A neuroscientist at the University of Minnesota reports has developed a computational model of drug addiction. The aim is to test hypotheses about how the brain "learns" addictive behaviors. From the University's announcement about the project, described in this week's issue of the scientific journal Science:
"Natural increases in dopamine occur after unexpected natural rewards; however, with learning these increases shift from the time of reward delivery to cueing stimuli. In TDRL (temporal-difference reinforcement learning), once the value function predicts the reward, learning stops. Cocaine and other addictive drugs, however, produce a momentary increase in dopamine through neuropharmacological mechanisms, thereby continuing to drive learning, forcing the brain to over-select choices which lead to getting drugs."
