SCS Faculty Candidate

  • Postdoctoral Researcher
  • Gallant Lab, Helen Wills Neuroscience Institute
  • University of California at Berkeley

Language and the Brain: Opportunities, Challenges and Progress

The advent of machine learning has allowed us to supplement hypothesis-driven science with data-driven science. In neuroscience, most language experiments to date have studied the brain by crafting conditions designed to isolate a single hypothesis. In my research, I have focused instead on naturalistic language experiments in which subjects process a rich text, and used machine learning and natural language processing techniques to discover and test multiple hypotheses. In this talk, I will describe a framework for making inferences about what the brain represents along three levels. The first level focuses on correct inference for a single naturalistic task (reading), the second is concerned with combining data across subjects and tasks (reading, writing, speaking), and the third addresses the reproducibility of inferences drawn across subjects and tasks in entirely different experimental paradigms (controlled vs. naturalistic). My framework consists of a promising collaboration between machine learning, natural language processing and cognitive neuroscience that could help us better understand language processing, and may bring us closer to building brain-inspired intelligent systems.

Leila Wehbe is a postdoctoral researcher at the Gallant Lab in the Helen Wills Neuroscience Institute at UC Berkeley. Previously, she obtained her PhD from the Machine Learning Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University, where she worked with Tom Mitchell. She studies language representations in the brain when subjects engage in naturalistic language tasks. She combines functional neuroimaging with natural language processing and machine learning tools to build spatiotemporal maps of the information represented in the brain during language processing.

Faculty Host: Matt Gormley (ML)

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