Literature DB >> 33118248

Applying Bayesian cognitive models to decisions to drive after drinking.

Denis M McCarthy1, Kayleigh N McCarty1, Laura E Hatz1, Christiana J Prestigiacomo1, Sanghyuk Park1, Clintin P Davis-Stober2.   

Abstract

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.
© 2020 Society for the Study of Addiction.

Entities:  

Keywords:  Alcohol; Bayesian modeling; alcohol-impaired driving; cognitive modeling; decision-making; drinking and driving

Mesh:

Substances:

Year:  2021        PMID: 33118248      PMCID: PMC8281388          DOI: 10.1111/add.15302

Source DB:  PubMed          Journal:  Addiction        ISSN: 0965-2140            Impact factor:   7.256


  14 in total

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Authors:  Hayley R Treloar; David H Morris; Sarah L Pedersen; Denis M McCarthy
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9.  Drinking-and-Driving-Related Cognitions Mediate the Relationship Between Alcohol Demand and Alcohol-Impaired Driving.

Authors:  Michael Amlung; David H Morris; Laura E Hatz; Jenni B Teeters; James G Murphy; Denis M McCarthy
Journal:  J Stud Alcohol Drugs       Date:  2016-07       Impact factor: 2.582

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  1 in total

1.  Decision strategies while intoxicated relate to alcohol-impaired driving attitudes and intentions.

Authors:  Sara D McMullin; Courtney A Motschman; Laura E Hatz; Denis M McCarthy; Clintin P Davis-Stober
Journal:  Psychol Addict Behav       Date:  2022-01-13
  1 in total

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