Examination of a recommended algorithm for eliminating nonsystematic delay discounting response sets

The relationship between nonsystematic delay discounting and low-quality survey responses in a sample of smokers: ROC curve analysis

Delay discounting (DD), the decrease of the subjective value of a reward as the delay to its receipt increases, is a crucial aspect of decision-making processes. As evidence continues to mount, additional attention needs to be given to nonsystematic DD, a response pattern that has been reported in the literature but rarely investigated. We noticed in our recent online research an increase in the proportion of nonsystematic DD responses across samples, consistent with the so-called Amazon Mechanical Turk (MTurk) data quality crisis. The significant proportion of nonsystematic responses created an opportunity to investigate its association with data quality in the present study. In a sample of smokers recruited from MTurk (n = 210), three independent quality check indexes evaluated participants' response quality. The degree of nonsystematic DD was quantified by the algorithms developed by Johnson and Bickel (2008). The area under the receiver operating characteristic curve (AUC) predicting response quality by nonsystematic DD was obtained. The observed AUC values were at the extreme of the null distributions (ps < .001) in a permutation test. Furthermore, the nonsystematic DD cutoffs provided in Johnson and Bickel (2008) showed good sensitivity (0.77-0.93), albeit low-moderate specificity (0.42-0.74), in detecting low-quality responses. The findings showed that nonsystematic DD was associated with low-quality responses, although other factors contributing to the nonsystematic responses remain to be identified. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A review of tobacco regulatory science research on vulnerable populations

In 2013 the U.S. Food and Drug Administration and National Institutes of Health established fourteen Tobacco Centers of Regulatory Science (TCORS) to advance scientific knowledge relevant to conducting evidence-based tobacco regulation. This report reviews TCORS-funded research with adult vulnerable populations. The literature search included a list of all TCORS-funded publications compiled by the TCORS coordinating center; all TCORS were requested to share publications not in the coordinating-center's list. Only TCORS-funded reports describing an empirical study with an adult vulnerable population published in a peer-reviewed journal between September 2013 and June 2018 were included. 71 reports met inclusion criteria; 39% (28/71) examined tobacco use among those with mental health and medical comorbidities, 34% (24/71) socioeconomic disadvantage, 31% (22/71) women of reproductive age, 30% (21/71) racial/ethnic minorities, 18% (13/71) rural residents, and 3% (2/71) each among active military/veterans and sexual/gender minorities. Regarding scientific domains, 63% (45/71) investigated behavior, 37% (26/71) addiction, 24% (17/71) health effects, 20% (14/71) impact analyses, 18% (13/71) toxicity, 8% (6/71) marketing influences, and 7% (5/71) communications. Totals exceed 100% because some reports addressed multiple populations/domains. TCORS funding has generated a substantial, multidisciplinary body of new scientific knowledge on tobacco use in adult vulnerable populations. However, considerable variability was noted in the amount of research conducted across the various vulnerable populations and scientific domains. Most notably, relatively few studies focused on active military/veterans or sexual/gender minorities, and the scientific domains of marketing influences and communications were conspicuously underrepresented. These are important knowledge gaps to address going forward.

Differences in Delay, but not Probability Discounting, in Current Smokers, E-cigarette Users, and Never Smokers

Steeper delay discounting in substance abuse populations, compared to non-abusing populations, has been well-established in prior studies. Despite the growing interest in e-cigarettes as a novel and relatively understudied form of nicotine consumption, relatively little is known as to how e-cigarette users discount rewards compared to traditional cigarette smokers and never smokers. In the present study, we measured delay and probability discounting rates, as well as perceived risk inherent to a delayed reward, in current traditional cigarette smokers, e-cigarette users, and never smokers. We found that traditional cigarette smokers and e-cigarette users discounted delayed rewards at a similar rate—and both were steeper than never smokers. However, no differences were observed in probability discounting or in perceived risk inherent in reward delay.

Effects of delay and probability combinations on discounting in humans

To determine discount rates, researchers typically adjust the amount of an immediate or certain option relative to a delayed or uncertain option. Because this adjusting amount method can be relatively time consuming, researchers have developed more efficient procedures. One such procedure is a 5-trial adjusting delay procedure, which measures the delay at which an amount of money loses half of its value (e.g., $1000 is valued at $500 with a 10-year delay to its receipt). Experiment 1 (n=212) used 5-trial adjusting delay or probability tasks to measure delay discounting of losses, probabilistic gains, and probabilistic losses. Experiment 2 (n=98) assessed combined probabilistic and delayed alternatives. In both experiments, we compared results from 5-trial adjusting delay or probability tasks to traditional adjusting amount procedures. Results suggest both procedures produced similar rates of probability and delay discounting in six out of seven comparisons. A magnitude effect consistent with previous research was observed for probabilistic gains and losses, but not for delayed losses. Results also suggest that delay and probability interact to determine the value of money. Five-trial methods may allow researchers to assess discounting more efficiently as well as study more complex choice scenarios.