ReviewPsychopharmacology2026
Improving translational insight using sequential sampling models in drug choice.
Review in Psychopharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.