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Related Concept Videos

Decision Making01:20

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Applying Bayesian cognitive models to decisions to drive after drinking.

Denis M McCarthy1, Kayleigh N McCarty1, Laura E Hatz1

  • 1Department of Psychological Sciences, University of Missouri, Columbia, MO, USA.

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|October 29, 2020
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Summary

Individuals who weigh ride service costs alongside alcohol consumption when deciding whether to drive are more likely to engage in alcohol-impaired driving (AID). This highlights a key factor in AID behavior, even when considering alternatives.

Keywords:
AlcoholBayesian modelingalcohol-impaired drivingcognitive modelingdecision-makingdrinking and driving

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Area of Science:

  • Behavioral Science
  • Public Health
  • Transportation Safety

Background:

  • Alcohol-impaired driving (AID) remains a significant public health issue in the United States.
  • Previous research indicates a high prevalence of AID despite negative public perceptions.
  • Understanding decision-making processes related to AID is crucial for developing effective interventions.

Purpose of the Study:

  • To investigate whether individuals consider both ride service cost and alcohol consumption level when making decisions about driving.
  • To determine if decision-making strategies influence the likelihood of engaging in alcohol-impaired driving.
  • To develop and validate a novel decision task for classifying AID-related decision strategies.

Main Methods:

  • A two-sample study design was employed, involving a laboratory setting at the University of Missouri.
  • A novel decision task was developed to classify participants' strategies as compensatory (considering both cost and consumption) or non-compensatory (considering only consumption).
  • Bayesian computational modeling was used to analyze choices in hypothetical drinking scenarios, with cross-validation in a second sample that also assessed AID risk factors and recent behavior.

Main Results:

  • The majority of participants in the first sample were classified as using either compensatory or non-compensatory decision strategies.
  • The second sample successfully replicated the classification rates from the novel task.
  • Participants employing a compensatory strategy were significantly more likely to report recent alcohol-impaired driving, even after controlling for other factors.

Conclusions:

  • Decision-making strategies in hypothetical alcohol-impaired driving scenarios are classifiable.
  • Considering ride service cost in conjunction with alcohol consumption level is associated with a higher likelihood of recent AID.
  • These findings suggest that the integration of cost-benefit analyses, including ride-sharing expenses, plays a role in the decision to drive while impaired.