Working memory capacity predicts cannabis-induced effects on alcohol urge
Center for Alcohol and Addiction Studies, Brown University School of Public Health, Providence, RI 02903, USA
Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI, USA
Department of Psychiatry, Yale University, New Haven CT, USA
Providence VA Medical Center, Providence, RI 02908, USA
Abstract
Background:
Cannabis has shown mixed results in its association with alcohol urge, which may be explained by individual differences. One such factor, working memory capacity (WMC) is associated with drug-related cue reactivity and implicated in alcohol use and problems. In the current study, we examined whether WMC moderates the acute effect of cannabis on alcohol urge in a randomized placebo-controlled crossover trial.
Methods:
Participants aged 21 to 44 (N = 125, 32 % female) reporting heavy alcohol use and cannabis use ≥ twice weekly completed a laboratory protocol across three days where they smoked a placebo, 3.1 % delta-9 tetrahydrocannabinol (THC), and 7.2 % THC cannabis cigarette. Participants were asked to rate their alcohol urge pre and post smoking. Prior to the experimental sessions, participants completed WMC measures including the n-back and the complex span tasks, operation span (OS) and symmetry span (SS).
Results:
Those with higher WMC, as assessed via the SS task, reported significantly lower alcohol urge after smoking the 7.2 %, but not the 3.1 %, THC dose, relative to placebo. Performance on the OS task was not associated with alcohol urge. Lower WMC as determined via n-back scores was associated with higher alcohol urge overall, but n-back scores did not moderate the impact of cannabis on alcohol urge.
Conclusion:
Findings suggest individuals with higher but not lower working memory experience lower alcohol urge under acute effects of cannabis. Although cannabis is increasingly perceived as a substitute for alcohol, individuals with lower working memory may be less likely to experience such benefits when attempting to reduce their drinking.
Untitled section
Keywords: Alcohol craving, Cannabis, Delta-9 tetrahydrocannabinol, Working memory
Article notes
Untitled section
Issue date 2026 Mar.
1.Introduction
In the United States, 47.5 % of individuals over the age of 12 report alcohol use in the past month, and 10.2 % of those reporting past year alcohol use met criteria for alcohol use disorder (AUD) (SAMHSA, 2021). Craving is a key feature of AUD and a robust predictor of relapse (Schneekloth et al., 2012; Stohs et al., 2019). Related to craving is the concept of “urge” which represents the more proximal behavioral intention to use and is a valuable measure of a momentary desire to use a substance (Sayette et al., 2000). Given the high prevalence of cannabis co-use in the population (Gonçalves et al., 2023), research has increasingly focused on how cannabis use impacts alcohol-related outcomes, such as craving or urge (McManus et al., 2025). Co-administration of alcohol and delta-9 tetrahydrocannabinol (THC), the primary psychoactive cannabinoid in cannabis, enhances pleasurable subjective effects of both substances in adults samples who co-use (Lukas & Orozco, 2001; McManus et al., 2025). Thus, it is possible that cannabis-related cues trigger urges to drink as a learned or pharmacologic response to cannabis, as has been shown in the reverse direction of alcohol cues on cannabis craving among adolescents and young adults (Wycoff et al., 2023). However, findings from the few studies of adults who co-use that specifically examined cannabis’s acute effects on alcohol craving/urge are mixed showing no effect or reduced alcohol craving following cannabis administration (Metrik et al., 2025; Pince et al., 2025; Venegas & Ray, 2023).
Notably, individual trait-level moderators, such as cognitive control and executive functioning, could impact these effects (Tiffany & Conklin, 2000; van Lier et al., 2018; Wilcox et al., 2014). One relevant cognitive factor in the association between cannabis use and alcohol urge is working memory capacity (WMC). WMC refers to the ability to temporarily store and manipulate information (Bechara & Martin, 2004; Conway et al., 2005). Low WMC has been associated with increased susceptibility to substance use and problems, due to difficulties with self-regulation and decision making, which are critical in the development and maintenance of addictive behaviors (Bechara & Martin, 2004). Additionally, research in college students and young adults shows those with lower WMC display diminished cognitive control, leading to more attention on the immediate rewards of substance use relative to potential long-term consequences, heightening risk for substance misuse (Ellingson et al., 2014; Endres et al., 2011).
