A major research question our lab seeks to address is, broadly speaking, how do L2 speakers integrate multiple hierarchical forms of grammatical representation? We can think about language as being hierarchical; sounds map onto words, and these words combine to form sentences, from which we can derive logical and "social" or pragmatic meaning.
Some researchers debate how difficult it is to "integrate" or process all of these forms of language simultaneously, in "real-time" processing, in our second language. Language processing already places a burden on our mental resources like memory and attention. For example, in a conversation, we store all words in our short-term memory, and keep track of this throughout a conversation. Processing in our second language -- especially as adolescent or adult second language learners -- may be even more demanding. When it comes to integrating multiple forms of language (linguistic representation) at the same time -- especially in our second language -- how mentally demanding is this, really?
In exploring the above (broad) research question, we additionally seek to explore the factors that may "help" or "hinder" language processing in our second language. For example, how does our proficiency level, the age at which we learn or acquire our second language, or how often we are exposed to our second language, facilitate the ease with which we process language?
We explore these questions by looking at a variety of linguistic structures, and by using methods such grammaticality and acceptability judgment tasks (i.e., how grammatical does one judge a sentence) as well as reading time paradigms (i.e., how fast does one read in milliseconds, as an index of real-time processing).
Statistics and data-wrangling is often seen by newcomers as an intimidating task to take on, but with experience, I believe statistics and data-wrangling can be a fun and exciting challenge, and is an empowering skill to have. More than the challenge and empowerment it offers to students, it is an invaluable and oftentimes necessary skill to learn as a psycholinguist or cognitive psychologist. We are all always developing our data skills!
To help undergraduate and graduate psycholinguists and cognitive psychologists, I aim to develop an open-access set of data wrangling and analysis tutorials for common experimental platforms in psycholinguistics and cognitive psychology, such as PCIbex, Qualtrics, EPrime, Gorilla, etc. I aim for these platforms to walk undergraduate and graduate students who are newcomers to data through the data wrangling and analysis process. My goal is for these tutorials to provide detailed explanations of useful code to use in data analyses. Additionally, I aim for these tutorials to provide illustration of what not to code, with explanations for why the code does not run, and of what different error messages mean.
Through such a tutorial, I hope to provide accessible guides that can serve as a gateway to data wrangling and analysis in the psychological sciences.