RESUMEN

OBJECTIVES: Despite a rich history of behavioral economic research on substance use there remains a need for further exploration of behavioral mechanisms that may underlie the etiology or persistence of substance use disorder. The purpose of this study was to measure the association between delay discounting and the endowment effect in people who smoke cigarettes, use cocaine, and controls, using online crowdsourcing. METHODS: Participants were categorized to a cocaine group (n = 36), cigarette group (n = 48), or control group (n = 47) based on recent reported drug use. Based on group, participants completed up to three delay discounting tasks (i.e., money, cigarettes and cocaine), an endowment effect task for multiple commodities, and other questionnaires. RESULTS: Participants in the cocaine and cigarette group demonstrated an increased rate in discounting for money compared to controls. Participants in the cocaine group had a less pronounced endowment effect for beer, compared to controls, as suggested by willingness to accept less to sell beer. A significant negative association was found between endowment ratios for non-drug commodities and delay discounting for cigarettes, but not monetary or cocaine delay discounting, indicating an inconsistent relationship between the two measures. CONCLUSIONS: These results support prior research demonstrating a relationship between cocaine and cigarette use and delay discounting and extend that work by measuring the association between delay discounting and the endowment effect. Future research should include both loss aversion and endowment effect tasks and compare their relationship with delay discounting among people that use drugs.

RESUMEN

The COVID-19 pandemic provided an opportunity to investigate factors related to public response to public health measures, which could help better prepare implementation of similar measures for inevitable future pandemics. To understand individual and environmental factors that influence likelihood in engaging in personal and public health measures, three crowdsourced convenience samples from Amazon Mechanical Turk (MTurk) completed likelihood-discounting tasks of engaging in health behaviors given a variety of hypothetical viral outbreak scenarios. Experiment 1 assessed likelihood of mask wearing for a novel virus. Experiment 2 assessed vaccination likelihood based on efficacy and cost. Experiment 3 assessed likelihood of seeking health care based on number of symptoms and cost of treatment. Volume-based measures and three-dimensional modeling were used to analyze hypothetical decision making. Hypothetical public and personal health participation increased as viral fatality increased and generally followed a hyperbolic function. Public health participation was moderated by political orientation and trust in science, whereas treatment-seeking was only moderated by income. Analytic methods used in this cross-sectional study predicted population-level outcomes that occurred later in the pandemic and can be extended to various health behaviors.

Asunto(s)

RESUMEN

Contingency management (CM), in which financial incentives are provided upon verification of abstinence from alcohol, cigarettes, and/or illicit substances, is one of the most highly effective and empirically supported treatments for substance use disorders. However, the financial cost of implementation has been identified as a major barrier to implementation of this treatment. The purpose of this study was to develop behavioral economic purchase tasks to assess interest in CM as a function of treatment cost and perceived effectiveness of CM as a function of abstinence incentive size in alcohol drinkers. Alcohol drinkers recruited from Amazon Mechanical Turk (MTurk) completed behavioral economic purchase tasks measuring demand for CM based on targeted abstinence intervals and treatment effectiveness and alcohol use disorder severity assessments. Nonlinear mixed-effects modeling was used to fit demand curves and assess the relationship between individual characteristics and demand metrics for CM. Results reveal that participants reported higher probability of remaining abstinent from drinking when offered larger incentives and required larger incentives when duration of abstinence required to earn the incentive was increased. Additionally, willingness to pay for treatment increased as effectiveness of treatment increased. Abstinence interval and treatment effectiveness are important features to consider when developing effective CM for widespread use, as these variables affected participants' likelihood of being abstinent and their interest in treatment. Future work will validate these assessments with actual treatment outcomes and determine predictors of CM treatment effectiveness. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Asunto(s)

