Princeton Neuroscience Institute and Psychology Department
In the past couple of decades reinforcement learning has emerged as a central framework for thinking of trial and error learning in the basal ganglia. However, it has been difficult to link synaptic modification to overt behavioral changes. Rodent models of DYT1 dystonia, a single-mutation motor disorder, demonstrate increased long-term potentiation and decreased long-term depression in corticostriatal synapses. Computationally, such asymmetric learning predicts risk taking in probabilistic tasks. Here we test DYT1 dystonia patients on a simple reinforcement learning task, and demonstrate abnormal risk taking correlated with disease severity, thereby implicating striatal plasticity in shaping choice behavior in humans. Our results are also relevant to the CCNP community as they suggest (and demonstrate) that behavioral tasks married to precise computational models may provide a non-invasive window to diagnosing and characterizing underlying neurological and mental disorders.