Topic: reward learning

Elucidating the neural basis of cognition requires incorporating cognitive models into the modelling of neural data. A prevalent strategy in neuroimaging is to fit computational models of cognition to concurrent behavior, and then regress the latent variables of those models onto fMRI data. Though widely used, this approach is restricted to mapping computational variables that are in some way expressed in behavior. Here we introduce a computational parametric mapping (CPM), a framework which builds on and generalizes the Bayesian population receptive field framework. CPM offers three main adva

The neurotransmitter dopamine is essential for normal reward learning and motivational arousal processes. Indeed these core functions are implicated in the major neurological and psychiatric dopamine disorders such as schizophrenia, substance abuse disorders/addiction and Parkinson's disease. Over the years, we have made significant strides in understanding the dopamine system across multiple levels of description, and I will focus on our recent advances in the computational description, and brain circuit mechanisms that facilitate the dual role of dopamine in learning and performance. I will

Learning the consequences our choices have as we interact with our world is critical for flexible behavior. Relational knowledge of one’s environment gives structure to otherwise-individual one-to-one stimulus-outcome mappings, providing a substrate to globally update behavioral contingencies in the face of changes in the landscape of reward. In the brain, this relational knowledge is thought to be encoded in the hippocampus (HPC) in the form of a cognitive map, while prefrontal regions, such as orbitofrontal cortex (OFC), are thought to instantiate subjective estimates of location on the map,