Cannabis is among the most used illicit drugs in the United States (Substance Abuse and Mental Health Services Administration., 2020). It is currently legal for medical use, recreational use, or both in all but 4 states. As legalization expands, so will the potential for cannabis-related problems (O’Grady et al., 2022). Access to legal and high-potency cannabis products is associated with a host of public health problems, including increases in motor vehicle crash fatalities (Windle et al., 2021), adolescent cannabis-associated emergency department and urgent care visits (Wang et al., 2018), cannabis-induced psychosis, and cannabis use disorder (Petrilli et al., 2022). Heavy use is associated with withdrawal symptoms in some people (Bonnet and Preuss, 2017, Razban et al., 2022), including cannabis hyperemesis syndrome (Englund et al., 2012, Razban et al., 2022). These data indicate a growing public health problem needing scientific solutions.

Behavioral economics combines microeconomics and psychology to understand choice behavior, offering insights into the public health implications of cannabis. Reinforcer pathology theories of addiction characterize substance use disorders by the excessive valuation of a substance relative to other available non-drug reinforcers and the disproportionate allocation of behavior toward the immediate acquisition or consumption of a substance despite long-term problems (Acuff et al., 2023, Bickel et al., 2014). The predominant behavioral economic method of determining relative substance valuation is a commodity purchase task, which measures the persistence of substance purchasing behavior against increasing prices. In a typical task, researchers ask participants how much of a commodity they would buy at various prices. The goal is to measure demand or the quantitative association between the consumption of a commodity and its cost. Purchase tasks yield indices like intensity (i.e., consumption at free price), Omax (i.e., maximum expenditure), Pmax (i.e., the price corresponding to Omax), and breakpoint (i.e., the price that suppresses consumption to zero). Researchers also employ nonlinear demand curve modeling (e.g., Hursh & Silberberg, 2008; Koffarnus et al., 2015) to determine the alpha parameter (α), reflecting the rate of change in elasticity.

Researchers commonly use a marijuana purchase task (MPT) to assess cannabis demand (Collins et al., 2014). The standard MPT configuration asks participants how much cannabis they would consume (e.g., grams of cannabis, joints, or individual hits) in a typical cannabis use situation or over a specific time interval (e.g., one week; Aston et al., 2021a, Aston et al., 2021b). Variations of the MPT assess demand in the present moment, allowing researchers to manipulate drug-use contexts and examine their effects on cannabis demand (e.g., Metrik et al., 2016; see also Acuff et al., 2020 for a review of experimental manipulations of demand). Research using MPTs designed to assess state or present moment demand primarily has taken two approaches: a) manipulating the hypothetical situation in which purchasing takes place and b) manipulating the presence of conditioned drug stimuli (hereafter referred to as "cues").

The first way researchers have historically measured contextual influences on demand is through vignette manipulations. Though several studies have examined how alcohol demand changes due to vignette manipulations (e.g., Gentile et al., 2012; Gilbert et al., 2012; Miller et al., 2023; Skidmore & Murphy, 2011; Teeters & Murphy, 2015), few studies have examined how cannabis changes due to vignette manipulations (e.g., Acuff et al., 2022; Ferguson et al., 2021). In one example, Ferguson et al. (2021) had adults who use cannabis recruited via Amazon Mechanical Turk (MTurk) complete MPTs under a no-responsibility scenario and a next-day job interview scenario. Demand for cannabis was more sensitive to price increases in the interview condition than in the standard (i.e., no-responsibility) condition, as evidenced by higher Omax and breakpoint and lower α. The results from these studies emphasize the importance of considering context and cannabis use; however, there is a lack of research on a wide range of potentially relevant contexts. For example, there is a lack of research assessing demand in sleep contexts despite evidence that many people use cannabis for sleep-related reasons.

The next category of manipulations is a cueing procedure, where researchers measure changes in demand after exposure to substance-related cues (Reynolds & Monti, 2013). Studies with alcohol cues (Amlung et al., 2012, Amlung and MacKillop, 2014, MacKillop et al., 2010) and tobacco cues (Aston et al., 2021a, Aston et al., 2021b, MacKillop et al., 2012) demonstrated that demand is sensitive to substance-related cues, but only a single study (Metrik et al., 2016) has examined the impact of cannabis cues on cannabis demand. Metrik et al. (2016) had 94 people who use cannabis frequently undergo a neutral cue exposure involving office supplies, followed by exposure to lit cannabis joints. Demand for cannabis and self-reported craving were measured after each cue set. Results indicated that exposure to cannabis cues significantly increased self-reported craving, intensity, Omax, and decreased α relative to the neutral cues.

The literature reviewed above suggests that drug use situations and the presence of cannabis cues impact demand for cannabis. However, other important questions remain unanswered. First, every state-based MPT study (Acuff et al., 2022, Ferguson et al., 2021, Metrik et al., 2016) examined the impact of either drug use context or cues separately, making it unclear how these two factors may interact. For instance, specific contexts may suppress demand for cannabis, but only in the absence of cannabis cues. Second, few studies have demonstrated how cannabis demand changes in everyday situations such as driving and sleep (Berey et al., 2022). Given that cannabis legalization has been associated with increased rates of motor vehicle crash fatalities (Windle et al., 2021), it is vital to replicate and extend studies like Teeters and Murphy (2015) using cannabis instead of alcohol. Similarly, cannabis is widely marketed and colloquially claimed to be a sleep aid; however, there is minimal evidence to back these claims (Abrams, 2018, Angarita et al., 2016, Babson and Bonn-Miller, 2014, Winiger et al., 2021). Therefore, the MPT may be one way further to elucidate the association between sleep and cannabis use.

We attempted to address these gaps in the literature by examining the influence of typical, driving, and sleep contexts, cannabis cues, and potential interactions between these factors on cannabis demand in a sample of adults who use cannabis. This study used a mixed repeated measures design in which we randomly assigned participants to a cannabis or neutral cues group. All participants completed four MPTs interspersed by exposure to either cannabis or neutral pictorial cues depending on their assigned cue group. The first two MPTs involved a typical scenario to test cue effects independent of context manipulation. The final two MPTs involved a driving or sleep scenario, completed in a counterbalanced order. We hypothesized that cannabis demand would be a) greater among participants viewing cannabis cues than those viewing neutral cues, and b) greatest in the typical context, followed by the sleep context, and finally, the driving context for all participants regardless of cue exposure group.