A zero-bounded model of operant demand

Consumer preferences for sleep-tracking wearables: The role of scientific evaluation and endorsement

OBJECTIVES

Accuracy and relevance to health outcomes are important to researchers and clinicians who use consumer sleep technologies, but economic demand motivates consumer sleep technology design. This report quantifies the value of scientific relevance to the general consumer in a dollar amount to convey the importance of device accuracy in terms that consumer sleep technology manufacturers can appreciate.

METHODS

Survey data were collected from 368 participants on Amazon mTurk. Participants ranked sleep metrics, evaluation methods, and scientific endorsement by perceived level of importance. Participants indicated their likelihood of purchasing a hypothetical consumer sleep technology that had either (1) not been evaluated or endorsed; (2) had been evaluated but not endorsed, and; (3) had been evaluated and endorsed by a sleep science authority. Demand curves determined the relative value of each consumer sleep technology.

RESULTS

Devices that were evaluated and endorsed had the most value, followed by those only evaluated, and then those with no evaluation. The unit price at which there was 50% probability of purchase increased by $30 or $48 for evaluation or endorsement, respectively, relative to a nonvalidated device. Respondents indicated the most valuable sleep metric was sleep duration, the most important evaluation method was against laboratory/hospital standards for sleep, and that the highest value of endorsement came from a medical institution.

CONCLUSIONS

Consumer demand is greatest for a device that has been evaluated by an independent laboratory and is endorsed by a medical institution. Consumer sleep technology manufacturers may be able to increase sales by partnering with sleep science authorities to produce a scientifically superior device.

Applying behavioral economics-based approaches to examine the effects of liquid sucrose consumption on motivation

Overconsumption of sugar contributes to obesity in part by changing the activity of brain areas that drive the motivation to seek out and consume food. Sugar-sweetened beverages are the most common source of excess dietary sugar and contribute to weight gain. However, very few studies have assessed the effects of liquid sucrose consumption on motivation. This is due in part to the need for novel approaches to assess motivation in pre-clinical models. To address this, we developed a within-session behavioral economics procedure to assess motivation for liquid sucrose. We first established and validated the procedure: we tested several sucrose concentrations, evaluated sensitivity of the procedure to satiety, and optimized several testing parameters. We then applied this new procedure to determine how intermittent vs. continuous access to liquid sucrose (1 M) in the home cage affects sucrose motivation. We found that intermittent liquid sucrose access results in an escalation of sucrose intake in the home cage, without altering motivation for liquid sucrose during demand testing (1 M or 0.25 M) compared to water-maintained controls. In contrast, continuous home cage access selectively blunted motivation for 1 M sucrose, while motivation for 0.25 M sucrose was similar to intermittent sucrose and control groups. Thus, effects of continuous home cage liquid sucrose access were selective to the familiar sucrose concentration. Finally, effects of sucrose on motivation recovered after removal of liquid sucrose from the diet. These data provide a new approach to examine motivation for liquid sucrose and show that escalation of intake and motivation for sucrose are dissociable processes.

Social familiarity and reinforcement value: a behavioral-economic analysis of demand for social interaction with cagemate and non-cagemate female rats

Rats were studied in social reinforcement procedures in which lever presses opened a door separating two adjacent spaces, permitting access to social interaction with a partner rat. The number of lever presses required for social interaction was systematically increased across blocks of sessions according to fixed-ratio schedules, generating demand functions at three different social reinforcement durations: 10 s, 30 s, and 60 s. The social partner rats were cagemates in one phase, and non-cagemates in a second phase. The rate at which social interactions were produced declined with the fixed-ratio price, and was well described by an exponential model that has been successfully employed with a range of social and non-social reinforcers. None of the main parameters of the model varied systematically with social interaction duration or with the social familiarity of the partner rat. On the whole, the results provide further evidence of the reinforcing value of social interaction, and its functional parallels with non-social reinforcers.

Interpretation(s) of essential value in operant demand

The operant demand framework has achieved high levels of adoption as an approach to quantify how various ecological factors influence choice. A central goal of the framework proposed by Hursh and Silberburg (2008) was to isolate the "essential value" of reinforcers-namely, their effects on behavior given various contextual factors. The effect of reinforcers on behavior is a phenomenon that is expected to vary as a function of reinforcer magnitude/dosage (i.e., units of reinforcement), price (i.e., schedule requirements), the intensity of demand (i.e., consumption in free operant conditions), the availability of reinforcers (i.e., supply, presence of alternatives), and the individual's current and historical context. This technical report provides a historical summary of the concept, describes the quantitative basis for essential value in the framework of Hursh and Silberburg (2008), reviews prior attempts to extract a generalizable index of essential value, and presents a newer formulation using exact solution that provides a more succinct and durable index. Proofs and solutions are provided to clarify the bases for novel and existing representations of essential value. Recommendations are provided to improve the precision and accuracy of behavioral economic metrics as well as support consensus regarding their interpretation in the operant demand framework.

