The role of learning-related dopamine signals in addiction vulnerability. Huys, Q. J M., Tobler, P. N., Hasler, G., & Flagel, S. B. Prog Brain Res, 211:31–77, 2014. Publisher: Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA; Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA.Paper doi abstract bibtex Dopaminergic signals play a mathematically precise role in reward-related learning, and variations in dopaminergic signaling have been implicated in vulnerability to addiction. Here, we provide a detailed overview of the relationship between theoretical, mathematical, and experimental accounts of phasic dopamine signaling, with implications for the role of learning-related dopamine signaling in addiction and related disorders. We describe the theoretical and behavioral characteristics of model-free learning based on errors in the prediction of reward, including step-by-step explanations of the underlying equations. We then use recent insights from an animal model that highlights individual variation in learning during a Pavlovian conditioning paradigm to describe overlapping aspects of incentive salience attribution and model-free learning. We argue that this provides a computationally coherent account of some features of addiction.
@article{huys_role_2014,
title = {The role of learning-related dopamine signals in addiction vulnerability.},
volume = {211},
url = {http://dx.doi.org/10.1016/B978-0-444-63425-2.00003-9},
doi = {10.1016/B978-0-444-63425-2.00003-9},
abstract = {Dopaminergic signals play a mathematically precise role in reward-related learning, and variations in dopaminergic signaling have been implicated in vulnerability to addiction. Here, we provide a detailed overview of the relationship between theoretical, mathematical, and experimental accounts of phasic dopamine signaling, with implications for the role of learning-related dopamine signaling in addiction and related disorders. We describe the theoretical and behavioral characteristics of model-free learning based on errors in the prediction of reward, including step-by-step explanations of the underlying equations. We then use recent insights from an animal model that highlights individual variation in learning during a Pavlovian conditioning paradigm to describe overlapping aspects of incentive salience attribution and model-free learning. We argue that this provides a computationally coherent account of some features of addiction.},
language = {eng},
journal = {Prog Brain Res},
author = {Huys, Quentin J M. and Tobler, Philippe N. and Hasler, Gregor and Flagel, Shelly B.},
year = {2014},
pmid = {24968776},
note = {Publisher: Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA; Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA.},
pages = {31--77},
}
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