Suboptimal decision-making strategies have been proposed to contribute to the pathophysiology of addiction. Decision-making, however, arises from a collection of computational components that can independently influence behavior. Disruptions in these different components can lead to decision-making deficits that appear similar behaviorally, but differ at the computational, and likely the neurobiological, level. Here, we discuss recent studies that have used computational approaches to investigate the decision-making processes underlying addiction. Studies in animal models have found that value updating following positive, but not negative, outcomes is predictive of drug use, whereas value updating following negative, but not positive, outcomes is disrupted following drug self-administration. We contextualize these findings with studies on the circuit and biological mechanisms of decision-making to develop a framework for revealing the biobehavioral mechanisms of addiction.
|Original language||English (US)|
|Number of pages||10|
|Journal||Trends in Neurosciences|
|State||Published - Feb 2022|
Bibliographical noteFunding Information:
This work was funded by NIDA DA051598 (S.M.G.), DA051977 (S.M.G.), DA041480 (J.R.T.), and DA043443 (J.R.T.), and NIAAA AA012870 (J.R.T.) and AA029454 (S.L.T.). Additional support was provided by the State of Minnesota through its support of the Medical Discovery Team on Addiction at the University of Minnesota and the State of Connecticut, Department of Mental Health and Addiction Services through its support of the Ribicoff Research Facilities. The work described in this manuscript does not express the views of the Department of Mental Health and Addiction Services or the States of Connecticut or Minnesota. The views and opinions expressed are those of the authors.
© 2021 Elsevier Ltd
- nucleus accumbens
- orbitofrontal cortex
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't