Application of a computational decision model to examine acute drug effects on human risk taking.
Scott D Lane, Eldad Yechiam, Jerome R. Busemeyer
Experimental and Clinical Psychopharmacology May 2006 DOI: 10.1037/1064-1297.14.2.254 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Secondary computational modeling analysis of individual-participant data from three prior experiments Peer reviewed |
|---|---|
| Population | Adult humans who participated in three previous experiments on alcohol, marijuana, and alprazolam effects on risky decision making |
| Interventions | Alcohol Marijuana Alprazolam |
| Dose | highest administered dose of all 3 drugs and corresponding placebo doses |
| Measures | expectancy valence model (J. R. Busemeyer & J. C. Stout, 2002) |
| Key findings | The expectancy valence model indicated that alcohol raised risky decision making mainly by increasing responsiveness to risky rewards and decreasing responsiveness to risky losses, while marijuana and alprazolam did so through altered learning/memory rather than motivation. Alcohol and marijuana also made choices less consistently tied to expected outcomes; no significant gender effects were found. |
Abstract
In 3 previous experiments, high doses of alcohol, marijuana, and alprazolam acutely increased risky decision making by adult humans in a 2-choice (risky vs. nonrisky) laboratory task. In this study, a computational modeling analysis known as the expectancy valence model (J. R. Busemeyer & J. C. Stout, 2002) was applied to individual-participant data from these studies, for the highest administered dose of all 3 drugs and corresponding placebo doses, to determine changes in decision-making processes that may be uniquely engendered by each drug. The model includes 3 parameters: responsiveness to rewards and losses (valence or motivation); the rate of updating expectancies about the value of risky alternatives (learning/memory); and the consistency with which trial-by-trial choices match expected outcomes (sensitivity). Parameter estimates revealed 3 key outcomes: Alcohol increased responsiveness to risky rewards and decreased responsiveness to risky losses (motivation) but did not alter expectancy updating (learning/memory); both marijuana and alprazolam produced increases in risk taking that were related to learning/memory but not motivation; and alcohol and marijuana (but not alprazolam) produced more random response patterns that were less consistently related to expected outcomes on the 2 choices. No significant main effects of gender or dose by gender interactions were obtained, but 2 dose by gender interactions approached significance. These outcomes underscore the utility of using a computational modeling approach to deconstruct decision-making processes and thus better understand drug effects on risky decision making in humans.