Improving translational insight using sequential sampling models in drug choice

The translation of pharmacological treatments for alcohol use disorder (AUD) remains challenging despite advances in neuroscientific and molecular sciences enabling unprecedented levels of analysis. We propose that formal characterization of pre-clinical discrete choice procedures with human decision-making models offers a solution. We review evidence supporting choice procedures in the screening of pharmacological treatments and the remarkable success of sequential sampling models in explaining decision making across tasks and species. This success implies shared cognitive laws of decision making that can be leveraged to improve translational insight. These formal approaches can integrate diverse datasets to clarify their role in treatment-related effects and are straightforward to implement, which we demonstrate in a brief, practical tutorial using open-source software.