1. Modeling motivation for alcohol in humans using traditional and machine learning approaches.
- Author
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Grodin, Erica N, Montoya, Amanda K, Bujarski, Spencer, and Ray, Lara A
- Subjects
Humans ,Alcoholic Intoxication ,Ethanol ,Breath Tests ,Self Administration ,Motivation ,Reward ,Models ,Psychological ,Adult ,Female ,Male ,Young Adult ,Craving ,Machine Learning ,Reinforcement ,Psychology ,alcohol seeking ,delay discounting ,machine learning ,motivation ,progressive ratio ,Alcoholism ,Alcohol Use and Health ,Brain Disorders ,Substance Misuse ,Clinical Research ,Behavioral and Social Science ,Oral and gastrointestinal ,Mental health ,Good Health and Well Being ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Substance Abuse - Abstract
Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely been studied using preclinical models. In order to bridge the gap between preclinical and clinical studies, this study examined predictors of motivation for alcohol self-administration using a novel paradigm. Heavy drinkers (n = 67) completed an alcohol infusion consisting of an alcohol challenge (target breath alcohol = 60 mg%) and a progressive-ratio alcohol self-administration paradigm (maximum breath alcohol 120 mg%; ratio requirements range = 20-3 139 response). Growth curve modeling was used to predict breath alcohol trajectories during alcohol self-administration. K-means clustering was used to identify motivated (n = 41) and unmotivated (n = 26) self-administration trajectories. The data were analyzed using two approaches: a theory-driven test of a-priori predictors and a data-driven, machine learning model. In both approaches, steeper delay discounting, indicating a preference for smaller, sooner rewards, predicted motivated alcohol seeking. The data-driven approach further identified phasic alcohol craving as a predictor of motivated alcohol self-administration. Additional application of this model to AUD translational science and treatment development appear warranted.
- Published
- 2021