Delay discounting is associated with addiction and mental health measures while controlling for health behaviors and health barriers in a large US sample.

Background: Excessive discounting of future rewards [delay discounting (DD)] may be a transdiagnostic process and treatment target underlying behavioral health outcomes, including trauma, depression, anxiety, and problematic substance use. However, multiple health behaviors and barriers are also related to these outcomes, including social media usage, adverse childhood experiences (ACEs), sleep quality, healthcare access, housing status, and exercise. To extend research examining DD as transdiagnostic process, we recruited a large, heterogenous sample to examine the association between DD, problematic substance use, and mental health outcomes while controlling for certain health behaviors and health barriers. Method: In a cross-sectional online survey of 3992 US residents, we administered validated measures of PTSD, depression, anxiety, and problematic alcohol, stimulant, and opioid use. Using linear or ordinal logistic models, scores for each outcome were regressed onto DD while controlling for demographics, health behaviors, and health barriers. Results: Including only DD and demographics, DD was associated with each outcome at low effect sizes (ƒ2 = .013, OR range = 1.08-1.16). Except for opioid ASSIST scores, these relationships held when controlling for social media usage, sleep, housing status, healthcare access, ACEs, physical exercise, and demographic variables (ƒ2 = .002, OR range = 1.03-1.12), increasing confidence that DD concurrently and directly relates to four of these five clinical outcomes. Discussion: These findings support the conceptualization of DD as a transdiagnostic process underlying certain psychopathologies and suggest targeting DD in co-occurring substance use disorder and/or mental health treatments may result in clinically significant outcomes.

Extra-experimental scarcity impacts hypothetical operant demand: A natural SARS-CoV-2 eperiment.

Behavioral economic demand models quantify the extent to which an organism defends its consumption of a commodity. Commodity purchase tasks permit humans a quick yet psychometrically sound approach to assessing commodity demand for various retail products. Operant behavioral economic literature suggests economy type (open vs closed) can significantly alter demand, yet this effect is largely undocumented in the commodity purchase task literature. In this study, we leveraged the market pressures for retail goods (hand lotion and sanitizer; paper towels and toilet paper; soda and water) resulting from SARS-CoV-2 into a natural experiment comparing within-subject demand across two time-points during the pandemic using a crowdsourced approach. Results suggest that hypothetical commodity purchase tasks are sensitive to extra-experimental market pressures (e.g., scarcity due to the closing of economies), adding additional confidence to the self-report nature of purchase task responding and providing further construct validity to these approaches.

Interest in and perceived effectiveness of contingency management among alcohol drinkers using behavioral economic purchase tasks.

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).

A tribute to Howard Rachlin and his two-parameter discounting model: Reliable and flexible model fitting.

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.

Notes on demand: Conceptual and empirical benefits of applying Rachlin's discounting equation to demand data.

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).

Hyperbolic discounting and exponentiated demand: Modeling demand for cigarettes in three dimensions.

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.

Illegal Experimental Tobacco Marketplace II: effects of vaping product bans - findings from the 2020 International Tobacco Control Project.

SIGNIFICANCE: Restrictive e-cigarette policies may increase purchases from illegal sources. The Illegal Experimental Tobacco Marketplace (IETM) allows examination of how restrictions impact illegal purchases. We investigated (1) the effect of a vaping ban, total flavour vaping ban and partial flavour vaping ban on the probability of purchasing illegal vaping products among different regulatory environments (USA, Canada and England) and tobacco user types (cigarette smokers, dual users and e-cigarette users); and (2) the relation between ban endorsement and illegal purchases. METHODS: Participants (N=459) from the International Tobacco Control Survey rated their support of bans and chose to purchase from a hypothetical legal experimental tobacco marketplace or IETM under control and the three ban conditions. RESULTS: In total, 25% of cigarette smokers, 67% of dual users and 79% of e-cigarette users made IETM purchases. Cross-country comparisons depicted dual users from Canada (OR: 19.8), and e-cigarette users from the USA (OR: 12.9) exhibited higher illegal purchases odds than the same user type in England. Within-country comparisons showed e-cigarette and dual users are more likely to purchase from the IETM than cigarette smokers in the most restrictive condition, with the largest effects in e-cigarette users (England-OR: 1722.6, USA-OR: 22725.3, Canada-OR: 6125.0). Increased opposition towards partial or total flavour ban was associated with increased IETM purchasing in the corresponding condition. CONCLUSIONS: Vaping restrictions may shift users' preference to the illegal marketplace in a regulatory environment. Evidence of the IETM generalisability in a geographically dispersed sample enhances its utility in tobacco regulatory science.

