Addiction as a computational process gone awry. Export

@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}, citeulike-linkout-0 = {http://dx.doi.org/10.1126/science.1102384}, citeulike-linkout-1 = {http://www.sciencemag.org/cgi/content/abstract/306/5703/1944}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/15591205}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=15591205}, day = {10}, doi = {10.1126/science.1102384}, issn = {1095-9203}, journal = {Science}, keywords = {addiction, ex, learning, neuroscience, reinforcement}, month = {December}, number = {5703}, pages = {1944--1947}, posted-at = {2008-12-13 13:44:58}, priority = {2}, title = {Addiction as a computational process gone awry.}, url = {http://dx.doi.org/10.1126/science.1102384}, volume = {306}, year = {2004} }

TY - JOUR ID - citeulike:360620 L3 - citeulike-article-id:360620 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. IS - 5703 JF - Science SN - 1095-9203 AD - Department of Neuroscience, 6-145 Jackson Hall, 321 Church Street SE, University of Minnesota, Minneapolis, MN 55455, USA. redish@ahc.umn.edu EP - 1947 TI - Addiction as a computational process gone awry. VL - 306 SP - 1944 KW - addiction KW - ex KW - learning KW - neuroscience KW - reinforcement AU - Redish, AD PY - 2004/12/10/ UR - http://dx.doi.org/10.1126/science.1102384 ER -