Regarding urge, research shows that lower WMC, particularly under stress, is associated with reduced ability to regulate emotional response to substance-related cues among adolescents (Miranda et al., 2019; Treloar Padovano & Miranda, 2018). Similarly, drug-related cue reactivity, a strong predictor of substance use, is more pronounced in adolescents with lower WMC, suggesting that these individuals may be more susceptible to fluctuations in alcohol urge (Grenard et al., 2008). The current study extended this work in adults to examine whether WMC moderated the acute effect of cannabis on alcohol urge in a controlled laboratory setting. We hypothesized that for individuals with lower WMC, cannabis with THC (relative to placebo) would be associated with greater alcohol urge.
2.Method
This is a secondary data analysis of clinical trial NCT02983773; PI: Metrik. Primary outcomes, full method description, and detailed participant demographics can be found in Metrik et al., 2025.
2.1.Participants
Participants met the following eligibility criteria: English fluency, age 21 to 44, at least twice weekly cannabis use in the past month and at least weekly in the past six months, positive THC urine toxicology screen at baseline (and negative for other substances), report smoking as a familiar mode of cannabis administration, heavy episodic drinking (≥5 drinks for males, ≥4 drinks for females) at least monthly over the past year, not meeting criteria for current DSM-5 major depressive episode, manic or hypomanic episode, panic disorder, or psychotic symptoms, ability to abstain from alcohol and cannabis for 24 h without withdrawal, no intent to quit or receive treatment for cannabis or alcohol use, not pregnant or nursing, no contraindicated medical issues confirmed via physical exam, body mass index less than or equal to 30, no history of seizures, use an average of less than 20 nicotine cigarettes daily, and history of simultaneous alcohol and cannabis use.
2.2.Procedure
Participants completed a baseline session following overnight cannabis abstinence (confirmed via carbon-monoxide, see Metrik et al., 2025) where demographic, recent substance use history, and WMC were assessed, followed by three experimental sessions, in which placebo, 3.1 % THC, and 7.2 % THC cannabis were administered in counterbalanced order. Prior to smoking, participants completed assessments, including alcohol urge, which were repeated after smoking. Cannabis cigarettes provided by the NIDA Drug Supply Program were rolled at both ends and administered via a paced puffing procedure (Foltin et al., 1987; Metrik et al., 2012).
3.Measures
3.1.Working Memory Capacity (WMC)
Given WMC does not reflect a single construct (Kane et al., 2007) and the tendency for specific tasks to tap individualized cognitive skills (Harvey, 2019; Redick et al., 2013), we used three measures of WMC representing unique constructs to probe the potential moderation of cannabis’ impact on alcohol urge.
3.2.N-back
The n-back paradigm focuses on flexible updating and continuous recognition capabilities and includes a spatial 1-back and 2-back task requiring maintenance and updating of 1 and 2 locations at a time. It requires participants to keep in mind the location of a sequence of squares on a screen, and to press a specific key when the location of the square is the same as 1 or 2 squares before. The primary dependent variable is d-prime, which incorporates both accuracy on hits and correct rejections (Kramer et al., 2014).
3.3.Complex Span Tasks: Operation Span (OS) and Symmetry Span (SS)
Relative to the continuous-recognition tasks (e.g., N-back), complex WMC tasks involve both storage and control that require access to information while completing competing cognitive tasks (Baddeley, 2007), and have shown unique variance to the N-back task (Kane et al., 2007). These tasks were administered via E-Prime (E-Prime 3.0, 2016). The OS task presented participants with a math operation followed by a TRUE or FALSE determination. Participants were presented with a letter to keep in mind and recall at the end of the particular set. After a series of math operations and letter presentations, they were asked to recall the letters in the order presented (Unsworth et al., 2005). The SS task involves first presenting a large array of white and black squares and asking participants to make a symmetry judgment. Then, a 4×4 matrix is presented with one position highlighted in red. After a series of symmetry judgments and matrix position locations, participants the matrix locations are to be recalled in order. The primary outcome in both tasks is the partial score (i.e., credit for each correctly recalled item) (Redick et al., 2012).
3.4.Alcohol Urge
The single item “I have an urge for alcohol” with response options from 1: ‘Strongly Disagree’ to 7: ‘Strongly Agree’, an item adapted from the Marijuana Craving Questionnaire (Heishman et al., 2001) for alcohol was assessed pre-smoking and immediately after smoking.
3.5.Recent History of Alcohol and Cannabis Use
The Time-Line Follow-back was used to assess past 60-day cannabis (frequency) and alcohol (frequency and quantity) use (Sobell & Sobell, 1992).
