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Published in final edited form as: Exp Clin Psychopharmacol. 2025 May 12;33(6):594–599. doi: 10.1037/pha0000780 Search in PMC Search in PubMed View in NLM Catalog Add to search Rate of cross-commodity discounting of substances varies by substance type for individuals in recovery from substance use disorders Anthony N Nist Anthony N Nist , Ph.D. 1 Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, Virginia, US Find articles by Anthony N Nist 1 , Daniel AR Cabral Daniel AR Cabral , Ph.D. 1 Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, Virginia, US Find articles by Daniel AR Cabral 1 , Shuangshuang Xu Shuangshuang Xu , Ph.D. 1 Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, Virginia, US Find articles by Shuangshuang Xu 1 , Allison N Tegge Allison N Tegge , Ph.D. 1 Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, Virginia, US Find articles by Allison N Tegge 1 , Warren K Bickel Warren K Bickel , Ph.D. 1 Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, Virginia, US Find articles by Warren K Bickel 1, * Author information Article notes Copyright and License information 1 Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, Virginia, US * In memoriam ✉ Corresponding Author: [email protected] , Contact Information: Fralin Biomedical Research Institute at VTC, 2 Riverside Circle, Roanoke, VA, 24016, Fax: 540-985-3361 Issue date 2025 Dec. PMC Copyright notice PMCID: PMC13071926 NIHMSID: NIHMS2151481 PMID: 40354268 The publisher's version of this article is available at Exp Clin Psychopharmacol Abstract The extant literature hints at the existence of substance-specific differences in rates of cross-commodity discounting. However, direct examinations are currently lacking. The present experiment aimed to replicate previous studies examining cross-commodity discounting of substances and to extend their findings by examining potential substance-specific relationships with discounting. Participants ( n = 122) on recovery pathways from substance use disorders indicated the substances they were still actively using, and then ranked these substances from most to least preferred. Participants then completed four discounting tasks: 1) money now - money later, 2) money now - drug later, 3) drug now - drug later, and 4) drug now - money later. Monetary and drug amounts were always equated. In these tasks, the drug commodity was always the participant’s most preferred except if participants indicated they used multiple substances, in which case they completed additional discounting tasks with their second most preferred substance. Results revealed that discounting rates across substances did not differ significantly in conditions where the same commodity was both the immediate and the delayed option. In contrast, in the drug now - money later condition, we found that rates of discounting varied significantly according to the specific drug commodity. Further, this relationship was inverted in the money now - drug later condition. Overall, results from previous examinations of the cross-commodity discounting of alcohol and stimulants were replicated. In addition, we provide the first direct evidence that rates of cross-commodity discounting may differ across different substances. Keywords: cross-commodity discounting, SUD recovery, behavioral economics Introduction Delay discounting (i.e., DD; the devaluation of rewards as a function of the delay to their receipt) is most often studied using single-commodity tasks with monetary rewards (e.g., a choice between a small amount of money now vs. a larger amount later; Pritschmann et al., 2021 ). However, consumable commodities such as drugs are discounted significantly more than money – even when the specific drug amount has an equivalent monetary value ( Bickel et al., 2011 ; Moody et al., 2017 ; Odum, 2011b ; Odum et al., 2020 ). In addition, single-commodity tasks where drugs are used as rewards may be limited because many individuals with substance use disorders (SUDs) intend to quit or have goals relating to future abstinence ( Babb et al., 2017 ; Bujarski et al., 2013 ; Heather et al., 2010 ). Thus, the availability of drugs in the future may serve as an aversive consequence. In other words, a high rate of discounting in single-commodity tasks with drug commodities could be interpreted as either a preference for the immediately available drug or an aversion to the future availability of the drug ( Pritschmann et al., 2021 ). Given this, cross-commodity discounting tasks, in which participants are asked to indicate preference for either an immediately available amount of one commodity or a delayed amount of a different commodity (e.g., drug now vs. money later), appear to be better suited for providing a more nuanced understanding of the association between DD and substance use ( Green & Myerson, 2019 ; Locey et al., 2023 ; Pritschmann et al., 2021 ). Generally, in such cross-commodity