The roles of delay and probability discounting in texting while driving: Toward the development of a translational scientific program

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Discounting is the process by which outcomes lose value. Much of discounting research has focused on differences in the degree of discounting across various groups. This research has relied heavily on conventional null hypothesis significance testing that is familiar to psychology in general such as t-tests and ANOVAs. As discounting research questions have become more complex by simultaneously focusing on within-subject and between-group differences conventional statistical testing is often not appropriate for the obtained data. Generalized estimating equations (GEE) are one type of mixed-effects model that are designed to handle autocorrelated data, such as within-subject repeated-measures data, and are therefore more appropriate for discounting data. To determine if GEE provides similar results as conventional statistical tests, were compared the techniques across 2,000 simulated data sets. The data sets were created using a Monte Carlo method based of an existing data set. Across the simulated data sets, the GEE and the conventional statistical tests generally provided similar patterns of results. As the GEE and more conventional statistical tests provide the same pattern of result, we suggest researchers use the GEE because it was designed to handle data that has the structure that is typical of discounting data.

Section: Methodsmentioning

confidence: 99%

“…The data for this study are from Hayashi et al (2018). In that study, scenarios were posed to participants in which they were driving and they received a text message.…”

Section: Methods Datamentioning

confidence: 99%

Section: Analysesmentioning

confidence: 99%

mentioning

confidence: 99%

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A Monte Carlo method for comparing generalized estimating equations to conventional statistical techniques for discounting data

Friedel

DeHart

Foreman

et al. 2019

Self Cite

“…Behavioral economics, in general, has been successfully applied to various maladaptive behavioral problems, such as substance use disorders (Bickel, Johnson, Koffarnus, MacKillop, & Murphy, 2014;MacKillop, 2016). Several studies have demonstrated that behavioral economics is useful for understanding the behavior of text messaging (e.g., Hayashi & Blessington, 2018;Hayashi, Fessler, Friedel, Foreman, & Wirth, 2018;Reed, Becirevic, et al, 2016). Several studies have demonstrated that behavioral economics is useful for understanding the behavior of text messaging (e.g., Hayashi & Blessington, 2018;Hayashi, Fessler, Friedel, Foreman, & Wirth, 2018;Reed, Becirevic, et al, 2016).…”

Section: Behavioral Economic Demand Analysismentioning

confidence: 99%

A cluster analysis of text message users based on their demand for text messaging: A behavioral economic approach

Hayashi

Friedel

Foreman

et al. 2019

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The goal of this study was to determine whether cluster analysis could be used to identify distinct subgroups of text message users based on behavioral economic indices of demand for text messaging. Cluster analysis is an analytic technique that attempts to categorize cases based on similarities across selected variables. Participants completed a questionnaire about mobile phone usage and a hypothetical texting demand task in which they indicated their likelihood of paying an extra charge to continue to send text messages. A hierarchical cluster analysis was conducted on behavioral economic indices, such as demand intensity, demand elasticity, breakpoint, and the maximum expenditure. With the cluster analysis, we identified 3 subgroups of text message users. The groups were characterized by (a) high intensity and low elasticity, (b) high intensity and medium elasticity, and (c) low intensity and high elasticity. In a demonstration of convergent validity, there were statistically significant and conceptually meaningful differences across the subgroups in various measures of mobile phone use and text messaging. Cluster analysis is a useful tool for identifying and profiling distinct, practically meaningful groups based on behavioral indices and could provide a framework for targeting interventions more efficiently.

Effects of immediate‐reinforcement training on delay discounting behavior in rats

Fox¹

2021

Chronic exposure to delayed reinforcement has been shown to increase choice for larger, later reinforcement in a subsequent delay discounting task. In the three experiments presented in this paper, the opposite was tested: effects of chronic exposure to immediate reinforcement on choice in a subsequent delay discounting task. In Experiment 1, larger, later reinforcement choice was significantly reduced as a result of exposure to immediate reinforcement, compared to a maturation/handle control group, in experienced, male Lewis rats. In Experiment 2, with naive male and female Wistar rats, and Experiment 3, with naïve male Long Evans rats, the impact of exposure to immediate reinforcement was less robust, but directionally consistent with results from Experiment 1. These results align with some previous work reporting that exposure to immediate reinforcement may decrease choice for larger, later reinforcement in a delay discounting task, and/or blunt maturational increases in choice for larger, later reinforcement. These findings have implications for future research investigating experience-based interventions to manipulate delay discounting behavior. They also have clinical implications for understanding and treating disorders involving maladaptive choice.