ArticleDrug and alcohol dependence2014
Cocaine behavioral economics: from the naturalistic environment to the controlled laboratory setting.
Article in Drug and alcohol dependence, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 13 citations in OpenAlex.
- N-acetylcysteine reduces cocaine-seeking behavior and anterior cingulate glutamate/glutamine levels among cocaine-dependent individuals.Addiction biology · 2021Trial
- Using behavioral economics to predict opioid use during prescription opioid dependence treatment.Drug and alcohol dependence · 2015Trial
- Repeated measures latent class analysis identifies unique daily cocaine-use patterns and associated clinical characteristics: A timeline followback-based analytic approach.Drug and alcohol dependence reports · 2026Article
- Environmental Uncertainty and Substance Use Disorders: A Behavior Analytic Perspective.Policy insights from the behavioral and brain sciences · 2023Article
- Choice between food and cocaine reinforcers under fixed and variable schedules in female and male rhesus monkeys.Experimental and clinical psychopharmacology · 2023Article
- Applied Quantitative Analysis of Behavior: What It Is, and Why We Care-Introduction to the Special Section.Perspectives on behavior science · 2021Article
- Demand curve analysis of marijuana use among persons living with HIV.Drug and alcohol dependence · 2021Article
- Unpredictability as a modulator of drug self-administration: Relevance for substance-use disorders.Behavioural processes · 2020Review
- Prediction of attempted suicide in men and women with crack-cocaine use disorder in Brazil.PloS one · 2020Article
- Further validation of a marijuana purchase task.Drug and alcohol dependence · 2015Article
- Use of Preclinical Drug vs. Food Choice Procedures to Evaluate Candidate Medications for Cocaine Addiction.Current treatment options in psychiatry · 2015Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundWe previously observed that behavioral economic factors predict naturalistic heroin seeking behavior that correlates with opioid seeking in the experimental laboratory. The present study sought to replicate and extend these prior findings with regular cocaine users.
methodsParticipants (N=83) completed a semi-structured interview to establish income-generating and cocaine-purchasing/use repertoire during the past month. Questions addressed sources/amounts of income and expenditures; price (money and time) per purchase; and frequency/amounts of cocaine purchased and consumed. Naturalistic cocaine purchasing and use patterns were: (1) analyzed as a function of income quartile, (2) perturbed by hypothetical changes in cost factors to assess changes in purchasing/use habits, and (3) correlated with experimental cocaine seeking.
resultsIncome was positively related to naturalistic cocaine seeking/use pattern (i.e., income elastic), and behaviors were cost-efficient and sensitive to supply chain. Income was unrelated to proportional expenditure on cocaine (≈55%) but inversely related to food expenditure. In all hypothetical scenarios (changes in income or dealer, loss of income assistance from government or family/friends, and increasing arrest risk when purchasing), the high-income group reported they would continue to use more cocaine daily than other groups. Number of laboratory cocaine choices significantly correlated with cocaine purchase time (positively) and purity of cocaine (negatively) in the naturalistic setting.
conclusionsThese results replicate and extend findings with regular heroin users, demonstrate the importance of income, cost-efficiency and supply-mindedness in cocaine seeking/use, and suggest that this interview-based approach has good external validity.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.