Strickland Justin C, Lile Joshua A, Stoops William W
Department of Psychology, University of Kentucky College of Arts and Sciences, 171 Funkhouser Drive, Lexington, KY 40506-0044, USA.
Department of Psychology, University of Kentucky College of Arts and Sciences, 171 Funkhouser Drive, Lexington, KY 40506-0044, USA; Department of Behavioral Science, University of Kentucky College of Medicine, 1100 Veterans Drive, Medical Behavioral Science Building Room 140, Lexington, KY 40536-0086, USA; Department of Psychiatry, University of Kentucky College of Medicine, 3470 Blazer Parkway, Lexington, KY 40509-1810, USA.
Behav Processes. 2017 Jul;140:33-40. doi: 10.1016/j.beproc.2017.03.017. Epub 2017 Mar 24.
Few studies have simultaneously evaluated delay discounting and behavioral economic demand to determine their unique contribution to drug use. A recent study in cannabis users found that monetary delay discounting uniquely predicted cannabis dependence symptoms, whereas cannabis demand uniquely predicted use frequency. This study sought to replicate and extend this research by evaluating delay discounting and behavioral economic demand measures for multiple commodities and including a use quantity measure. Amazon.com's Mechanical Turk was used to sample individuals reporting recent cannabis use (n=64) and controls (n=72). Participants completed measures of monetary delay discounting as well as alcohol and cannabis delay discounting and demand. Cannabis users and controls did not differ on monetary delay discounting or alcohol delay discounting and demand. Among cannabis users, regression analyses indicated that cannabis delay discounting uniquely predicted use severity, whereas cannabis demand uniquely predicted use frequency and quantity. These effects remained significant after controlling for other delay discounting and demand measures. This research replicates previous outcomes relating delay discounting and demand with cannabis use and extends them by accounting for the contribution of multiple commodities. This research also demonstrates the ability of online crowdsourcing methods to complement traditional human laboratory techniques.
很少有研究同时评估延迟折扣和行为经济需求，以确定它们对药物使用的独特贡献。最近一项针对大麻使用者的研究发现，货币延迟折扣独特地预测了大麻依赖症状，而大麻需求独特地预测了使用频率。本研究试图通过评估多种商品的延迟折扣和行为经济需求指标，并纳入使用量指标，来重复和扩展这项研究。利用亚马逊的土耳其机器人服务从报告近期使用过大麻的个体（n = 64）和对照组（n = 72）中进行抽样。参与者完成了货币延迟折扣以及酒精和大麻延迟折扣与需求的测量。大麻使用者和对照组在货币延迟折扣或酒精延迟折扣与需求方面没有差异。在大麻使用者中，回归分析表明，大麻延迟折扣独特地预测了使用严重程度，而大麻需求独特地预测了使用频率和使用量。在控制了其他延迟折扣和需求测量指标后，这些效应仍然显著。本研究重复了先前关于延迟折扣和需求与大麻使用之间关系的研究结果，并通过考虑多种商品的贡献对其进行了扩展。本研究还证明了在线众包方法补充传统人体实验室技术的能力。
