Addiction as a Computational Process Gone Awry
Top Cited Papers
- 10 December 2004
- journal article
- other
- Published by American Association for the Advancement of Science (AAAS) in Science
- Vol. 306 (5703) , 1944-1947
- https://doi.org/10.1126/science.1102384
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.Keywords
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