Nathaniel J. Smith - Academic homepage


Until recently, I was a Computational Fellow at BIDS, the UC Berkeley Institute for Data Science, where I divide my time between computationally-informed research on human cognition (esp. language processing), and on building better computational tools for researchers in general.

Brief research statement: Language is one of humanity's most complicated artifacts -- yet language use is fast, effective, and tightly coordinated with concurrent non-linguistic activities. The goal of my research is to understand the architecture of the cognitive systems that allow language to be used in real time, and to interact in a fine-grained, flexible, and non-modular way with non-linguistic cognition and action. I'm interested in this both for its own sake, and because it seems to me a paradigm case of a challenging cognitive task: a domain where some of the complexities of high-level cognition are laid bare, and whose study is likely to give insight into the architecture of high- and low-level cognition in general. Theoretically, my work draws on insights from traditional, psycho-, cognitive, and computational linguistics, and also theoretical tools from other psychological domains, in particular rational models of perception and control. Empirically, I use a wide variety of methods, including both designed experiments and corpus studies of eye-tracking, self-paced reading, cloze tasks, and EEG/ERP/rERP.

Some recent papers/manuscripts