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The Hasson Lab investigates the underlying neural basis of brain-to-brain human communication, natural language processing, and language acquisition in children, materializing in real-world contexts. The studies of neural responses to natural stimuli, such as audio-visual narratives, have led to the realization that the models developed for particular experimental manipulations in the lab fail to capture the variance found in real-life contexts, as exemplified by work on memory processes. Inspired by the success of deep learning models in capturing natural stimuli, the 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, the way we use language to communicate our thoughts in daily life, provides a recent example of an attempt to model cognition in these real-world contexts. The lab models brain responses while people engage in open-ended conversations. Their dataset consists of ECoG data (intracranial EEG) collected continuously from the brains of epilepsy patients engaged in open-ended, free conversations with doctors, friends, and family during a week-long hospital stay. Advances in deep language models provide a new theoretical framework for understanding natural language processing in the human brain, where the lab is currently exploring how these models can explain the neural processing of language. Findings suggest that deep language models offer a new biologically feasible computational framework for studying the neural basis of language.
GRE scores are not accepted. Ph.D. is the primary degree; students are not required to hold an M.S.E. prior to admission.