I am an associate professor at UCLA in the Department of Linguistics, and advise 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 was held virtually at UC Irvine.
As a person who stutters, I’m proud to serve on the Board of Directors of the Stuttering Scholarship Alliance, a non-profit dedicated to facilitating access to acceptance-based speech therapy to people in underserved communities, as well as providing resources on disability rights for students who stutter and education for speech-language pathologists in training.
Finally, I regularly participate in 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 core areas of psycholinguistics include language acquisition, language perception, language production, language comprehension, language and the brain, and language disorders and damage. This course emphasizes depth over breadth, and so we will not delve into all of these topics. Instead, we will be focusing on just two areas of research: mental representations and processing of lexical units, and sentence comprehension. We start with the basics of lexical access and decision, exploring various models of the processes involved. We then move to an overview of classic models of sentence processing which vary according to a number of related properties such as the modularity/interactionism of information channels and the serialism/parallelism of processing. Finally, we discuss several topics in current and classical language research, including the filler-gap dependencies, semantic processing, and sentence production.
Linguistic research has always placed a high premium on data in various forms: native-speaker introspection, fieldwork, corpora, judgment studies, reaction time studies, eye movements, and electrophysiology, to name a few. As the empirical base of linguistics had evolved, community- wide standards for data collection and analysis have become increasingly important. This course provides a practical, hands-on introduction to research design and analysis, with an emphasis on experimental data collection, study design, and proper statistical analysis. Assuming no programming, statistics, or experimental background, the course will provide you with the necessary conceptual and practical tools for carrying out experimental research.
By the end of the course, you should be able to design an experiment that uses an appropriate method and that minimizes confounds, for which you would be able to apply appropriate statistical analysis techniques. Students will work in groups to design an experiment or corpus study to be presented at the end of the course, on an issue relevant to their own research interests.
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.
This tutorial shows how to use R to access the US Census to visualize language families spoken in the United States. The interactive Shiny app below illustrates how various languages are distributed in California according to the 2012 American Community Survey.
git clone https://github.com/jaharris/Linguistic_Diversity_CA.git
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 tenure 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
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.