Computational model of addiction which can be used to make predictions about human behavior

Dec 13 2004

A University of Minnesota researcher developed a computational model of addiction which can be used to make predictions about human behavior, animal behavior, and neurophysiology.

By bringing addiction theory into a computational realm, researchers will be able to ask and answer key questions to gain valuable insight into addictive behavior. The model was developed based on two hypotheses: that dopamine serves as a reward-error learning signal to produce temporal-difference learning in the normal brain, and that cocaine produces an increase in dopamine directly in phases. The research will be published in the December 10 issue of Science.

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.

http://www.umn.edu/