Addiction as a computational process gone awry.
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.
Redish
A David
AD
Department of Neuroscience, 6-145 Jackson Hall, 321 Church Street SE, University of Minnesota, Minneapolis, MN 55455, USA. redish@ahc.umn.edu
D000818:Animals; D002755:Choice Behavior; D019970:Cocaine-Related Disorders / Q000503:physiopathology* / Q000523:psychology; D003198:Computer Simulation; D003463:Cues; D004298:Dopamine / Q000502:physiology*; D006801:Humans; D007858:Learning; D008433:Mathematics; D008959:Models, Neurological; D008960:Models, Psychological; D009474:Neurons / Q000502:physiology; D011336:Probability; D012054:Reinforcement, Psychology; D012201:Reward
D004298:Dopamine