RESUMEN

OBJECTIVE: Howard Rachlin wrote extensively on how value diminishes in a hyperbolic form, and he contributed to understanding choice processes between different commodities as a molar pattern of behavior. The field of behavioral economic demand has been dominated by exponential decay functions, indicating that decreases in consumption of a commodity are best fit by exponential functions. Because of the success of Rachlin's equation at describing how hyperbolic decay affects the value of a commodity across various factors (e.g., delay, probability, social distance), we attempted to extend his equation to behavioral economic demand data for alcohol and opioids. METHOD: Rachlin's discounting equation was applied to estimate consumption on alcohol purchase task data and nonhuman drug demand data. We compared results of his equation to the exponentiated demand equation using both a mixed-effects modeling approach and a two-stage approach. RESULTS: Rachlin's equation provided better fits to consumption data than the exponentiated equation for both mixed-effects and two-stage modeling. We also found that traditional demand metrics, such as Pmax, can be derived analytically when using Rachlin's equation. Certain metrics derived from Rachlin's equation appeared to be related to clinical covariates in ways similar to the exponentiated equation. CONCLUSIONS: Rachlin's equation better described demand data than did the exponentiated equation, indicating that demand for a commodity may decrease hyperbolically rather than exponentially. Other benefits of his equation are that it does not have the same pitfalls as the current exponential equations and is relatively straightforward in its conceptualization when applied to demand data. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Asunto(s)

RESUMEN

Behavioral economics has been a fruitful area of research in substance use. Mathematical descriptions of how individuals temporally discount the value of a commodity have been correlated with substance use and mathematical descriptions of drug consumption decreasing as a function of price (i.e., demand) predict maladaptive substance use. While there is a logical assumption that temporal factors affect demand for a drug, little has been done to merge these models. Thus, the purpose of this study was to combine models of discounting and demand, extending Howard Rachlin's work and contributions to novel areas of study. Data from 85 participants from Amazon Mechanical Turk (MTurk) who completed a hypothetical cigarette purchase task that included price of and delay to cigarettes were analyzed. Multilevel modeling was used to determine descriptive accuracy of combined additive and multiplicative models of discounting and demand. Of the discounting models used in conjunction with the exponentiated demand equation, the Rachlin hyperboloid best described the delay dimension of consumption. The multiplicative version of the Rachlin equation applied to both delay and price outperformed other models tested. Therefore, existing models of discounting and demand can be extended to modeling consumption data from complex multidimensional experimental arrangements.

Asunto(s)

RESUMEN

Delay discounting reflects the rate at which a reward loses its subjective value as a function of delay to that reward. Many models have been proposed to measure delay discounting, and many comparisons have been made among these models. We highlight the two-parameter delay discounting model popularized by Howard Rachlin by demonstrating two key practical features of the Rachlin model. The first feature is flexibility; the Rachlin model fits empirical discounting data closely. Second, when compared with other available two-parameter discounting models, the Rachlin model has the advantage that unique best estimates for parameters are easy to obtain across a wide variety of potential discounting patterns. We focus this work on this second feature in the context of maximum likelihood, showing the relative ease with which the Rachlin model can be utilized compared with the extreme care that must be used with other models for discounting data, focusing on two illustrative cases that pass checks for data validity. Both of these features are demonstrated via a reanalysis of discounting data the authors have previously used for model selection purposes.

Asunto(s)

RESUMEN

Behavioral economic demand has been shown to have high utility in quantifying the value or consumption of a commodity. Demand describes the relationship between cost and consumption of a commodity, and tends to be curvilinear with consumption approaching zero as the cost increases to a sufficiently high cost to suppress consumption completely. Over a period spanning greater than three decades, behavioral economists have made great strides in the modeling of demand and addressing analytical challenges, although this work is not complete and unresolved challenges remain. The analytical challenges associated with modeling zeros both when they arise as consumption values of zero and when consumption at zero cost is assessed have been a substantial part of this evolution in models. The goals of this methodological review are to provide a historical overview of the major behavioral economic demand models that have been proposed, describe some of the common difficulties with analyzing behavioral economic demand, and discuss general considerations for the analysis of demand. In an environment with evolving and multiple competing analytical practices, we conclude that researchers can maximize scientific rigor by embracing transparency in their analysis choices and employing techniques such as sensitivity analyses to determine if their analysis choices impact the conclusions of their experiments.

Asunto(s)

RESUMEN

Smoking rates among those who use prescribed or recreational opioids are significantly higher than the general population. Hypothesized neuropharmacological interactions between opioids and nicotine may contribute to this pattern of polysubstance use, especially during withdrawal. However, little research has examined how the withdrawal of one substance may affect the consumption of the other (i.e., cross-drug withdrawal effects). Behavioral economic demand tasks (e.g., hypothetical purchase tasks) can be used to quickly assess the value of a drug. Crowdsourcing can be a convenient tool to gain preliminary insight into different processes in substance valuation that may otherwise be impossible or prohibitively difficult to study. The purpose of the present study was to provide a preliminary examination of the effects of hypothetical withdrawal of cigarettes and opioids on the consumption of those drugs among polysubstance users. Amazon Mechanical Turk workers who reported daily smoking and at least monthly opioid use completed a series of hypothetical purchase tasks for doses of opioids and cigarettes under various withdrawal conditions. Sensitivity to the price of both drugs decreased when under withdrawal for either, indicating a higher drug value of cigarettes and opioids due to effects of cross-drug withdrawal. Nicotine and opioid dependence severity, impulsive choice, and riskiness were also positively related to drug purchasing. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Asunto(s)