paradigms with drug commodities, participants complete discounting tasks in four different conditions: money now vs. money later (M-M), drug now vs. drug later (D-D), drug now vs. money later (D-M), and money now vs. drug later (M-D). In addition, as is typical in single-commodity tasks, the immediate amount of drug or money is smaller than that of the delayed option (e.g., Odum, 2011a ). To determine the specific value of drug commodities used in these tasks, participants are asked to indicate what quantity of a specific drug would be equally preferable to receiving some amount of money. Then, where applicable, this amount is used as the larger later (LL) option, and one half of this amount is used as the smaller sooner option (SS). Using this paradigm, to date several consistent findings have emerged in the literature: 1) discounting rates vary as a function of discounting condition (e.g., M-D vs. D-M), 2) discounting rates are highest in the M-D condition, 3) discounting rates in the D-D condition are higher than in the M-M condition, and 4) discounting rates are always higher when drug is the LL commodity, regardless of the SS commodity. Such findings have been demonstrated in individuals who use cocaine, whether they are treatment-seeking ( Bickel et al., 2011 ) or not ( Wesley et al., 2014 ), as well as in individuals who use alcohol ( Moody et al., 2017 ; Naudé et al., 2021 , Taylor et al., 2023 ) and cannabis ( Naudé et al., 2021 ). Despite that, overall, significant continuity in findings has been shown across these studies, noteworthy differences have emerged, too. For example, Bickel et al. (2011) found that discounting rates varied by condition and, from the lowest to the highest DD rates, were: M-M, D-M, D-D, and M-D. In other words, when money was the LL option, regardless of the SS commodity, DD rates were lowest. Conversely, participants were most likely to wait for the delayed option when both commodities were money, but were least likely to do so when money was the SS and cocaine was the LL. Further, when cocaine was the LL, regardless of the SS commodity, DD rates were highest. Moody et al. (2017) and Taylor et al. (2023) replicated these results with individuals who use alcohol with the exception that DD rates were found to be lower in the D-M condition than in the M-M condition. In other words, when an immediate amount of alcohol was the SS commodity, participants were more likely to wait for delayed money than when an equivalent amount of money was the SS commodity. This difference, although subtle, is an indication that DD rates in cross-commodity tasks may differ depending on the substance in question. Additional tentative evidence for this notion can be seen in the results reported by Naudé et al. (2021 , Fig. 1 ) who collected cross-commodity DD data where both alcohol and cannabis were arranged as drug commodities. Although a slight difference between alcohol and cannabis was observed when they were arranged as the LL option in the M-D condition, the authors did not quantify this relationship statistically. Naudé et al. (2021) also replicated the findings of Moody et al. (2017) and Taylor et al. (2023) in that DD rates in the D-M condition were lower than those in the M-M condition for individuals who use alcohol. Figure 1. Open in a new tab Observed mean ln(k) for each discounting condition, grouped by substance. M-M is money now vs. money later, D-M is drug now vs. money later, D-D is drug now vs. drug later, and M-D is money now vs. drug later. Error bars represent one standard error of the mean. Thus, the present study sought to replicate the findings of previous studies using cross-commodity tasks with substances (e.g., Bickel et al., 2011 ; Moody et al., 2017 ; Naudé et al., 2021 ; Taylor et al., 2023 ; Wesley et al., 2014 ), and to expand upon the CCT literature by adding direct comparisons between substances. Such replication and extension could prove to be important, as any substance-specific differences in discounting that are uncovered could point to unique differences in understanding SUD recovery. As such, here we report discounting rates in cross-commodity tasks for individuals in recovery from SUDs. These individuals identified themselves as currently using their primary substance, which included alcohol, cannabis, opioids, or stimulants. Methods Participants Data were collected from the International Quit and Recovery Registry (IQRR; https://www.quitandrecovery.org/ ). The IQRR is an online community and registry composed of individuals who meet DSM-5 lifetime criteria for at least one SUD ( Hasin et al., 2013 ) and self-report being in recovery from at least one of the SUDs for which they met lifetime criteria. Here, we define in recovery as starting any active change in substance use behavior and taking steps to cut down or stop using, whether or not they were successful ( Athamneh et al., 2022 ; Cabral et al., 2024 ; Dwyer et al., 2023 ). Thus, participants