Cognitive neuroscientists employ clever experimental manipulations to explain the relationship between the brain, behavior, and the environment. We hope that the models we derive from tightly-controlled experimental manipulations will provide some traction in real-world contexts. This belief relies on the assumption that the human brain implements a set of rules that capture the underlying principles by which the world works. We assume that these rules, like in classical physics, are relatively simple and interpretable and, once discovered, will extrapolate to the richness of human behavior. To discover these rules, we filter out as many seemingly irrelevant variables (considered “confounds ”or “noise ”) in hopes of isolating the few essential latent variables ( “signals ”) that dictate brain-behavior relationships. But to what extent do our experiments uncover simple rules that can be generalized to human behavior outside the laboratory? 

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). 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 - is a prime example of the tension between our interpretable theories and the messiness of 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. 

Traditionally, the investigation of the neural basis of language relied on psycholinguistic models, which use explicit symbolic representations of parts of speech (like nouns, verbs, adjectives, adverbs, and determiners) combined with rule-based logical operations embedded in hierarchical tree structures. Classical symbolic-based linguistic models, however, fail to capture the multitude of linguistic variations in the real world. 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. 

Cognition materializes in an interpersonal space. Little is known about the neural substrates that underlie our ability to communicate with other brains in naturalistic settings.  We are developing novel methodological and analytical tools for characterizing neural responses between speaker and listener - during the production and comprehension - of complex real-life speech. We use fMRI and ECoG hyperscanning methods to measure the neural coupling during natural communication. We study the speaker-listener neural coupling during storytelling, open conversations, and teacher-student structured interaction. Our studies start to uncover the shared underlying neural mechanisms at the basis of the ability to transfer our thoughts, memories, and emotions to others.