3.6.Alcohol and Cannabis Use Disorder (AUD/CUD)
The Structured Clinical Interview for DSM-5-Research Version (SCID-5-RV (First et al., 2002)) was used to assess AUD and CUD at baseline.
3.7.Data analysis plan
Participants who completed computer-based tasks and at least two laboratory sessions (one placebo) as part of the parent trial were included in the present analysis (N = 125).1 Generalized estimating equations (GEE) were used to examine the effects of dose (7.2 % THC and 3.1 % THC, ref: placebo), WMC (N-back, OS, SS), and their interaction on alcohol urge post-smoking. Dummy-coded variables were used for examining main effects of THC for 3.1 % and 7.2 % doses (ref: placebo) and moderation effects. Separate GEE models were used to test the moderation of each measure of WMC by interacting with the main effect of THC dose on alcohol urge. A time-varying covariate of pre-smoking urge was included in all models, in addition to person-level order of dose administration. For n-back models, participants with invalid scores were removed (n = 6) (Kramer et al., 2014). Analyses were conducted using SPSS 28.0 (IBM Corp, 2021).
4.Results
The sample (N = 125) self-reported the following racial/ethnic categories: 1 % American Indian/Alaska Native, 2 % Asian, 17 % Black/African American, 61 % White, 11 % other racial identity, 8 % multiracial identity, and 24 % Hispanic or Latinx. Thirty-two percent were female, and the average age was 25.76 (SD = 5.16). Participants reported drinking an average of 4.65 (SD = 2.10) drinks per drinking day and 95 (75 %) and 55 (43 %) met criteria for a current CUD and AUD diagnosis, respectively, at baseline. Mean urge score prior to experimental administration was 1.89 (SD = 1.29). Significant bivariate correlations were observed within WMC tasks. OS and SS were positively correlated (r = 0.25, p = 0.005) and the N-back (OS: r = 0.35, p < 0.001; SS: r = 0.33, p < 0.001).
Full GEE results are presented in Table 1. As previously reported (Metrik et al., 2025), the 7.2 % THC dose, relative to placebo, significantly suppressed alcohol urge immediately post-smoking. There was no significant effect of the 3.1 % dose, relative to placebo or the 7.2 % dose. On the SS task, a significant interaction between WMC and THC dose was observed on alcohol urge (B = −0.09, SE = 0.02, p = 0.04). Post-hoc simple slopes analyses examined the effect of dose at the mean and one standard deviation above and below for SS. As shown in Fig. 1, predicted urge values were significantly lower for the 7.2 % THC dose, relative to the placebo, at the mean (Est = −0.38, SE = 0.12, p = 0.002) and one standard deviation above the mean of SS (Est = −0.64, SE = 0.14, p < 0.001). All other simple slopes contrasts were not significant. In other words, those with average and high, but not low, WMC showed lower urge when administered the 7.2 % THC dose. There was no interaction between THC dose and N-back scores, but a significant negative main effect of N-back on urge was observed, suggesting lower WMC was associated with higher urge regardless of the cannabis dose smoked. There were no significant interactions or main effects of WMC on alcohol urge on the OS task.
