Addiction is likely to be a complex process arising from transitions between learning algorithms. Because this model has key variables and values in place, researchers can test a variety of questions regarding addictive behaviors to better understand factors of addiction.
"Different theories about addictions have existed for a long time, but had not yet been connected with learning and memory," said David Redish, Ph. D., Department of Neuroscience, University of Minnesota. "By connecting addiction research with learning and memory research, we are able to use learning and memory models to test and predict a variety of addictive behaviors and signals."
Addictive drugs have been hypothesized to access the same neurophysiological mechanisms as natural learning systems. These systems can be modeled through temporal-difference reinforcement learning (TDRL), which requires a reward-error signal thought to be carried by dopamine.
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, 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.
This computational model of addiction connects a variety of disparate learning theories and will allow researches to test how addiction impacts learning systems.
The Academic Health Center is home to the University of Minnesota's six health professional schools and colleges as well as several health-related centers and institutes. Founded in 1851, the University is one of the oldest and largest land grant institutions in the country. The AHC prepares the new health professionals who improve the health of communities, discover and deliver new treatments and cures, and strengthen the health economy.
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