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Applying Bayesian cognitive models to decisions to drive after drinking
Author(s) -
McCarthy Denis M.,
McCarty Kayleigh N.,
Hatz Laura E.,
Prestigiacomo Christiana J.,
Park Sanghyuk,
DavisStober Clintin P.
Publication year - 2021
Publication title -
addiction
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.424
H-Index - 193
eISSN - 1360-0443
pISSN - 0965-2140
DOI - 10.1111/add.15302
Subject(s) - sample (material) , psychology , consumption (sociology) , task (project management) , alcohol consumption , cognition , social psychology , applied psychology , psychiatry , engineering , alcohol , social science , biochemistry , chemistry , systems engineering , chromatography , sociology
Background and aims Despite 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. Design A 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. Setting A laboratory setting at the University of Missouri, USA. Participants Sample 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. Findings In 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. Conclusions In 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.