| Parameter | B | Std. Error | 95 % Wald Confidence Interval | Sig. | |
|---|---|---|---|---|---|
| Lower | Upper | ||||
| (Intercept) | 0.22 | 0.32 | −0.40 | 0.84 | 0.48 |
| Dose Order | 0.10 | 0.04 | 0.02 | 0.18 | 0.02 |
| [7.2 % THC] | 0.32 | 0.45 | −0.55 | 1.19 | 0.48 |
| [3.1 % THC] | 0.17 | 0.33 | −0.48 | 0.82 | 0.61 |
| Pre-smoke urge | 0.62 | 0.06 | 0.51 | 0.73 | 0<.001 |
| Ospan | 0.03 | 0.02 | −0.00 | 0.07 | 0.06 |
| 7.2 % THC * Ospan | −0.04 | 0.03 | −0.09 | 0.01 | 0.11 |
| 3.1 % THC * Ospan | −0.02 | 0.02 | −0.06 | 0.02 | 0.37 |
| (Intercept) | 0.47 | 0.36 | −0.24 | 1.17 | 0.19 |
| Dose Order | 0.11 | 0.04 | 0.03 | 0.19 | 0.01 |
| Pre-smoke urge | 0.62 | 0.06 | 0.51 | 0.73 | <.001 |
| [7.2 % THC] | 0.35 | 0.41 | −0.46 | 1.15 | 0.40 |
| [3.1 % THC] | 0.06 | 0.37 | −0.67 | 0.78 | 0.88 |
| Sspan | 0.03 | 0.04 | −0.04 | 0.10 | 0.35 |
| 7.2 % THC * Sspan | −0.09 | 0.04 | −0.13 | −0.00 | 0.04 |
| 3.1 % THC * Sspan | −0.03 | 0.04 | −0.11 | 0.06 | 0.57 |
| (Intercept) | 1.22 | 0.28 | 0.67 | 1.78 | <.001 |
| Dose Order | 0.11 | 0.04 | 0.02 | 0.19 | 0.01 |
| Pre-smoke urge | 0.61 | 0.06 | 0.50 | 0.73 | <.001 |
| [7.2 % THC] | −0.34 | 0.32 | −0.95 | 0.28 | 0.29 |
| [3.1 % THC] | −0.45 | 0.31 | −1.07 | 0.16 | 0.15 |
| Nback | −0.28 | 0.12 | −0.51 | −0.04 | 0.02 |
| 7.2 % THC * Nback | −0.04 | 0.15 | −0.34 | 0.25 | 0.78 |
| 3.1 % THC * Nback | 0.17 | 0.19 | −0.20 | 0.54 | 0.36 |
5.Discussion
The current study is the first to our knowledge to examine if trait WMC moderated the effect of cannabis on alcohol urge. A significant interaction between the 7.2 % THC dose and the SS task was observed, suggesting that those with average and higher WMC may experience lower alcohol urge after higher doses of THC administration. The observed moderation effect was modest and therefore may not be clinically significant and should be considered preliminary and in need of replication. This interaction effect was not observed in the OS task or the n-back task. However, the n-back task did reveal a significant main effect on alcohol urge, suggesting those with lower WMC experienced higher alcohol urge, regardless of THC dose. These tasks have been shown to explain unique variance, and the SS tasks may be more robust across populations (Draheim et al., 2018). The unique effects of SS relative to the other measures of WMC may be partially explained by the complex span tasks’ ability to represent more real-world cognitive performance, such as reasoning and decision-making, relative to the n-back (Redick et al., 2013). Regarding the unique performance of the SS to the OS task, this may be explained by its more robust psychometric performance across samples (Draheim et al., 2018).
Overall, this study suggests that individuals with lower WMC (i.e., poorer executive functioning) may be less susceptible to the acute effect of cannabis on alcohol urge. This work is critical in light of recent suggestions that cannabis may serve as an effective substitute for alcohol among those trying to cut back on alcohol use (Gunn et al., 2021; Risso et al., 2020; Subbaraman, 2014) and is in contrast to complementary theory, which suggests that cannabis may lead to increased alcohol use (Subbaraman, 2016). Importantly, those with lower executive functioning, as is reflected in performance on WMC tasks, may not experience lower alcohol urge when experiencing cannabis’ acute effects. This is particularly relevant, as there is evidence that individuals with AUD and more alcohol-related problems may have lower WMC and executive control (Finn, 2002; Gunn & Finn, 2013). Given this, these individuals may not experience potential harm reduction benefits of cannabis to aid reductions in alcohol use. Clinical research on cannabinoids tested as a potential target for AUD should consider this potential impact for those with lower WMC. Although our sample included 43 % with AUD, cannabis use as a harm reduction strategy should be closely considered and further studied in samples who are seeking treatment for AUD.
6.Limitations and future directions
Study findings should be understood in the context of several limitations.. First, as with any laboratory study, findings may not translate to all settings and populations. Participants were required to meet a number of eligibility criteria in order to complete a laboratory-based administration study and the cannabis used in this study was provided by the NIDA drug supply, which may differ from cannabis currently used by consumers (ElSohly et al., 2021). Future work should consider examining these effects in naturalistic data collection or in laboratory studies using other cannabinoids and formulations. Second, it is important to acknowledge that WMC is not just a stable trait characteristic, but can also fluctuate at the state-level (Buschman & Miller, 2023; Lechner et al., 2016; Ward et al., 2020). Future studies should examine state-level changes in WMC, such as those initiated by anxiety (Ward et al., 2020) and particularly as they relate to the allostatic fluctuations characteristic of addiction (Kwako et al., 2019), which may also impact alcohol urge. Finally, here we just measured the acute effect of THC on alcohol urge, future work should expand to a more comprehensive understanding of cannabis’ effects on the full clinical conceptualization of alcohol craving to fully inform treatment implications for those with AUD.
