Ph.D., The Weizmann Institute of Science
The Hasson Lab investigates the underlying neural basis of brain-to-brain human communication, natural language processing, and language acquisition in children as they materialize in real-world contexts. Our studies of neural responses to natural stimuli (such as audio-visual narratives) led us to realize that models developed under particular experimental manipulations in the lab fail to capture variance in real-life contexts (see, for example, our work on process memory). Inspired by the success of deep learning in modeling natural stimuli, the Hasson Lab aims to develop new theoretical frameworks and computational tools to model the neural basis of cognition as it materializes in the real world.
Natural language processing - that is, the way we use language to communicate our thoughts in daily life - provides a recent example of our attempt to model cognition as it materializes in the real world. The lab aims to model brain responses as people engage in open-ended conversations. Our data set consists of ECoG data (intracranial EEG) collected continuously from the brains of epileptic patients as they were engaged in open-ended, free conversations with their doctors, friends, and family members during a week-long stay at the hospital. Advances in deep language models provide a new theoretical framework for modeling natural language processing in the human brain. We currently explore whether we can use deep language models as a cognitive model to explain natural language processing in the human brain. In particular, we are searching for shared computational principles and inherent differences in how the brain and deep neural networks process natural language. Overall, our findings suggest that deep language models provide a new and biologically feasible computational framework for studying the neural basis of language.