Behavioral economic demand modeling chronology, complexities, and considerations: Much ado about zeros.

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.

Applied behavioral economics and public health policies: Historical precedence and translational promise.

Behavioral economics is an approach to understanding behavior though integrating behavioral psychology and microeconomic principles. Advances in behavioral economics have resulted in quick-to-administer tasks to assess discounting (i.e., decrements in the subjective value of a commodity due to delayed or probabilistic receipt) and demand (i.e., effort exerted to defend baseline consumption of a commodity amidst increasing constraints)-these tasks are built upon decades of foundational work from the experimental analysis of behavior and exhibit adequate psychometric properties. We propose that the behavioral economic approach is particularly well suited, then, for experimentally evaluating potential public policy decisions, particularly during urgent times or crises. Using examples from our collaborations (e.g., cannabis legalization, happy hour alcohol pricing, severe weather alerts, COVID-19 vaccine marketing), we demonstrate how behavioral economic approaches have rendered novel insights to guide policy development and garnered widespread attention outside of academia. We conclude with implications on multidisciplinary work and other areas in need of behavioral economic investigations.

Using crowdsourcing to study the differential effects of cross-drug withdrawal for cigarettes and opioids in a behavioral economic demand framework.

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).

Applications of operant demand to treatment selection I: Characterizing demand for evidence-based practices.

Various treatment approaches have been determined efficacious for improving child behavior outcomes. Despite a variety of evidence-based options, consumers often disregard empirically supported treatments to pursue alternatives that lack empirical support, such as 'fad' therapies. The choice to pursue therapies lacking empirical support has been considered a 'gamble' on therapeutic outcomes and this form of risky choice has historically been explained using various cognitive heuristics and biases. This report translates quantitative analyses from the Operant Demand Framework to characterize how caregivers of children with behavioral issues consume treatment services. The Operant Demand Framework is presented, its utility for characterizing patterns of treatment consumption is discussed, and a preliminary application of cross-price analyses of demand is performed to illustrate how various factors jointly influence treatment-related choice. Results indicated that caregivers endorsing interest in receiving behavioral parent training regularly pursued pseudoscientific alternatives as a functional substitute for an established therapy, despite explicit language stating a lack of evidence. These findings question the presumption of rationality in models of treatment choice and degree to which scientific evidence influences the consumption of therapies. This report concludes with a discussion of Consumer Behavior Analysis and how quantitative analyses of behavior can be used to better understand factors that enhance or detract from the dissemination of evidence-based practices.

Blood Nicotine Predicts the Behavioral Economic Abuse Liability of Reduced-Nicotine Cigarettes.

BACKGROUND: Cigarette smoking continues to be a major health concern and remains the leading preventable cause of death in the US. Recent efforts have been made to determine the potential health and policy benefits of reducing nicotine in combustible cigarettes. The degree to which changes in blood nicotine relate to measures of the abuse liability of reduced-nicotine cigarettes is unknown. The current study examined the relation between blood nicotine and behavioral economic demand measures of cigarettes differing in nicotine content. METHODS: Using a within-subject design, participants smoked a single cigarette during each experimental session. Cigarettes included the participant's usual-brand cigarette and SPECTRUM investigational cigarette differing in nicotine level (mg of nicotine to g of tobacco; 15.8 mg/g, 5.2 mg/g, 2.4 mg/g, 1.3 mg/g, and 0.4 mg/g). During each session, blood was collected at multiple timepoints and behavioral economic demand was assessed. Nonlinear mixed-effects models were used to estimate differences in derived intensity (Q0) and change in elasticity (α). RESULTS: Measures of blood nicotine decreased in an orderly fashion related to nicotine level and significantly predicted change in elasticity (α), but not derived intensity. No differences in demand parameters between the usual brand and 15.8mg/g cigarettes were observed. However, α was significantly higher (lower valuation) for 0.4mg/g than 15.8mg/g cigarettes. CONCLUSIONS: The lowest nicotine level (0.4mg/g) corresponded with the lowest abuse liability (α) compared to the full-strength control (15.8mg/g), with the 1.3mg/g level also resulting in low abuse liability. IMPLICATIONS: This is the first study examining the relative contributions of nicotine content in cigarettes and blood nicotine levels on the behavioral economic demand abuse liability of cigarettes ranging in nicotine content. Our results suggest blood nicotine and nicotine content both predict behavioral economic demand abuse liability. In addition, our results suggest a nicotine content of 1.3mg/g or lower may be effective at reducing cigarette uptake among first-time (naïve) smokers. Our results largely conform to previous findings suggesting a very low nicotine content cigarette maintains lower abuse liability than full-strength cigarettes.