In summary, findings suggest that WMC may moderate the impact of cannabis intoxication on alcohol urge. This work is preliminary yet suggests that individuals with lower WMC may not experience the substitution effects of using cannabis for alcohol.’
Acknowledgment
This study was funded by a National Institute on Alcohol Abuse and Alcoholism grant to Dr. Metrik (R01AA024091) and Research Career Development Award to Dr. Gunn (K08AA029711). The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs. The authors gratefully acknowledge Cassandra Delapaix, Hayley Buckey, Suzanne Sales, and Timothy Souza for their contribution to the project and the NIDA Drug Supply Program for providing cannabis plant material for this study.
Footnotes
Footnote Group
Data availability
Data will be made available on request.
References
Untitled section
References
- Baddeley AD (2007). Working Memory, Thought, and Action. Oxford Psychology Series.
- Bechara A, & Martin EM (2004). Impaired decision making related to working memory deficits in individuals with substance addictions. Neuropsychology, 18(1), 152.
- Buschman TJ, & Miller EK (2023). Working memory is complex and dynamic, like your thoughts. Journal of Cognitive Neuroscience, 35(1), 17–23. 10.1162/jocn_a_01940
- Conway AR, Kane MJ, Bunting MF, Hambrick DZ, Wilhelm O, & Engle RW (2005). Working memory span tasks: A methodological review and user’s guide. Psychonomic bulletin & review, 12(5), 769–786.
- Draheim C, Harrison TL, Embretson SE, & Engle RW (2018). What item response theory can tell us about the complex span tasks. Psychological Assessment, 30(1), 116–129. 10.1037/pas0000444
- Ellingson JM, Fleming KA, Vergés A, Bartholow BD, & Sher KJ (2014). Working memory as a moderator of impulsivity and alcohol involvement: Testing the cognitive-motivational theory of alcohol use with prospective and working memory updating data. Addictive Behaviors, 39(11), 1622–1631.
- ElSohly MA, Chandra S, Radwan M, Majumdar CG, & Church JC (2021). A Comprehensive Review of Cannabis Potency in the United States in the last Decade. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 6(6), 603–606. 10.1016/j.bpsc.2020.12.016
- Endres MJ, Rickert ME, Bogg T, Lucas J, & Finn PR (2011). Externalizing psychopathology and behavioral disinhibition: Working memory mediates signal discriminability and reinforcement moderates response bias in approach-avoidance learning. Journal of Abnormal Psychology, 120(2), 336–351. 10.1037/a0022501
- E-Prime 3.0. (2016). [Computer software]. Psychology Software Tools, Inc. https://support.pstnet.com/.
- Finn PR (2002). Motivation, working memory, and decision making: A cognitive-motivational theory of personality vulnerability to alcoholism. Behavioral and Cognitive Neuroscience Reviews, 1(3), 183–205. 10.1177/1534582302001003001
- First M. B. et, Spitzer RL, Gibbon M, Williams JBW, & First Spitzer, Gibbon W. (2002). Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Non-patient Edition (SCID-I/NP). In For DSMIV. Biometrics Research, New York State Psychiatric Institute.
- Foltin RW, Fischman MW, Pedroso JJ, & Pearlson GD (1987). Marijuana and cocaine interactions in humans: Cardiovascular consequences. Pharmacology, Biochemistry and Behavior, 28(4), 459–464. 10.1016/0091-3057(87)90506-5
- Gonçalves PD, Levy NS, Segura LE, Bruzelius E, Boustead AE, Hasin DS, Mauro PM, & Martins SS (2023). Cannabis Recreational Legalization and Prevalence of Simultaneous Cannabis and Alcohol Use in the United States. Journal of General Internal Medicine, 38(6), 1493–1500. 10.1007/s11606-022-07948-w
- Grenard JL, Ames SL, Wiers RW, Thush C, Sussman S, & Stacy AW (2008). Working memory capacity moderates the predictive effects of drug-related associations on substance use. Psychology of Addictive Behaviors, 22(3), 426.