RESUMEN

AIM: Identifying correlates of suicidality is an important goal for suicide researchers because these correlates may predict suicidal behaviors. Psychological tasks that assess sensitivity to the outcomes of actions (i.e., consequence-based learning) have been commonly used by researchers seeking to identify correlates of suicidality. This is likely due to the straightforward integration of the tasks within most theoretical frameworks for understanding suicidality. Contextual factors have been shown to have a substantial effect on responding in behavior-outcome tasks. However, the direct relevance of these factors as determinants of behavior in suicide research is not clear. Thus, the purpose of this review was to assess the role of context in tasks involving behavior-outcome relations in suicide research. METHODS: Four databases were searched using terms from general learning theory. Articles that featured evaluation of tasks with hypothetical or real outcomes to differentiate suicidality were included. RESULTS: Eighty-two studies met inclusion criteria. Across studies there were 27 different tasks. Most instances of tasks across studies involved rewards (76.9%), while others emphasized punishment (15.7%), social (5.6%), or virtual suicide (1.8%) outcomes. Differentiation of suicidality was detected by 43.4%, 64.7%, 83.3%, and 50% of tasks featuring reward, punishment, social contexts, and virtual suicide respectively. All but five studies were retrospective. CONCLUSION: Tasks that more closely mimic contexts and outcomes related to suicide appear to produce more pronounced differentiation of people with suicidality from people without suicidality. The lack of prospective designs is an important limitation of the literature.HIGHLIGHTSTasks that involve punishment or social outcomes better discriminate suicidality.Reward-based tasks are overused in suicide research.The conditioning hypothesis of suicidality is closely aligned with the literature.Only 5 of 82 studies incorporated prospective measures.

Asunto(s)

RESUMEN

The Questions About Behavioral Function (QABF) has a high degree of convergent validity, but there is still a lack of agreement between the results of the assessment and the results of experimental function analysis. Machine learning (ML) may improve the validity of assessments by using data to build a mathematical model for more accurate predictions. We used published QABF and subsequent functional analyses to train ML models to identify the function of behavior. With ML models, predictions can be made from indirect assessment results based on learning from results of past experimental functional analyses. In Experiment 1, we compared the results of five algorithms to the QABF criteria using a leave-one-out cross-validation approach. All five outperformed the QABF assessment on multilabel accuracy (i.e., percentage of predictions with the presence or absence of each function indicated correctly), but false negatives remained an issue. In Experiment 2, we augmented the data with 1,000 artificial samples to train and test an artificial neural network. The artificial network outperformed other models on all measures of accuracy. The results indicated that ML could be used to inform conditions that should be present in a functional analysis. Therefore, this study represents a proof-of-concept for the application of machine learning to functional assessment.

RESUMEN

Contingency management (CM) is an efficacious incentive-based intervention for promoting drug abstinence, but treatment providers have not widely adopted it. Smartphone and smart debit card technologies can deliver automated, patient-centered, high-fidelity CM and related services, including cognitive behavioral therapy and appointment reminders. This study evaluated clinical outcomes associated with an integrated smartphone-smartcard platform in an inner-city outpatient clinic. We enrolled adults with opioid use disorder (n = 85) over nine weeks, and they received CM services for four months. We retrospectively compared them to matched controls who received services at the same time, from the same provider at a similar, nearby clinic in the same city. The platform was associated with significantly higher rates of counseling appointment attendance in study months 2 to 4 (9.6%-18.0% increases) and an odds ratio of 4.84 for increased proportion of urine samples consistent with drug abstinence and adherence to prescribed medication, compared to controls at 120 days (P < 0.05). Overwhelmingly, participants reported that they found the platform acceptable. These results suggest that the platform is an effective method for remote delivery of CM services that could overcome key logistical barriers to widespread adoption of CM among treatment providers.

Asunto(s)