in recovery may still be actively using substances. The present data were collected online in January 2024. Only participants who were 1) >= 18 years old, 2) in recovery from at least one SUD, and 3) indicated actively using substances within the past 30 days were eligible to participate. Participants who did not indicate actively using alcohol (liquor, beer, wine), cannabis, opioids (heroin or prescription pain relievers), cocaine, or stimulants (methamphetamine or prescription amphetamine pills) in the past 30 days were excluded. Participation was voluntary, and consent was inferred from the completion and submission of the survey questionnaires. Ethical approval for this study was granted by the Institutional Review Board at Virginia Polytechnic and State University. Demographics Collected demographics are age, sex, race, ethnicity, education, and income. Substance Preferences This study examined the following five drug commodities: alcohol, cannabis, opioids, cocaine, and stimulants. These five substances were used because they are the most prevalent in the IQRR ( Cabral et al., 2024 ). Participants were iteratively asked, “Of the substances that you have used within the past 30 days, please indicate which is your most preferred”, to establish a ranking of substances they used in the past 30 days based on their preferences. The top two preferred substances were considered in the analysis and henceforth referred to as primary and secondary . If a participant has only used one substance in the past 30 days, then that participant only has a primary substance. Substance Valuation Participants were asked for their primary and secondary substances to indicate what quantity of that substance would be equal to receiving $1000. To normalize across participants for a given substance, these quantities were converted to single serving units. For example, individuals who preferred alcohol were asked about it in terms of single servings of beer, wine or liquor. Individuals who preferred other substances were asked about their substance in terms of either grams (i.e., grams of cannabis, cocaine, heroin, or methamphetamine) or a specific quantity of pills (i.e., a number of painkillers or prescription amphetamines). Delay discounting tasks Participants completed a series of 5-trial adjusting delay tasks (Koffarnus & Bickel, 2014) including M-M, D-D, D-M, and M-D discounting conditions for each of their preferred substances. These four conditions were presented for each substance in a block design with the primary and secondary substance order being counterbalanced and the order of conditions within the block also being randomized. For each substance, the previously specified quantity of their preferred substance served as the “D” commodity. Statistical analysis In a data cleaning step, we considered participants with complete data on all of the required assessments, which included demographics and DD tasks; thus, a total of 122 participants were included in our analytical sample. Because of the small sample size for cocaine ( n =13), participants who indicated cocaine as their primary or secondary substance were merged with stimulants. We summarized the demographics using means (standard deviations) and frequencies (percentages) where appropriate. To test the hypothesis that there are substance-specific differences in DD rates, we used linear mixed effects models, where discounting condition (parameterized as a categorical variable), substance (parameterized as a categorical variable), substance preference (parameterized as a binary variable), and the pairwise interaction terms between discounting condition, substance, and substance preference are independent fixed effects variables, and ln(k) is the dependent variable. We include a random effect for the participant to account for the repeated measures. We performed model selection to identify the set of predictors in the optimal model, defined as the model with the lowest Bayesian information criterion. Model results were reported using type III Wald F-tests with Kenward-Roger degrees of freedom corrections ( Kenward & Roger, 1997 ) for all effects in the multivariate regression models. Finally, pairwise contrasts were performed to indicate the simple effects between levels using the emmeans package ( Lenth, 2024 ), and multiple testing corrections were implemented using the method described by ( Benjamini & Hochberg, 1995 ). All analyses were performed using R version 4.4.1 ( R Core Team, 2024 ), and an α ≤ 0.05 was considered statistically significant. Results Table 1 contains demographic information and sample characteristics. In brief, the current participants had a mean age of 41.1 years, were predominantly female (56.6%), White (76.2%), not Hispanic or Latino (91.2%), and reported current use of multiple substances (97.5%) within the last 30 days. The sample sizes for each individual substance (either primary or secondary) were as follows: