Abstract:
The value of the environment determines animals’ motivational states and sets expectations for error-based learning. But how are values computed? We developed a novel temporal wagering task with latent structure, and used high-throughput behavioral training to obtain well-powered behavioral datasets from hundreds of rats that learned the structure of the task. We found that rats use distinct value computations for sequential decisions within single trials. Moreover, these sequential decisions are supported by different brain regions, suggesting that distinct neural circuits support specific types of value computations. I will discuss our ongoing efforts to delineate how distributed circuits in the orbitofrontal cortex and striatum coordinate complex value-based decisions in this task.
Biography
Christine graduated from New York University with a B.S. in Neural Science in 2008, and earned her PhD in Neurobiology and Behavior in Randy Bruno’s lab at Columbia University in 2013. She then completed a postdoctoral fellowship at the Princeton Neuroscience Institute with David Tank and Carlos Brody. She joined NYU as an Assistant Professor in the Center for Neural Science in January 2019.