Computational Substrates of Addiction and Reward

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DESCRIPTION (provided by applicant): Drugs of abuse operate in part by subverting the adaptive mechanisms (algorithms) that are normally used to choose actions and make decisions. Otherwise, why would a normal human give up food, sex, shelter, and general well being to put white powder in their nose or heroin in their veins? Clearly an, addict's decision making algorithms are malfunctioning, and in this sense, drug taking represents a set of diseased algorithms for reward-learning and reward-dependent decision-making. It is well known that many if not most drugs of abuse affect midbrain dopamine systems either at the level of dopamine receptors or directly on the neurons that deliver dopamine to target structures. It is therefore important to understand the algorithms that these systems carry out, the way that the algorithms normally function, and the way they are linked to underlying cellular and molecular events. The broad objective of this proposal is to establish one such link by providing computational understanding of the kinds of information constructed and broadcast by midbrain dopamine systems and the influence of these signals on downstream neural targets. Using functional magnetic resonance imaging (fMRI) in human subjects, this work will carry out detailed tests of a computational model of midbrain dopaminergic systems: the prediction error model. This model has been shown to account for rapid changes in neural activity in non-human primates during reward-dependent learning and decision-making tasks. One central idea of this computational model is that activity changes in a large subset of dopamine neurons of the ventral tegmental area and substantia nigra represent errors in the predictions of future rewarding events. The details of this model make numerous important predictions that have never been tested in human subjects. This work will identify those neural pathways involved in reward conditioning in humans and will serve to test and extend the prediction error model of dopaminergic function. Moreover; by studying a fundamental neurobiological process of reward expectancy in humans, this proposal has broad implications for understanding aspects of learning and other cognitive processes.