alcohol, n = 73; cannabis, n = 79; opioids, n = 29; and stimulants/cocaine, n = 60. Notably, the most common pairs of substances in the data set (see Table S1 ) included alcohol and cannabis (41.0%), stimulants and cannabis (16.4%), and opioids and stimulants (11.5%). Table 1. Sample characteristics and demographics ( N = 122). Sample demographics Frequency(%)/Mean(SD) Age a 41.1 (10.1) Sex-Female b 69 (56.6) Race b American Indian or Alaskan Native 1 (0.8) Asian 1 (0.8) Black or African American 20 (16.4) White 93 (76.2) Multiracial 3 (2.5) Prefer not to answer/other 4 (3.2) Ethnicity - Hispanic or Latino b 10 (8.1) Education b Less than High School 5 (4.1) High School diploma/GED 22 (18.0) Some college/Associate’s degree 54 (44.3) Bachelor’s degree 30 (25.0) Master’s, Professional, or Doctorate degree 11 (9.0) Annual Income b Less than $10,000 15 (12.3) $10,000 to $19,999 17 (14.0) $20,000 to $29,999 17 (14.0) $30,000 to $39,999 15 (12.3) $40,000 to $49,999 8 (6.6) $50,000 to $99,999 32 (26.2) More than $100,000 18 (14.8) Sample characteristics Frequency(%) Primary substance b Alcohol 35 (28.7) Cannabis 43 (35.2) Cocaine 5 (4.1) Opioids 17 (13.9) Stimulants 22 (18.0) Secondary substance b Alcohol 38 (31.1) Cannabis 36 (29.5) Cocaine 8 (6.6) Opioids 12 (9.8) Stimulants 25 (20.5) No secondary substance 3 (2.5) Open in a new tab a Mean (SD) b Frequency (%) To identify the variables important to estimating discounting rate, our model selection identified the optimal set of predictors as discounting condition (i.e., M-M, D-D, D-M, M-D), substance, and the interaction between discounting condition and substance ( Figure 1 ). The multivariate mixed effects linear regression model revealed a significant interaction between the effects of discounting condition and substance on discounting rate ( F (9, 712.38) = 10.33, p < .001, η p 2 = .16); a significant main effect of discounting condition ( F (3, 708.39) = 97.91, p < .001, η p 2 = .29), and a non-significant main effect of substance ( F (3, 778.46) = 0.35, p = .79, η p 2 = .001). Note that substance preference (i.e., primary vs secondary) was not selected in the optimal model, and thus the data does not support differences in discounting rate by primary or secondary substance. Post hoc pairwise comparisons indicated discounting rates in all discounting conditions were significantly different from one another for the alcohol group (all p s ≤ .03; see Table S2 for all pairwise comparisons and effect sizes). For the cannabis and stimulants groups, discounting rates in all conditions were found to be significantly different from one another (all p s ≤ .02) with the exception of M-M vs. D-M (cannabis, p = .07, stimulants, p = .94) and M-M vs. D-D (cannabis, p = .06, stimulants, p = .06). Finally, for the opioids group, DD rates in the M-D condition were significantly greater than in the M-M condition ( p = .03), but no other significant differences were detected (all ps > .06). Comparisons within conditions and across substances revealed non-significant substance differences in the M-M or D-D discounting conditions ( p s > .46). In the D-M condition, all groups were found to be significantly different from one another (all p s ≤ .03) with the exception of the stimulants and opioids groups ( p = .10). Similarly, in the M-D condition, all groups were significantly different from one another (all p s ≤ .02) with the exception of cannabis and stimulants ( p = .13). Importantly, the order of discounting rates by substance was inverted between the M-D and D-M conditions; that is, from lowest to highest discounting rate: alcohol, cannabis, stimulants, opioids for D-M; and opioids, stimulants, cannabis, alcohol for M-D. Discussion The primary aim of the present experiment was to both replicate and extend previous findings in the literature regarding the cross-commodity discounting of substances. Previous studies (e.g., Bickel et al., 2011 ; Moody et al., 2017 ; Naudé et al., 2021 ; Taylor et al., 2023 ) showed that substance-specific differences in rates of cross-commodity discounting when drugs are employed as the SS or LL commodities may exist. Thus, the present study (1) re-examined the cross-commodity discounting of drugs including alcohol, cocaine, and cannabis; (2) added examinations of drugs not yet studied in cross-commodity tasks including stimulants and opioids; and (3) systematically compared cross-commodity tasks with different substances collected in the same experiment, allowing for direct comparisons among substances. In general, we replicated three key findings of cross-commodity tasks established in previous studies ( Bickel et al., 2011 ; Moody et al., 2017 ; Naudé et al., 2021 ; Taylor et al., 2023 ). First, across all substances mean discounting rates were always greatest in the M-D condition. Second, mean discounting rates in the D-D condition for a particular substance were always greater than rates in the M-M condition for that