Jessica is interested in the brain mechanisms involved in learning and decision making, and the role that neuromodulatory systems such as dopamine play in these tasks. Her research focuses on computational neural network models of the basal ganglia, amygdala and dopamine system, with a particular focus on expanding those models to look at the role of dopamine and the basal ganglia in negative valence learning and understanding the brain mechanisms that drive prediction errors for worse than expected outcomes. She has applied these models to a variety of learning tasks. Her empirical research focuses on fMRI, using a conditioned inhibition experiment with juice rewards that draws on worse than expected prediction errors, and involves applying temporal-differences learning and neural network models to this experiment, as well as a pain learning task, to assess the fit of these different models to fMRI data in dopaminergic and subcortical regions. She is interested in comparing the fit of different models with empirical data, and looking at how evaluation of reward and punishment outcomes relates to addictive behaviors.