Mathematical Modeling of Intoxicated Risky Decision Making

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(7) Project Summary/Abstract Long Term Objectives: The overarching goals of this application are to (1) use mathematical models of decision making to better understand risk for engagement in alcohol-related negative behaviors, and to (2) use process models to test novel hypotheses about alcohol’s effects on cognitive processes underlying impulsivity. The applicant’s main career objective is to develop a program of research integrating sophisticated quantitative modeling approaches with rigorous laboratory addictions techniques to characterize alcohol’s effects on risk taking behaviors. Specific Aims: The proposed project aims to (1) model acute alcohol effects on decision making strategies and variability in preference in risky sexual behavior, and (2) apply process models (e.g., diffusion models) to traditional laboratory measures of impulsivity to decompose behavioral data into psychologically relevant components of cognitive processing (e.g., rate of evidence accumulation, non-decision processes). In order to complete the proposed project, the applicant will receive extensive training in innovative mathematical modeling techniques from experts in the field of judgment and decision making. Training will be obtained via (1) coursework, (2) conference and workshop attendance, and (3) meetings with expert consultants in cognitive process models and alcohol-related impulsivity. Method: The applicant will recruit 40 participants (ages 21-29) to complete a within-subjects double-blind alcohol/placebo administration study. Each participant will complete three sessions during which they will consume a low-moderate dose of alcohol, a moderate-high dose of alcohol, or a placebo beverage (counterbalanced across sessions). Following beverage administration, participants will complete a delay discounting task, cued go/no-go task, and tasks assessing hypothetical monetary and sexual decision making. Data will be analyzed using mathematical modeling approaches designed to assess discrete features (e.g., decision making strategy, preference variability, evidence accumulation rate) of choice behavior that are not assessed via traditional analytic techniques. Statistical analyses will assess (1) alcohol dose and placebo effects on features of decision making and (2) associations between these features and self-reported alcohol- related negative behaviors. Significance: Results from this project will increase understanding of factors contributing to alcohol-related risky decision making. These findings will help to clarify individual differences in alcohol’s pharmacological effects on risk taking behaviors, and may ultimately help to inform efforts to prevent and reduce harm associated with alcohol use.