I am an associate professor at UCLA in the Department of Linguistics, and director of the UCLA Language Processing Lab. My research investigates how language users develop a sufficiently rich linguistic meaning during online comprehension. Recent topics include the processing of ellipsis and the assignment of focus, as well as the role of other semantic, pragmatic, and prosodic defaults in sentence interpretation.
I am committed to using experimental methods in my research, including Internet based questionnaires, corpora, and online methods such as self-paced reading and eye tracking. See this page for a description of the various methods and data collection tools used in the lab.
I am an organizer for the California Meeting on Psycholinguistics (CAMP), and hosted the inaugural meeting at UCLA in 2017. CAMP 2018 was held at the University of Southern California. CAMP 2019 was held at UC Santa Cruz. CAMP 2021 will be held virtually at UC Irvine.
Finally, I often lead the Psycholinguistics / Neurolinguistics Seminar; the current schedule may be found here.
PhD in Linguistics, 2012
MSc in Logic, 2007
University of Amsterdam
MA in Linguistics, 2003
University of Chicago
BA in Linguistics, 2003
University of Chicago
How does the language processing system make efficient use of multiple sources of information to produce a sufficiently rich representation? What information may go underspecified? How does grammatical knowledge constrain representations considered during online sentence processing?*
Recent and upcoming
Psycholinguistics is a relatively young, but rapidly growing, discipline that addresses how language might be realized as a component within the general cognitive system, and how language is comprehended, produced, and represented in memory. It is an interdisciplinary effort, drawing on research and techniques from linguistics, psychology, neuroscience, and computer science, and utilizes a variety of methods to investigate the underlying representations and mechanisms that are involved in linguistic computations.
This course concentrates on (i) uncovering and characterizing the subsystems that account for linguistic performance, (ii) exploring how such subsystems interact, and whether they interact within a fixed order, and (iii) investigating how the major linguistic subsystems relate to more general cognitive mechanisms.
The Los Angeles Reading Corpus of Individual Differences (LARCID) is a corpus of natural reading and individual differences measures. The corpus is currently a feasibility pilot of eye tracking data collected from 15 readers. Five texts from public domain sources were included. In addition to the eye tracking measures, a battery of individual difference measures, along with basic demographic information, was collected in a separate session. Individual difference measures included the Rapid Automatized Naming, Reading Span, N-Back, and Raven’s Progressive Matrices tasks.
Pilot data, write up, and R-markdown files can be found on this Open Science Framework page. Comments welcome!
Robodoc is a Python program that automatically cleans eye tracking data of blinks and track losses. This new version improves usability and command line options. Learn more about this handy code here.
THE NPR Corpus scraper is a collection of Python programs built to crawl NPR and download transcripts into XML format, with links to audio files of radio interviews into a directory. It can be tweaked to crawl other news sites. Note: this tool requires a working knowledge of Python. To be posted with instructions soon!
The script downloads the Linguist List job posting archives for the years specified below. After some reformatting, it removes all but tenture track job postings and categorizes the jobs according to keywords listed in the posting. The method for categorization largely follows previous efforts; see the Language Log postings on the 2008 data, 2009 data, and 2009-2012 data.
git clone https://github.com/jaharris/linglist-scrape.git scrape
Simple to the point of trivial, this Ruby program writes results from Linger’s .dat files to a single file with the experiment name automatically appended along with the number of subjects run. Primarily for command line phobics. If Ruby is installed on Windows, simply place in the same folder as your .dat files, and then double click on the icon to run. Also works with Mac and Linux.