Applying Mixed-Effects Modeling to Behavioral Economic Demand: An Introduction.

Behavioral economic demand methodology is increasingly being used in various fields such as substance use and consumer behavior analysis. Traditional analytical techniques to fitting demand data have proven useful yet some of these approaches require preprocessing of data, ignore dependence in the data, and present statistical limitations. We term these approaches "fit to group" and "two stage" with the former interested in group or population level estimates and the latter interested in individual subject estimates. As an extension to these regression techniques, mixed-effect (or multilevel) modeling can serve as an improvement over these traditional methods. Notable benefits include providing simultaneous group (i.e., population) level estimates (with more accurate standard errors) and individual level predictions while accommodating the inclusion of "nonsystematic" response sets and covariates. These models can also accommodate complex experimental designs including repeated measures. The goal of this article is to introduce and provide a high-level overview of mixed-effects modeling techniques applied to behavioral economic demand data. We compare and contrast results from traditional techniques to that of the mixed-effects models across two datasets differing in species and experimental design. We discuss the relative benefits and drawbacks of these approaches and provide access to statistical code and data to support the analytical replicability of the comparisons. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40614-021-00299-7.

Remotely administered incentive-based treatment for alcohol use disorder with participant-funded incentives is effective but less accessible to low-income participants.

The delivery of monetary incentives contingent on verified abstinence is an effective treatment for alcohol use disorder. However, incentive cost has often been cited as a barrier to delivering this type of treatment. In the present randomized parallel groups trial, we systematically replicated a previous trial we conducted that employed remote alcohol monitoring and incentive delivery to promote abstinence from alcohol, but with the additional requirement for participants to partially self-fund their abstinence incentives. Treatment-seeking participants with alcohol use disorder (n = 92) who met inclusion criteria (n = 36) were randomized to either a Contingent or Noncontingent group (n = 18 each). Those not meeting inclusion criteria included 15 participants who agreed to the deposit requirement but failed to make the deposit payment. The Contingent group received nearly immediate monetary incentives each day they remotely provided negative breathalyzer samples. The Noncontingent group received matched incentives each day they successfully provided samples independent of alcohol content. Days abstinent in the Contingent group were 86%, which was significantly higher than the 44% recorded in the Noncontingent group, corresponding to an odds ratio of 8.2. Exploratory analyses revealed that the deposit requirement prevented participation in those with lower incomes and those with greater alcohol use. These results support the efficacy of this remotely deliverable alcohol abstinence reinforcement incentive intervention with a deposit requirement. However, the requirement to provide a monetary deposit to self-fund abstinence incentives may prevent those with greater alcohol use and/or those experiencing extreme poverty from participating in the intervention. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The Illegal Experimental Tobacco Marketplace I: Effects of Vaping Product Bans.

