Unique prediction of cannabis use severity and behaviors by delay discounting and behavioral economic demand

Justin C. Strickland, Joshua A. Lile, William W. Stoops

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)33-40
Number of pages8
JournalBehavioural Processes
Volume140
DOIs
StatePublished - Jul 1 2017

Bibliographical note

Publisher Copyright:
© 2017 Elsevier B.V.

Keywords

  • Behavioral economics
  • Crowdsourcing
  • Demand curve
  • Drug
  • Substance use

ASJC Scopus subject areas

  • Animal Science and Zoology
  • Behavioral Neuroscience

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