Speaker: Leila Wehbe, Postdoctoral researcher at the Gallant Lab in the Helen Wills Neuroscience Institute at UC Berkeley
Title: Studying the brain basis of language with naturalistic experiments: opportunities, challenges and progress
Abstract: 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 in the brain.