Anhedonia modulates benzodiazepine and opioid demand among persons in treatment for opioid use disorder

Background

Benzodiazepine (BZD) misuse is a significant public health problem, particularly in conjunction with opioid use, due to increased risks of overdose and death. One putative mechanism underlying BZD misuse is affective dysregulation, via exaggerated negative affect (e.g., anxiety, depression, stress-reactivity) and/or impaired positive affect (anhedonia). Similar to other misused substances, BZD consumption is sensitive to price and individual differences. Although purchase tasks and demand curve analysis can shed light on determinants of substance use, few studies have examined BZD demand, nor factors related to demand.

Methods

This ongoing study is examining simulated economic demand for alprazolam (among BZD lifetime misusers based on self-report and DSM-5 diagnosis; n = 23 total; 14 male, 9 female) and each participant's preferred-opioid/route using hypothetical purchase tasks among patients with opioid use disorder (n = 59 total; 38 male, 21 female) who are not clinically stable, i.e., defined as being early in treatment or in treatment longer but with recent substance use. Aims are to determine whether: (1) BZD misusers differ from never-misusers on preferred-opioid economic demand, affective dysregulation (using questionnaire and performance measures), insomnia/behavioral alertness, psychiatric diagnoses or medications, or urinalysis results; and (2) alprazolam demand among BZD misusers is related to affective dysregulation or other measures.

Results

Lifetime BZD misuse is significantly (p < 0.05) related to current major depressive disorder diagnosis, opioid-negative and methadone-negative urinalysis, higher trait anxiety, greater self-reported affective dysregulation, and younger age, but not preferred-opioid demand or insomnia/behavioral alertness. Alprazolam and opioid demand are each significantly positively related to higher anhedonia and, to a lesser extent, depression symptoms but no other measures of negative-affective dysregulation, psychiatric conditions or medications (including opioid agonist therapy or inpatient/outpatient treatment modality), or sleep-related problems.

Conclusion

Anhedonia (positive-affective deficit) robustly predicted increased BZD and opioid demand; these factors could modulate treatment response. Routine assessment and effective treatment of anhedonia in populations with concurrent opioid and sedative use disorder may improve treatment outcomes.

Clinical trial registration

https://clinicaltrials.gov/ct2/show/NCT03696017, identifier NCT03696017.

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.

Time Cost and Demand: Implications for Public Policy

The success of policy involves not only good design but a good understanding of how the public will respond behaviorally to the benefits or detriments of that policy. Behavioral science has greatly contributed to how we understand the impact of monetary costs on behavior and has therefore contributed to policy design. Consumption taxes are a direct result of this; for example, cigarette taxes that aim to reduce cigarette consumption. In addition to monetary costs, time may also be conceptualized as a constraint on consumption. Time costs may therefore have policy implications, for example, long waiting times could deter people from accessing certain benefits. Recent data show that behavioral economic demand curve methods used to understand monetary cost may also be used to understand time costs. In this article we discuss how the impact of time cost can be conceptualized as a constraint on demand for public benefits utilization and public health when there are delays to receiving the benefits. Policy examples in which time costs may be relevant and demand curve methods may be useful are discussed in the areas of government benefits, public health, and transportation design.

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.

A behavioral economic demand analysis of mothers' decision to exclusively breastfeed in the workplace

The present study determined whether behavioral economic demand analysis could characterize mothers' decision to exclusively breastfeed in the workplace. Females, aged between 18 and 50 who have given birth in the past three years, completed a novel demand task with hypothetical scenarios, in which they returned to work with a 2-month-old baby. Participants rated their likelihood of breastfeeding their baby at a workplace lactation room versus formula-feeding their baby at their desk. The distance to the lactation room ranged from 10 s to 60 min. This assessment was conducted with and without hypothetical financial incentives for 6-month exclusive breastfeeding. Primary dependent measures were demand intensity and change in demand elasticity, which could conceptually represent initiation and continuation of breastfeeding, respectively. Demand for breastfeeding was more intense and less elastic (i.e., more likely to initiate and continue breastfeeding) among mothers with an experience of 6-month exclusive breastfeeding and under the condition with the financial incentives. The novel demand task can potentially provide a useful behavioral marker for quantifying mothers' decision to initiate and continue exclusive breastfeeding in the workplace, informing workplace policy regarding lactation rooms, identifying risk for early cessation, and developing and individualizing an intervention to assist mothers to exclusively breastfeed in the workplace.