OBJECTIVES: Banning vaping products may have unintended outcomes, such as increased demand for illegal products. This study experimentally examined the effects of a vaping ban and a flavored vaping ban on the probability of purchasing illicit vaping products, and factors affecting purchasing from a hypothetical illegal marketplace. METHODS: A crowdsourced sample of exclusive cigarette smokers, exclusive e-cigarette users, and frequent dual users (n = 150) completed hypothetical purchasing trials in an Experimental Tobacco Marketplace under three conditions (no ban, vaping ban, and flavored vaping ban). Participants chose to purchase in a hypothetical legal experimental tobacco marketplace (LETM) or illegal experimental tobacco marketplace (IETM). Vaping products were available in each marketplace depending on the condition. Other tobacco products were always available in the LETM. A hypothetical illicit purchase task with five fine amounts assessed the effect of monetary penalties. RESULTS: Participants from all groups were more likely to purchase from the IETM when product availability in the LETM was more restricted, with e-cigarette users being most affected. The likelihood of purchasing illegal products was systematically decreased as monetary penalties associated with the IETM increased, with e-cigarette users showing greater persistence in defending their illicit purchases. CONCLUSIONS: Restricting vaping products from the marketplace may shift preference towards purchasing vaping products in the illegal marketplace. Nevertheless, penalties imposed on consumer's behavior might be effective in preventing illicit trade. The IETM is a methodological extension that supports the utility and flexibility of the ETM as a framework for understanding the impact of different tobacco regulatory policies. IMPLICATIONS: This study suggests that limiting or banning vaping products as a possible strategy to reduce the adverse effects of vaping products could result in some tobacco-users seeking banned products from illegal sources. Monetary fines were shown to reduce illegal purchases. Therefore, policymakers should consider implementing strategies that may mitigate illegal purchases.

A zero-bounded model of operant demand.

Contemporary approaches for evaluating the demand for reinforcers use either the Exponential or the Exponentiated model of operant demand, both derived from the framework of Hursh and Silberberg (2008). This report summarizes the strengths and complications of this framework and proposes a novel implementation. This novel implementation incorporates earlier strengths and resolves existing shortcomings that are due to the use of a logarithmic scale for consumption. The Inverse Hyperbolic Sine (IHS) transformation is reviewed and evaluated as a replacement for the logarithmic scale in models of operant demand. Modeling consumption in the "log10 -like" IHS scale reflects relative changes in consumption (as with a log scale) and accommodates a true zero bound (i.e., zero consumption values). The presence of a zero bound obviates the need for a separate span parameter (i.e., k) and the span of the model may be more simply defined by maximum demand at zero price (i.e., Q0 ). Further, this reformulated model serves to decouple the exponential rate constant (i.e., α) from variations in span, thus normalizing the rate constant to the span of consumption in IHS units and permitting comparisons when spans vary. This model, called the Zero-bounded Exponential (ZBE), is evaluated using simulated and real-world data. The direct reinstatement ZBE model showed strong correspondence with empirical indicators of demand and with a normalization of α (ZBEn) across empirical data that varied in reinforcing efficacy (dose, time to onset of peak effects). Future directions in demand curve analysis are discussed with recommendations for additional replication and exploration of scales beyond the logarithm when accommodating zero consumption data.

Effects of Reduced-Nicotine Cigarettes Across Regulatory Environments in the Experimental Tobacco Marketplace: A Randomized Trial.

INTRODUCTION: Cigarette smoking remains the leading preventable cause of death in the United States. Recent efforts have explored the potential health and policy benefits of reducing nicotine, an addictive component, in combustible cigarettes. To date, an experimental, prospective analysis directly comparing the effects of varying regulatory environments on purchases of multiple products has yet to be conducted. The present study compared real purchasing of conventional cigarettes, reduced-nicotine cigarettes, and a variety of other nicotine and tobacco products across a range of regulatory environments. METHODS: Participants were assigned to one of five groups, each associated with a different nicotine level (mg of nicotine to g of tobacco) in SPECTRUM investigational cigarettes (15.8, 5.2, 2.4, 1.3, and 0.4 mg/g). Across sessions, participants made real purchases for nicotine/tobacco products in an Experimental Tobacco Marketplace. Each session corresponded with a distinct regulatory environment wherein different nicotine/tobacco products were available for purchase. RESULTS: Our results suggest that the primary drivers of cigarette and nicotine purchasing are regulatory environment and the presence/absence of alternative nicotine and tobacco products. Perhaps surprisingly, nicotine level does not appear to be such a driver of purchasing behavior under these experimental conditions. Investigational cigarette purchasing is lowest when other preferred combustible products are available and highest when investigational cigarettes are the only combustible product available for purchase. CONCLUSIONS: If a reduced-nicotine policy is implemented, great care should be taken in determining and making available less-harmful nicotine/tobacco products as the availability of preferred combustible products may result in undesirable levels of purchasing. IMPLICATIONS: This is the first experimental study investigating different potential regulatory effects related to a reduced-nicotine policy by examining purchasing across a range of nicotine/tobacco products. Our results suggest the presence of affordable, highly preferred combustible products is likely to maintain tobacco purchasing at undesirable levels. To promote switching to less-harmful products, affordable alternate nicotine and tobacco products should be readily available. Finally, our results suggest that the availability of noncigarette products, not cigarette nicotine level, will most likely affect purchasing of reduced-nicotine cigarettes.

