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Addictive drugs have been hypothesized to access the same neurophysiologicalmechanisms as natural learning systems. These natural learningsystems can be modeled through temporal-difference reinforcementlearning (TDRL), which requires a reward-error signal that hasbeen hypothesized to be carried by dopamine. TDRL learns topredict reward by driving that reward-error signal to zero.By adding a noncompensable drug-induced dopamine increase toa TDRL model, a computational model of addiction is constructedthat over-selects actions leading to drug receipt. The modelprovides an explanation for important aspects of the addictionliterature and provides a theoretic view-point with which toaddress other aspects.
Department of Neuroscience, 6-145 Jackson Hall, 321 Church Street SE, University of Minnesota, Minneapolis, MN 55455, USA.
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