Nathaniel J. Smith - Academic homepage
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.
- Abend, O., Kwiatkowski, T., Smith,
N. J., Goldwater, S., & Steedman, M. (2017). Bootstrapping Language
Cognition, 164, 116-143.
- Smith, N. J. (submitted). ZS: A
file format for efficiently distributing, using, and archiving
record-oriented data sets of any size.
- Smith, N. J., & Kutas,
M. (2015). Regression-based estimation of ERP waveforms: I. The
Psychophysiology, 52(2), 157-168.
- Smith, N. J., & Kutas,
M. (2015). Regression-based estimation of ERP waveforms:
II. Non-linear effects, overlap correction, and practical
Psychophysiology, 52(2), 169-189.
- Smith, N. J., Goodman, N. D., & Frank,
M. C. (2013). Learning and using language via recursive
pragmatic reasoning about other agents. In C. J. C. Burges and L. Bottou and
M. Welling and Z. Ghahramani and K. Q. Weinberger (Eds.),
Advances in neural information
processing systems (NIPS) 26 (pp. 3039-3047).
PDF, poster PDF)
- Smith, N. J.
(in press). Blending across modalities in mathematical discourse. In
L. D. Edwards, F. Ferrara, & D. Moore-Russo (Eds.),
perspectives in gesture and embodiment in mathematics. Charlotte,
NC: Information Age Publishing.
- Smith, N. J., & Levy, R. (2013). The
effect of word predictability on reading time is
Cognition, 128(3), 302-319.