Applications of operant demand to treatment selection III: Consumer behavior analysis of treatment choice

Behavior analysts and psychologists advocate for the use of therapies and strategies based on credible, scientific evidence. Researchers and clinicians regularly advocate for Evidence-based Practices (EBPs) over questionable "alternatives" because caregivers seldom choose interventions based on scientific evidence alone. This study applied methods and concepts from Consumer Behavior Analysis to conduct a reinforcer-based evaluation of the consequences that influence treatment choices. Hypothetical Treatment Purchase Tasks (HTPTs) were designed to evaluate how utilitarian (UR; i.e., the efficacy of treatment) and informational sources of reinforcement (IR; i.e., community support for treatment) jointly influence treatment-related choices. A total of 104 caregivers were recruited using the Amazon Mechanical Turk (MTurk) framework to complete two HTPTs. Results indicated that caregivers overall favored treatments with greater IR over those with greater UR, suggesting that indirect contingencies for treatment choices exerted greater overall influence than the direct contingencies of treatment choices (i.e., efficacy). This finding extends the literature on treatment choice by providing a reinforcer-based perspective on why 'fad', questionable, and pseudoscientific practices can achieve and maintain high levels of adoption by caregivers. This work concludes with a discussion of Consumer Behavior Analysis and how reinforcer-based interpretations of choice can be used to improve efforts to support and advocate for evidence-based child behavior treatments.

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.

Applications of operant demand to treatment selection II: Covariance of evidence strength and treatment consumption

Evidence-based practices (EBPs) are a critical component of effective and ethical service delivery. Clinicians in the behavioral sciences regularly advocate for the use of therapies and interventions based on the strength and breadth of scientific evidence. However, caregiver choices related to specific behavior therapies are seldom based solely on the degree (or the presence) of scientific evidence. This study applied methods from the Operant Demand Framework to characterize caregiver choices when concurrently available behavior therapies varied in terms of unit price and levels of evidence. Four Hypothetical Treatment Purchase Tasks were designed to evaluate how relative differences in scientific evidence between behavior therapies influenced the demand for, and substitutability of, EBPs. Results from 106 caregivers recruited from the Amazon Mechanical Turk platform indicated that low-, moderate-, and high-evidence treatment choices all functioned as substitutes for a high-evidence (i.e., well-established) behavior therapy. A main effect was observed for the level of evidence, whereby the strength of evidence appeared to moderate the degree to which respective treatments functioned as substitutes. These results extend the literature on the factors associated with treatment choices, and specifically, highlight how differences in the degree of scientific evidence influence choice when deciding between behavior therapies. These results are discussed in the context of more effectively advocating for the use of EBPs with mainstream and lay audiences.

Hidden equivalence in the operant demand framework: A review and evaluation of multiple methods for evaluating nonconsumption

Operant translations of behavioral economic concepts and principles have enhanced the ability of researchers to characterize the effects of reinforcers on behavior. Operant behavioral economic models of choice (i.e., Operant Demand) have been particularly useful in evaluating how the consumption of reinforcers is affected by various ecological factors (e.g., price, limited resources). Prevailing perspectives in the Operant Demand Framework are derived from the framework presented in Hursh and Silberberg (2008). Few dispute the utility of this framework and model, though debate continues regarding how to address the challenges associated with logarithmic scaling. At present, there are competing views regarding the handling of nonconsumption (i.e., 0 consumption values) and under which situations that alternative restatements of this framework are recommended. The purpose of this report was to review the shared mathematical bases for the Hursh and Silberberg and Koffarnus et al. (2015) models and how each can accommodate nonconsumption values. Simulations derived from those featured in Koffarnus et al. were used to conduct tests of equivalence between modeling strategies while controlling for interpretations of residual error as well as the absolute lower limit. Simulations and proofs were provided to illustrate how neither the Hursh and Silberberg nor Koffarnus et al. models can characterize demand at 0 and how both ultimately arrive at the same upper and lower limits. These findings are discussed, and recommendations are provided to build consensus related to zero consumption values in the Operant Demand Framework.

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.