substance. However, importantly, the pairwise comparisons for these conditions in both the cannabis and opioids groups did not reach statistical significance ( p s = .06). Finally, with the exception of the opioids group, mean discounting rates were always greatest when drug was the LL commodity. When the specific drug is considered, the present data also replicated that the discounting rate in the D-M condition was lower than in the M-M condition for individuals who use alcohol, but not for those who use stimulants/cocaine (i.e., cocaine in Bickel et al., 2011 ). Thus, these replications may indicate a number of relatively robust findings in the cross-commodity literature with drug commodities. This is the first study to identify significant substance-specific differences in cross-commodity tasks. We found that the substance-specific discounting rate across commodities was orderly and related to whether drug was the immediate or delayed commodity. For example, when drug was the SS commodity, mean DD rates from lowest to highest were alcohol, cannabis, stimulants/cocaine, then opioids. Conversely, when drug was the LL commodity, mean DD rates from lowest to highest were opioids, stimulants/cocaine, cannabis, then alcohol. These substance-specific differences align with previous work showing that DD rates for participants who use substances, although most often found to be significantly greater than individuals who do not consume these substances (e.g., Bickel, et al., 1999 ; Heil et al., 2006 ; Madden et al., 1997 ; Petry, 2001 ), may not necessarily be equivalent across substances (e.g., Johnson et al., 2010 ; Kirby & Petry, 2004 ). For example, Kirby and Petry (2004) showed that participants who use heroin and cocaine exhibited significantly greater rates of discounting for delayed monetary rewards compared to non-drug using controls, while those who use alcohol did not. Similarly, Johnson et al. (2010) found that individuals who use cannabis showed a trend toward discounting more than non-using controls and suggested that the effect size for cannabis specifically may just be less than that of previously examined substances. However, in the same commodity discounting conditions (i.e., M-M and D-D), no significant differences as a function of substance type were detected. Moody et al. (2017) explained that differences in D-M discounting rates between their findings with participants who use alcohol and those of Bickel et al. (2011) with those who use cocaine may be explained in terms of SUD severity. As such, perhaps those who heavily use alcohol and cannabis may not be struggling as greatly as those who heavily use other substances such as stimulants and opioids, and as a result, do not discount LL rewards as steeply. Although this explanation seems plausible, a limitation of the present data set is that we did not quantify current SUD severity. All participants met lifetime DSM-5 criteria for an SUD, but within each substance, heterogeneity in terms of physical dependence and amount of use may have existed. Additionally, not all substance pairs were appropriately represented from a within-subject perspective. As such, substance-specific differences in discounting could be attributed to differences in the populations that use each substance, rather than due to differences in the substances themselves. Further, the generalizability of the present results may be limited due to the nature of our sample. For example, previous research has shown that DD rates in individuals currently using a substance may differ from those in individuals who formerly used that substance and are now abstinent (e.g., Bickel et al., 1999 ; Heil et al., 2006 ; Petry, 2001 ). Although our sample indicated continued substance use, they also self-identified as being in recovery, which implies at the very least a future goal of reduced substance use. Given that for these individuals the future availability of their drug of choice may serve as an aversive consequence, the present results would likely differ compared to a sample of individuals who are both not in recovery and non-abstinent. Thus, future research in this domain should include individuals at different stages of recovery in addition to quantifications of current SUD severity to better understand the impact that interactions between such variables may have on cross-commodity discounting of substances. Further limitations include that the substance groupings herein were only cross-sectional in nature, the sample was completely online, and all measurements were self-reported. Despite these limitations, this study adds valuable information to the cross-commodity discounting literature and points to several interesting future directions of research. First, a replication of substance-specific differences in a more generalizable sample would be beneficial. Future studies may also be needed to further examine the impact of opioids in this context. For example, the general pattern of