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Abstract：
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.
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.
A laboratory setting at the University of Missouri, USA.
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.
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.
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.
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.
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.
A laboratory setting at the University of Missouri, USA.
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.
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.
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.
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.
摘要：
尽管存在广泛的负面看法，在美国,酒精损害驾驶(AID)的患病率仍然高得令人无法接受.这项研究使用了一项新颖的决策任务来评估个人在决定是否开车时是否同时考虑了乘车服务成本和饮酒水平，以及由此产生的策略是否与参与AID相关。
双样本研究，其中样本1开发了一个新的AID决策任务，通过决策策略对参与者进行分类。样本2用于交叉验证任务，并检查决策策略分类是否可以预测先前报告的AID行为。
密苏里大学的实验室,美国。
样本1包括来自密苏里大学心理学入门课程的38名学生参与者。样本2包括从当地社区招募的67名年轻成人参与者。
我们开发了一项决策任务，该任务提出了假设的饮酒情景，该情景在饮酒量（一到六种饮料）和乘车服务成本（5-25美元）方面有所不同。我们应用贝叶斯计算模型将选择分类为与以下任一一致：在做出假设的AID决策时，将乘车成本和消费水平（补偿性）相结合或仅考虑消费水平（非补偿性）。在样本2中，我们评估了已确定的AID风险因素(性别，最近饮酒，感知的安全限度)和最近(过去3个月)参与AID。
在样本1中，大多数参与者被分类为使用与补偿或非补偿过程一致的决策策略。样本2的结果复制了总体分类率，并表明使用补偿策略的参与者更有可能报告最近的AID，即使在考虑了研究协变量之后。
在假设的酒精受损驾驶(AID)决策任务中，考虑消费水平和乘车服务成本的个人比完全根据消费水平做出决定的人更有可能报告最近的AID。
双样本研究，其中样本1开发了一个新的AID决策任务，通过决策策略对参与者进行分类。样本2用于交叉验证任务，并检查决策策略分类是否可以预测先前报告的AID行为。
密苏里大学的实验室,美国。
样本1包括来自密苏里大学心理学入门课程的38名学生参与者。样本2包括从当地社区招募的67名年轻成人参与者。
我们开发了一项决策任务，该任务提出了假设的饮酒情景，该情景在饮酒量（一到六种饮料）和乘车服务成本（5-25美元）方面有所不同。我们应用贝叶斯计算模型将选择分类为与以下任一一致：在做出假设的AID决策时，将乘车成本和消费水平（补偿性）相结合或仅考虑消费水平（非补偿性）。在样本2中，我们评估了已确定的AID风险因素(性别，最近饮酒，感知的安全限度)和最近(过去3个月)参与AID。
在样本1中，大多数参与者被分类为使用与补偿或非补偿过程一致的决策策略。样本2的结果复制了总体分类率，并表明使用补偿策略的参与者更有可能报告最近的AID，即使在考虑了研究协变量之后。
在假设的酒精受损驾驶(AID)决策任务中，考虑消费水平和乘车服务成本的个人比完全根据消费水平做出决定的人更有可能报告最近的AID。
