ArticleAddiction (Abingdon, England)2021
Applying Bayesian cognitive models to decisions to drive after drinking.
Article in Addiction (Abingdon, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Within-Person and Between-Person Risk Perceptions Predict Driving After Drinking: An Ecological Momentary Assessment Study.Alcohol, clinical & experimental research · 2026Article
- Do Subjective Effects from Alcohol and Cannabis Predict Simultaneous Use During a Decision-Making Task?Substance use & misuse · 2024Article
- Decision strategies while intoxicated relate to alcohol-impaired driving attitudes and intentions.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2022Article
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Authors and funding
6 authors.
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Abstract
BACKGROUND AND
aimsDespite widespread negative perceptions, the prevalence of alcohol-impaired driving (AID) in the United States remains unacceptably high. This study used a novel decision task to evaluate whether individuals considered both ride service cost and alcohol consumption level when deciding whether or not to drive, and whether the resulting strategy was associated with engagement in AID.
designA two-sample study, where sample 1 developed a novel AID decision task to classify participants by decision strategy. Sample 2 was used to cross-validate the task and examine whether decision strategy classifications were predictive of prior reported AID behavior.
settingA laboratory setting at the University of Missouri, USA.
participantsSample 1 included 38 student participants from introductory psychology classes at the University of Missouri. Sample 2 included 67 young adult participants recruited from the local community. MEASUREMENTS: We developed a decision task that presented hypothetical drinking scenarios that varied in quantity of alcohol consumption (one to six drinks) and the cost of a ride service ($5-25). We applied a Bayesian computational model to classify choices as consistent with either: integrating both ride cost and consumption level (compensatory) or considering only consumption level (non-compensatory) when making hypothetical AID decisions. In sample 2, we assessed established AID risk factors (sex, recent alcohol consumption, perceived safe limit) and recent (past 3 months) engagement in AID.
findingsIn sample 1, the majority of participants were classified as using decision strategies consistent with either a compensatory or non-compensatory process. Results from sample 2 replicated the overall classification rate and demonstrated that participants who used a compensatory strategy were more likely to report recent AID, even after accounting for study covariates.
conclusionsIn a hypothetical alcohol-impaired driving (AID) decision task, individuals who considered both consumption level and ride service cost were more likely to report recent AID than those who made decisions based entirely on consumption level.
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Registered trials
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.