Testing neurobiology-of-language theories in the wild with NLP
Do findings about the brain obtained from controlled language tasks also apply when the brain processes natural language that is "unconstrained"? Naturalistic experiments, by their use of complex language stimuli, allow us to study language processes in the wild, and to test if theories built on controlled stimuli generalize to the natural setting. To analyse these complex experiments, NLP tools can help capture different streams of information present in the language stimulus. The language representations extracted from these tools can be carefully combined to create in vitro, computationally-controlled experiments and test different theories about how language information is represented in the brain. In this talk, I describe how we can use these approaches to disentangle the brain representation of composed meaning from individual word meaning, and semantic information from syntactic information. I also describe the promises, and potential pitfalls, of merging NLP and the cognitive neuroscience of language.
Bio: Leila Wehbe is an assistant professor in the Machine Learning Department and the Neuroscience Institute at Carnegie Mellon University. Previously, she was a postdoctoral researcher at the Helen Wills Neuroscience Institute at UC Berkeley, working with Jack Gallant. She obtained her PhD from the Machine Learning Department at Carnegie Mellon University, where she worked with Tom Mitchell. She studies language representations in the brain when subjects engage in naturalistic language tasks by combining functional neuroimaging with natural language processing and machine learning.