Dopamine in Reward Learning and Neuropsychiatric Disorders Context and Salience: the Role of Dopamine in Reward Learning and Neuropsychiatric Disorders

iii Abstract Evidence suggests that a change in the firing rate of dopamine (DA) cells is a major neurobiological correlate of learning. The Temporal Difference (TD) learning algorithm provides a popular account of the DA signal as conveying the error between expected and actual rewards. Other accounts have attempted to code the DA firing pattern as conveying surprise or salience. The DA mediated cells have also been implicated in several neuropsychological disorders such as obsessive compulsive disorder and schizophrenia. Compelling neuropsychological explanations of the DA signal also frame it as conveying salience. A model-based reinforcement learning algorithm using a salience signal analogous to dopamine neurons was built and used to model existing animal behavioral data. Different reinforcement learning models were then compared under conditions of altered DA firing patterns. Several differing predictions of the TD model and the salience model were compared against animal behavioral data in an obsessive compulsive disorder (OCD) model using a dopamine agonist. The results show that the salience model predictions more accurately model actual animal behavior. The role of context in the salience model is different than the standard TD-learning algorithm. Several predictions of the salience model for how people should respond to context shifts of differing salience were tested against known behavioral correlates of endogenous dopamine levels. As predicted, individuals with behavioral traits correlated with higher endogenous dopamine levels are far more sensitive to low salience context shifts than those with correlates to lower endogenous dopamine levels. This is a unique prediction of the salience model for the DA signal which allows for better integration of reinforcement learning models and neuropsychological frameworks for discussing the role of dopamine in learning, memory and behavior. iv Acknowledgments Primary thanks goes to my supervisor, Sue Becker, whose support and assistance with this research went above and beyond the call of duty. I would also like to extend thanks to my committee members, Scott Watter and Pat Bennett, for their assistance and ideas throughout my time at McMaster. Finally I would like to extend special thanks to my friends and family, particularly my mother and father, for their unwavering support in helping me complete this thesis.