- Gunn RL, Aston ER, & Metrik J (2021). Patterns of cannabis and alcohol co-use: Substitution versus complementary effects. Alcohol Research: Current Reviews, 4(1). 10.35946/arcr.v42.1.04
- Gunn RL, & Finn PR (2013). Impulsivity partially mediates the association between reduced working memory capacity and alcohol problems. Alcohol, 47, 3–8. 10.1016/j.alcohol.2012.10.003
- Harvey PD (2019). Domains of cognition and their assessment. Dialogues in Clinical Neuroscience, 21(3), 227–237. 10.31887/DCNS.2019.21.3/pharvey
- Heishman SJ, Singleton EG, & Liguori A (2001). Marijuana Craving Questionnaire: Development and initial validation of a self-report instrument. Addiction, 96(7), 1023–1034. 10.1046/j.1360-0443.2001.967102312.x
- Kane MJ, Conway ARA, Miura TK, & Colflesh GJH (2007). Working memory, attention control, and the n-back task: A question of construct validity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33(3), 615–622. 10.1037/0278-7393.33.3.615
- Kramer JH, Mungas D, Possin KL, Rankin KP, Boxer AL, Rosen HJ, Bostrom A, Sinha L, Berhel A, & Widmeyer M (2014). NIH EXAMINER: Conceptualization and development of an executive function battery. Journal of the International Neuropsychological Society, 20(1), 11–19. 10.1017/S1355617713001094
- Kwako LE, Schwandt ML, Ramchandani VA, Diazgranados N, Koob GF, Volkow ND, Blanco C, Goldman D. Neurofunctional Domains Derived From Deep Behavioral Phenotyping in Alcohol Use Disorder . Am J Psychiatry. 2019. Sep 1;176(9):744–753. doi: 10.1176/appi.ajp.2018.18030357. Epub 2019 Jan 4. Erratum in: Am J Psychiatry. 2019 Jun 1;176(6):489. doi: 10.1176/appi.ajp.2019.1766correction2.
- Lechner WV, Day AM, Metrik J, Leventhal AM, & Kahler CW (2016). Effects of alcohol-induced working memory decline on alcohol consumption and adverse consequences of use. Psychopharmacology, 233, 83–88. 10.1007/s00213-015-4090-z
- Lukas SE, & Orozco S (2001). Ethanol increases plasma D 9 -tetrahydrocannabinol (THC) levels and subjective effects after marihuana smoking in human volunteers. Drug and Alcohol Dependence, 64(2), 143–149. 10.1016/S0376-8716(01)00118-1
- McManus KR, Venegas A, Cooper ZD, & Ray LA (2025). Alcohol and cannabis co-use: Probing subjective response in eliciting cross-substance craving. Addictive Behaviors, 160, Article 108189. 10.1016/j.addbeh.2024.108189
- Metrik Aston, E. R., Gunn RL, MacKillop J, Swift R, & Kahler C. (2025). Acute Effects of Cannabis on Alcohol Craving and Consumption: A Randomized Controlled Crossover Trial. American Journal of Psychiatry. DOI: 10.1176/appi.ajp.20250115.
- Metrik JM, Kahler CW, Reynolds B, Mcgeary JE, Monti PM, Haney M, & Rohsenow DJ (2012). Balanced placebo design with marijuana: Pharmacological and expectancy effects on impulsivity and risk taking. Psychopharmacology (Berl), 223(4), 489–499. 10.1007/s00213-012-2740-y
- Miranda R, Wemm SE, Treloar Padovano H, Carpenter RW, Emery NN, Gray JC, & Mereish EH (2019). Weaker memory Performance exacerbates stress-induced cannabis craving in youths’ daily lives. Clinical Psychological Science, 7(5), 1094–1108. 10.1177/2167702619841976
- Pince CL, Stallsmith VT, Piercey CJ, Weldon K, Ruehrmund J, Dooley G, Bidwell LC, & Karoly HC (2025). Cannabis administration is associated with reduced alcohol consumption: Evidence from a novel laboratory co-administration paradigm. Drug and Alcohol Dependence, 112860. 10.1016/j.drugalcdep.2025.112860
- Redick TS, Broadway JM, Meier ME, Kuriakose PS, Unsworth N, Kane MJ, & Engle RW (2012). Measuring working memory capacity with automated complex span tasks. European Journal of Psychological Assessment, 28(3), 164–171. 10.1027/1015-5759/a000123
- Redick TS, Lindsey DRB, Oswald FL, McAbee ST, Redick TS, Hambrick DZ, Foster JL, Shipstead Z, Harrison TL, Hicks KL, Redick TS, Engle RW, & Lindsey DRB (2013). Complex span and n-back measures of working memory: A meta-analysis. Psychonomic Bulletin & Review, 20(6), 1102–1113. 10.3758/s13423-013-0453-9
- Risso C, Boniface S, Subbaraman MS, & Englund A (2020). Does cannabis complement or substitute alcohol consumption? A systematic review of human and animal studies. Journal of Psychopharmacology, 34(9), 938–954. 10.1177/0269881120919970
- SAMHSA. (2021). Key substance use and mental health indicators in the United States: Results from the 2020 National Survey on Drug Use and Health (HHS Publication No. PEP21–07-01–003, NSDUH Series H-56). Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration.
