Login / Signup

Persisting on the past: Cross-sectional and prospective associations between sunk cost propensity and cannabis use.

Michael J SofisShea M LemleyAlan J BudneyCatherine StangerDavid P Jarmolowicz
Published in: Experimental and clinical psychopharmacology (2019)
Prevalence of cannabis use in the United States continues to rise, and 30% of cannabis users eventually meet criteria for Cannabis Use Disorder (CUD). One response to this problem is to develop decision-making constructs that indicate vulnerability to CUD that might not be gleaned from diagnostic criteria. Unfortunately, there is limited evidence that decision-making constructs consistently relate to cannabis use. Interestingly, those who exhibit the sunk cost bias, an overgeneralized tendency to persist based on past investment, and those who use cannabis, both tend to focus on the past and perseverate more than their counterparts. Despite this overlap, no studies have assessed whether the sunk cost bias is positively associated with cannabis use. In 2 experiments with undergraduates, relations between cannabis use and the propensity to engage in the sunk cost bias were examined using negative binomial models. Experiment 1 (n = 46) evaluated the association between sunk cost bias propensity (using hypothetical costs and rewards) and frequency of cannabis use over the past 30 days. Greater sunk cost propensity was associated with more frequent cannabis use after controlling for demographics and alcohol use. In Experiment 2 (n = 103), more frequent cannabis use during a 6-week follow-up period was predicted by greater sunk cost propensity at baseline (using a real cost and reward-based task), independently and after controlling for mental health symptoms, alcohol use, and demographics. These findings provide preliminary evidence that a propensity to exhibit the sunk cost bias may be an important feature associated with cannabis use. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Keyphrases
  • mental health
  • decision making
  • cross sectional
  • machine learning
  • randomized controlled trial
  • clinical trial
  • depressive symptoms
  • risk factors
  • mental illness
  • adverse drug
  • sleep quality
  • drug induced