Interpretation(s) of elasticity in operant demand.

This brief report provides an account of varying interpretations of elasticity (η) in the operant demand framework. General references to "demand elasticity" have existed since the Exponential model of operant demand was proposed by Hursh and Silberberg (2008). This term has been used interchangeably with Essential Value (EV), PMAX , and the rate of change constant α. This report provides an in-depth account of η and the various ways in which this metric has been used to interpret fitted demand functions. A review of relevant mathematic terms, operations associated with differentiating parameters, and worked solutions for η are provided for linear and nonlinear demand functions. The relations between η and EV, PMAX , and α are described and explained in terms of their mathematical bases and recommendations are provided regarding their individual interpretation. This report concludes with recommendations for providing additional mathematical detail in published works and emphasizing a consistent use of terms when describing aspects of operant demand.

Reinforcer Pathology: Implications for Substance Abuse Intervention.

The rate at which individuals discount future rewards (i.e., discounting rate) is strongly associated with their propensity for substance abuse as well as myriad other negative health behaviors. An excessive preference for immediately available rewards suggests a shortened time horizon in which immediate rewards are overvalued and future, potentially negative consequences are undervalued. This review outlines Reinforcer Pathology Theory (i.e., the interaction between excessive preference for immediately available rewards and the overvaluation of a particular commodity that offers brief, intense reinforcement), its neurobiological/behavioral underpinnings, and its implications for treating substance use disorders. In doing so, the current review provides an overview of a variety of ways in which interventions have been used to manipulate aspects of reinforcer pathology in an individual, including narrative theory, framing manipulations, and neuromodulation (e.g., working memory training, TMS) which may serve as promising avenues for the modulation of the temporal window and/or valuation of reinforcers.

Modeling Treatment-Related Decision-Making Using Applied Behavioral Economics: Caregiver Perspectives in Temporally-Extended Behavioral Treatments.

Evidence-based behavioral therapies for children with disruptive and challenging behavior rarely yield immediate improvements in behavior. For caregivers participating in behavioral therapies, the benefits from these efforts are seldom visible until after substantial time commitments. Delays associated with relief from challenging behavior (i.e., improved behavior) can influence how caregivers decide to respond to instances of problem behavior, and in turn, their continued commitment (i.e., integrity, adherence) to treatments that require long-term implementation to produce improvements in child behavior. This study applied delay discounting methods to evaluate how delays affected caregiver preferences related to options for managing their child's behavior. Specifically, methods were designed to evaluate the degree to which caregiver preferences for a more efficacious, recommended approach was affected by delays (i.e., numbers of weeks in treatment). That is, methods evaluated at which point caregivers opted to disregard the optimal, delayed strategy and instead elected to pursue suboptimal, immediate strategies. Results indicated that caregivers regularly discounted the value of the more efficacious treatment, electing to pursue suboptimal approaches when delays associated with the optimal approach grew larger. Caregivers demonstrated similar patterns of suboptimal choice across both clinical (i.e., intervention) and non-clinical (i.e., monetary) types of decisions. These findings are consistent with research that has highlighted temporal preferences as an individual factor that may be relevant to caregiver adherence to long-term evidence-based treatments and encourage the incorporation of behavioral economic methods to better understand caregiver decision-making.