- Sayette MA, Shiffman S, Tiffany ST, Niaura RS, Martin CS, & Shadel WG (2000). The measurement of drug craving. Addiction (Abingdon, England), 95 Suppl 2 (Suppl 2), S189–210. DOI: 10.1080/09652140050111762.
- Schneekloth TD, Biernacka JM, Hall-Flavin DK, Karpyak VM, Frye MA, Loukianova LL, Stevens SR, Drews MS, Geske JR, & Mrazek DA (2012). Alcohol Craving as a Predictor of Relapse. The American Journal on Addictions, 21(s1), S20–S26. 10.1111/j.1521-0391.2012.00297.x
- Sobell L, & Sobell M (1992). Timeline Follow-Back: A technique for assessing self-reported alcohol consumption. In Psychosocial and biological methods (pp. 41–72). Humana Press.
- Stohs ME, Schneekloth TD, Geske JR, Biernacka JM, & Karpyak VM (2019). Alcohol Craving Predicts Relapse after Residential Addiction Treatment. Alcohol and Alcoholism, 54(2), 167–172. 10.1093/alcalc/agy093
- Subbaraman MS (2014). Can cannabis be considered a substitute medication for alcohol? Alcohol and Alcoholism, 49(3), 292–298. 10.1093/alcalc/agt182
- Subbaraman MS (2016). Substitution and complementarity of alcohol and cannabis: A review of the literature. Substance Use & Misuse, 51(11), 1399–1414. 10.3109/10826084.2016.1170145
- Tiffany ST, & Conklin CA (2000). A cognitive processing model of alcohol craving and compulsive alcohol use. Addiction, 95(8s2), 145–153. 10.1046/j.1360-0443.95.8s2.3.x
- Treloar Padovano H, & Miranda R (2018). Subjective cannabis effects as part of a developing disorder in adolescents and emerging adults. Journal of Abnormal Psychology, 127(3), 282–293. 10.1037/abn0000342
- Unsworth N, Heitz RP, Schrock JC, & Engle RW (2005). An automated version of the operation span task. Behavior Research Methods, 37(3), 498–505. 10.3758/bf03192720
- van Lier HG, Pieterse ME, Schraagen JC, Postel MG, de Haan H, & Noordzji ML (2018). Identifying viable theoretical frameworks with essential parameters for real-time and real world alcohol craving research: A systematic review of craving models. Addiction Research & Theory, 26(1). https://www.tandfonline.com/doi/full/10.1080/16066359.2017.1309525.
- Venegas A, & Ray LA (2023). Cross-substance primed and cue-induced craving among alcohol and cannabis co-users: An experimental psychopharmacology approach. Experimental and Clinical Psychopharmacology, 31(3), 683–693. 10.1037/pha0000621
- Ward RT, Lotfi S, Sallmann H, Lee H-J, & Larson CL (2020). State anxiety reduces working memory capacity but does not impact filtering cost for neutral distracters. Psychophysiology, 57(10), Article e13625. 10.1111/psyp.13625
- Wilcox CE, Dekonenko CJ, Mayer AR, Bogenschutz MP, & Turner JA (2014). Cognitive control in alcohol use disorder: Deficits and clinical relevance. Reviews in the Neurosciences, 25(1), 1–24. 10.1515/revneuro-2013-0054
- Wycoff AM, Treloar Padovano H, & Miranda R (2023). Cannabis craving in response to alcohol cues among adolescents and young adults in the laboratory and in daily life. Experimental and Clinical Psychopharmacology, 31(3), 674–682. 10.1037/pha0000614
Associated Data
Data Availability Statement
Data will be made available on request.