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Deconstructing appetitive learning in cocaine addiction using hierarchical Bayesian modelling

Abstract:

Background: Decision making in cocaine-addicted patients has frequently been described as suboptimal and risky. Since decisions are usually made with a view to a favourable out- come, rewards provide the motivation to make decisions and cognition is necessary to appraise the available options and evaluate the consequences involved with each choice [1]. Disadvantageous decision making in cocaine addiction may be related to impairments in either reward process- ing or goal-directed learning, or both. Computational mod- elling approaches provide a method to elucidate the nature of aberrant decision making by deconstructing the processes of choice selection. Reinforcement learning is a widely used framework that defines key parameters that underlie op- timal learning [2], for example the extent to which prior experience influences choice selection (learning rate), per- severative responding (‘stickiness’ to options presented on one side), extinction to non-reinforced choices (extinction rate) and sensitivity to the value of the reinforcer (rein- forcement sensitivity).