A Short Biography:

Language, a capacity universal across human cultures but unique to our species, can transfer thoughts across minds. My lab studies how the feat of language comprehension is made possible—i.e., the “cognitive architecture” of mental representations and processes that underlies our ability to understand language. The lab aims to “carve comprehension at its joints” into sub-components that are functionally distinct from one another, describe how they work together during language processing, and discover the formats of mental data-structures that constitute the “meaning” of language. This endeavor includes two parallel avenues: (1) looking “inward” into language, i.e., establishing the constituent mechanisms of language itself; and (2) looking “outward”, establishing the place of language within the broader architecture of the human mind, i.e., with regard to other high-level cognitive processes. To this end, I take a cognitive neuroscience approach: I use fMRI to study the neural mechanisms that are engaged in comprehension in order to inform and constrain cognitive theories. Specifically, a large portion of my work takes a "cognitive network neuroscience” perspective, investigating sets of brain regions that constitute functionally integrated systems and are “natural kinds” of the brain’s functional organization; the main network I focus on is the “core language network”, a domain-specific network that is reliably and robustly engaged in language processing, but not during other high-level cognitive processes. I study the internal organization of this network, and its functional relationships with other large-scale networks, such as those that support executive functions (i.e., fluid intelligence) or mentalizing (i.e., social cognition). I use a synergistic combination of two neuroimaging methodologies: (1) “individual phenotyping”, i.e., identifying functional networks in individual participants (rather than at the group-level), which confers higher interpretability, replicability, and statistical power compared to traditional methods; and (2) naturalistic paradigms, such as passive story comprehension, which mimic “language in the wild” by broadly sampling the range of cognitive processes engaged in real-life language processing (cf. cleverly designed artificial tasks for studying “language in the lab”). These methodologies are combined with tools from computational modeling, psycholinguistics, and linguistic theory.