discounting across conditions looks similar for all substances with the exception of the opioids group. For the alcohol, cannabis, and stimulants groups, discounting rates were always greatest in conditions in which drug was the LL commodity, whereas for the opioids group there was relatively little change in discounting rate in these conditions compared to the D-M condition. This could be indicative of a further substance-specific difference with respect to opioids. However, as mentioned above, to our knowledge, this is the first examination of opioids within the context of CCTs, and more research is needed to disentangle the complex relationship between money and drug discounting. Finally, data with respect to SUD severity could help explain this difference, as well as other variables such as withdrawal symptoms and/or craving intensity, and comparisons between recovery and non-recovery cohorts. Supplementary Material Supplemental Material NIHMS2151481-supplement-Supplemental_Material.docx (23.8KB, docx) Public Significance Statement: We found that the preference for immediate over delayed rewards for individuals in substance use disorder recovery varies by the substance considered. These differences in substance-specific preferences highlight the need to recognize personalized substance use disorder recovery pathways. Funding: This study was supported by the Fralin Biomedical Research Institute, and the Longitudinal Study of Recovery: Psychosocial Functioning, Relapse, and Neuro-Behavioral Markers (R01AA029135). Declaration of Interests: Although the following activities/relationships do not create a conflict of interest pertaining to this manuscript, in the interest of full disclosure, Dr. Tegge would like to report the following: A. N. Tegge works on a project supported by Indivior, Inc. W. K. Bickel is a principal of HealthSim, LLC; BEAM Diagnostics, Inc.; and Red 5 Group, LLC. In addition, he serves on the scientific advisory board for Ria Health and as a consultant for Lumanity and AlphaSights. We would like to acknowledge the support and contribution by the late Dr. Warren Bickel in the conceptualization, preparation, supervision, and review of this paper. Footnotes CReDiT statement: ANN: conceptualization, data curation, formal analysis, investigation, methodology, software, visualization, writing — original draft, writing — review and editing DARC: investigation, writing — original draft, writing — review and editing SX: formal analysis, methodology, software, writing — review and editing ANT: data curation, formal analysis, supervision, validation, writing — review and editing WKB conceptualization, funding acquisition, methodology, project administration, resources, supervision References Athamneh LN, Freitas Lemos R, Basso JC, Tomlinson DC, Craft WH, Stein MD, & Bickel WK (2022). The phenotype of recovery II: The association between delay discounting, self-reported quality of life, and remission status among individuals in recovery from substance use disorders. Experimental and Clinical Psychopharmacology, 30(1), 59–72. 10.1037/pha0000389 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Babb S, Malarcher A, Schauer G, Asman K, & Jamal A (2017). Quitting Smoking Among Adults — United States, 2000–2015. Morbidity and Mortality Weekly Report. Surveillance Summaries, 65(52), 1457–1464. 10.15585/mmwr.mm6552a1 [ DOI ] [ PubMed ] [ Google Scholar ] Benjamini Y, & Hochberg Y (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 57(1), 289–300. 10.1111/j.2517-6161.1995.tb02031.x [ DOI ] [ Google Scholar ] Bickel WK, Landes RD, Christensen DR, Jackson L, Jones BA, Kurth-Nelson Z, & Redish AD (2011). Single- and cross-commodity discounting among cocaine addicts: the commodity and its temporal location determine discounting rate. Psychopharmacology, 217(2), 177–187. 10.1007/s00213-011-2272-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Bickel WK, Odum AL, & Madden GJ (1999). Impulsivity and cigarette smoking: delay discounting in current, never, and ex-smokers. Psychopharmacology, 146(4), 447–454. 10.1007/pl00005490 [ DOI ] [ PubMed ] [ Google Scholar ] Bujarski S, O’Malley SS, Lunny K, & Ray LA (2013). The effects of drinking goal on treatment outcome for alcoholism. Journal of Consulting and Clinical Psychology, 81(1), 13–22. 10.1037/a0030886 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cabral DAR, Tegge AN, Dwyer CL, Quddos F, Kaur RP, Nguyen J, … Bickel WK (2024). Associations between delay discounting and unhealthy behaviors in substance use recovery. Drug and Alcohol Dependence, 262(111395), 111395. 10.1016/j.drugalcdep.2024.111395 [ DOI ] [ PubMed ] [ Google Scholar ] Dwyer CL, Tegge AN, Craft WH, Tomlinson DC, Athamneh LN, & Bickel WK (2023). The Phenotype of Recovery X: Associations between delay discounting, regulatory flexibility, and remission from substance use disorder. Journal of Substance Use and Addiction Treatment, 209122. 