Addiction as a computational process gone awry.

by: AD Redish

Science, Vol. 306, No. 5703. (10 December 2004), pp. 1944-1947.

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Abstract

Addictive drugs have been hypothesized to access the same neurophysiological mechanisms as natural learning systems. These natural learning systems can be modeled through temporal-difference reinforcement learning (TDRL), which requires a reward-error signal that has been hypothesized to be carried by dopamine. TDRL learns to predict reward by driving that reward-error signal to zero. By adding a noncompensable drug-induced dopamine increase to a TDRL model, a computational model of addiction is constructed that over-selects actions leading to drug receipt. The model provides an explanation for important aspects of the addiction literature and provides a theoretic view-point with which to address other aspects.

@article{citeulike:360620, abstract = {Addictive drugs have been hypothesized to access the same neurophysiological mechanisms as natural learning systems. These natural learning systems can be modeled through temporal-difference reinforcement learning (TDRL), which requires a reward-error signal that has been hypothesized to be carried by dopamine. TDRL learns to predict reward by driving that reward-error signal to zero. By adding a noncompensable drug-induced dopamine increase to a TDRL model, a computational model of addiction is constructed that over-selects actions leading to drug receipt. The model provides an explanation for important aspects of the addiction literature and provides a theoretic view-point with which to address other aspects.}, address = {Department of Neuroscience, 6-145 Jackson Hall, 321 Church Street SE, University of Minnesota, Minneapolis, MN 55455, USA. redish@ahc.umn.edu}, author = {Redish, A. D. }, citeulike-article-id = {360620}, doi = {http://dx.doi.org/10.1126/science.1102384}, issn = {1095-9203}, journal = {Science}, keywords = {addiction, discounting, reinforcement\_learning}, month = {December}, number = {5703}, pages = {1944--1947}, posted-at = {2005-10-26 18:22:25}, priority = {2}, title = {Addiction as a computational process gone awry.}, url = {http://dx.doi.org/10.1126/science.1102384}, volume = {306}, year = {2004} }

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TY - JOUR ID - citeulike:360620 L3 - citeulike-article-id:360620 TI - Addiction as a computational process gone awry. JF - Science VL - 306 IS - 5703 SP - 1944 EP - 1947 AD - Department of Neuroscience, 6-145 Jackson Hall, 321 Church Street SE, University of Minnesota, Minneapolis, MN 55455, USA. redish@ahc.umn.edu SN - 1095-9203 N2 - Addictive drugs have been hypothesized to access the same neurophysiological mechanisms as natural learning systems. These natural learning systems can be modeled through temporal-difference reinforcement learning (TDRL), which requires a reward-error signal that has been hypothesized to be carried by dopamine. TDRL learns to predict reward by driving that reward-error signal to zero. By adding a noncompensable drug-induced dopamine increase to a TDRL model, a computational model of addiction is constructed that over-selects actions leading to drug receipt. The model provides an explanation for important aspects of the addiction literature and provides a theoretic view-point with which to address other aspects. KW - addiction KW - discounting KW - reinforcement_learning AU - Redish, AD PY - 2004/12/10/ UR - http://dx.doi.org/10.1126/science.1102384 ER -

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