# Mathematical Modeling of Intoxicated Risky Decision Making

> **NIH NIH F31** · UNIVERSITY OF MISSOURI-COLUMBIA · 2020 · $35,846

## Abstract

(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.

## Key facts

- **NIH application ID:** 9938304
- **Project number:** 5F31AA027162-02
- **Recipient organization:** UNIVERSITY OF MISSOURI-COLUMBIA
- **Principal Investigator:** Laura E Hatz
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $35,846
- **Award type:** 5
- **Project period:** 2019-07-01 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9938304

## Citation

> US National Institutes of Health, RePORTER application 9938304, Mathematical Modeling of Intoxicated Risky Decision Making (5F31AA027162-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9938304. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*