10.1016/j.josat.2023.209122 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Green L, & Myerson J (2019). On the Complexity of Discounting, Choice Situations, and People. Perspectives on Behavior Science, 42(3), 433–443. 10.1007/s40614-019-00209-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Hasin DS, O’Brien CP, Auriacombe M, Borges G, Bucholz K, Budney A, … Grant BF (2013). DSM-5 criteria for substance use disorders: recommendations and rationale. The American Journal of Psychiatry, 170(8), 834–851. 10.1176/appi.ajp.2013.12060782 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Heather N, Adamson SJ, Raistrick D, Slegg GP, & UKATT Research Team. (2010). Initial preference for drinking goal in the treatment of alcohol problems: I. Baseline differences between abstinence and non-abstinence groups. Alcohol and Alcoholism, 45(2), 128–135. 10.1093/alcalc/agp096 [ DOI ] [ PubMed ] [ Google Scholar ] Heil SH, Johnson MW, Higgins ST, & Bickel WK (2006). Delay discounting in currently using and currently abstinent cocaine-dependent outpatients and non-drug-using matched controls. Addictive Behaviors, 31(7), 1290–1294. 10.1016/j.addbeh.2005.09.005 [ DOI ] [ PubMed ] [ Google Scholar ] Johnson MW, Bickel WK, Baker F, Moore BA, Badger GJ, & Budney AJ (2010). Delay discounting in current and former marijuana-dependent individuals. Experimental and Clinical Psychopharmacology, 18(1), 99–107. 10.1037/a0018333 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Kenward MG, & Roger JH (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics,53(3), 983–997. 10.2307/2533558 [ DOI ] [ PubMed ] [ Google Scholar ] Kirby KN, & Petry NM (2004). Heroin and cocaine abusers have higher discount rates for delayed rewards than alcoholics or non-drug-using controls. Addiction, 99(4), 461–471. 10.1111/j.1360-0443.2003.00669.x [ DOI ] [ PubMed ] [ Google Scholar ] Lenth R (2024). emmeans: Estimated Marginal Means, aka Least-Squares Means. Rpackage version 1.10.5. https://CRAN.R-project.org/package=emmeans [ Google Scholar ] Locey ML, Buddiga NR, Barcelos Nomicos L, & Smith CA (2023). Commodity discounting: Obstacles and solutions. Psychology of Addictive Behaviors: Journal of the Society of Psychologists in Addictive Behaviors, 37(1), 25–36. 10.1037/adb0000879 [ DOI ] [ PubMed ] [ Google Scholar ] Madden GJ, Petry NM, Badger GJ, & Bickel WK (1997). Impulsive and self-control choices in opioid-dependent patients and non-drug-using control patients: Drug and monetary rewards. Experimental and Clinical Psychopharmacology, 5(3), 256–262. 10.1037//1064-1297.5.3.256 [ DOI ] [ PubMed ] [ Google Scholar ] Moody LN, Tegge AN, & Bickel WK (2017). Cross-commodity delay discounting of alcohol and money in alcohol users. The Psychological Record, 67(2), 285–292. 10.1007/s40732-017-0245-0 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Naudé GP, Reed DD, Jarmolowicz DP, Martin LE, Fox AT, Strickland JC, & Johnson MW (2021). Single- and cross-commodity discounting among adults who use alcohol and cannabis: Associations with tobacco use and clinical indicators. Drug and Alcohol Dependence, 229(Pt B), 109082. 10.1016/j.drugalcdep.2021.109082 [ DOI ] [ PubMed ] [ Google Scholar ] Odum AL (2011a). Delay discounting: I’m a k, you’re a k. Journal of the Experimental Analysis of Behavior, 96(3), 427–439. 10.1901/jeab.2011.96-423 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Odum AL (2011b). Delay discounting: trait variable? Behavioural Processes, 87(1), 1–9. 10.1016/j.beproc.2011.02.007 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Odum AL, Becker RJ, Haynes JM, Galizio A, Frye CCJ, Downey H, … Perez DM (2020). Delay discounting of different outcomes: Review and theory. Journal of the Experimental Analysis of Behavior, 113(3), 657–679. 10.1002/jeab.589 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Petry NM (2001). Delay discounting of money and alcohol in actively using alcoholics, currently abstinent alcoholics, and controls. Psychopharmacology, 154(3), 243–250. 10.1007/s002130000638 [ DOI ] [ PubMed ] [ Google Scholar ] Pritschmann RK, Yurasek AM, & Yi R (2021). A review of cross-commodity delay discounting research with relevance to addiction. Behavioural Processes, 186, 104339. 10.1016/j.beproc.2021.104339 [ DOI ] [ PubMed ] [ Google Scholar ] R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ [ Google Scholar ] Taylor H, Smith AP, & Yi R (2023). Valuation of future alcohol in cross-commodity delay discounting is associated with alcohol misuse/consequences. Psychology of Addictive Behaviors, 37(1), 166–176. 10.1037/adb0000863 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wesley MJ, Lohrenz T, Koffarnus MN, McClure SM, De La Garza R II, Salas R, …, & Montague PR (2014). Choosing money over drugs: the neural underpinnings of difficult choice in chronic cocaine users. Journal of Addiction, 2014, 189853. 10.1155/2014/189853 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. 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