405
0.402
0.3000 $100 000 won
$1000 won
Cigarettes Ideal dating partner
Error bars: 95% Cl
Area under the curve (AUC) means and 95% confidence intervals (CIs) for discounting rates of probability commodities collapsed over smoking status.*Significantly different from discounting rates for other commodities at a = 0.05.
Fig. 2
Mean AUCs across probability commodities
completion of the instruments. As a result, smokers in the present study showed only mild withdrawal on the MNWS. Yi and Landes (2012) recently reported significantly greater DD of monetary gains and losses after 24-h smoking abstinence compared with ad lib smoking. At the same time, the authors suggested that smokers’ decision making regarding probabilistic outcomes is not influenced by smoking abstinence, withdrawal, or craving.
c∗ 0.60 b a 0.40
0.20
0.409
0.472
0.541
0.00 Never smoker
Light smoker Heavy smoker Smoking status Error bars: 95% CI
Means and 95% confidence intervals (CIs) for area under the curve (AUC) across four probability discounting commodities by smoking status.*AUC mean for smoking status marked ‘c’ is significantly different from ‘a’ but not ‘b’ at a = 0.05.
to be affected by magnitudes and probabilities included in the procedure. In contrast to some of the previous reports (e.g. Bickel et al., 1999; Mitchell, 2004a; Odum and Baumann, 2007) and our original hypothesis, cigarette smokers in our study did not differ from nonsmokers in DD of monetary rewards, retirement income, or medical treatment. One of the reasons for the null findings could be the fact that we did not require nicotine deprivation before the
Furthermore, even our heavy smokers had a fairly low score on the FTND (mean = 3.90). This could be problematic, as in the study by Sweitzer et al. (2008) low-dependence smokers were more similar to neversmokers than to high-dependence smokers in their discounting of delayed rewards, implying that the smoking/discounting relationship may be driven by a subgroup of highly dependent smokers. In their study the number of cigarettes smoked per day was not significantly predictive of the smokers’ delayed discounting. In contrast, another explanation of the null findings on the DD task may involve the shape of the distribution of the DD data. Typically, binary measures of temporal discounting in both normal and drug-dependent populations yield data that can be well described with a hyperbolic curve, in which reported mean and median R2 are often greater than 0.80 (e.g. Sweitzer et al., 2008; Shead and Hodgins, 2009; Yi and Landes, 2012). In the present study mean R2 values were between 0.53 and 0.66 for probability discounting questions, and between 0.53 and 0.56 for DD questions (see Table 2 for details).The observed poor hyperbolic model fit may be related to increased variability in the data (random responding), which in the present study may have been more likely in the group of smokers. Specifically, never-smokers showed a significantly greater proportion of variance accounted for (R2 = 0.71, SD = 0.30) than smokers (R2 = 0.61, SD = 0.31) on probability discounting questions (t = 1.98, P = 0.05). The trend was similar on DD questions (R2never-smokers = 0.60 vs. R2smokers = 0.52) but did not reach statistical significance (t = 1.51, P = 0.13). Thus, to verify the obtained results after fitting the data to a hyperbolic curve, we also performed analyses using AUC, which is thought to be an atheoretical measure of discounting capable of accounting for all observed data (Myerson et al., 2001). While in most instances the results of the analyses of the AUCs were congruent with those of transformed k and h parameters, for DD questions, we observed a loss of sensitivity, resulting in nonsignificant findings. This result was not unexpected. Yi and Landes (2012) recently reported a similar loss of test sensitivity when contrasting primary analyses with those of AUCs. Overall, delay discounting tasks showed steeper discounting of the smaller monetary reward compared with a larger monetary reward, retirement income, and medical treatment. Steeper discounting of smaller monetary
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666 Behavioural Pharmacology 2013, Vol 24 No 8
rewards compared with larger ones is one of the more robust findings in the literature known as the magnitude effect (Thaler, 1981). The present data showed a similar trend, but this difference did not reach statistical significance, which could be partly due to the fact that monetary rewards on the DD task were presented in terms of money owed rather than pure monetary gains (e.g. money received/won). Although a statistically significant magnitude effect on the same measures has previously been observed by our group (e.g. Weatherly and Derenne, 2013), framing of the monetary reward may matter, as a number of studies showed much more reliable magnitude effects with conventionally framed delayed and probabilistic monetary gains compared, for example, with monetary losses (Thaler, 1981; Benzion et al., 1989; Estle et al., 2006). Study limitations
This study does have a number of limitations. First, in addition to the aforementioned lack of control for smoking abstinence, smoking status itself was selfreported but not verified with measurements of expired carbon monoxide. Second, there were only 17 smokers who reported smoking over 10 cigarettes/day, which increased the 95% confidence intervals for this group on measures of discounting, perhaps rendering important group differences statistically nonsignificant. In conjunction with this issue of low statistical power, a low number of heavy smokers prevented us from excluding individuals with potentially random patterns of responding. Third, to avoid some of the issues associated with binary-choice procedures (e.g. learned patterns of responses), we limited the number of commodities for each discounting procedure to 4, with probability and DD questions containing a different set of commodities. The tradeoff, however, was our inability to directly compare the results between the two methods. Conclusion
Overall, the results of the present study showed that the use of the MC methodology in combination with probability discounting may be a sensitive measure of smoking status, even in ad lib smokers with low nicotine dependence. On these tasks, heavy smokers displayed greater risk-seeking characterized by shallower probabilistic discounting (they disregarded increasing uncertainty of the odds to win a larger monetary reward), which supports the theoretical model of addiction. More research, however, is needed to further understand the contextual utility of probability discounting. For example, future studies may compare delay and probability discounting of monetary as well as cigarette gains and losses in nicotine-deprived vs. ad lib heavy smokers, using both binary and MC procedures. Administration of probability discounting questions for various commodities based on the MC procedure could also be explored further in drug-dependent and clinical populations,
including individuals with behavioral addictions such as problem gambling.
Acknowledgements Conflicts of interest
There are no conflicts of interest.
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