I work with faculty and students within the Linguistics department and across the Language Science Center (LSC) and the Neuroscience and Cognitive Science Program (NACS). We have a 32-channel Neuroscan EEG lab located in the department (which I co-direct with Colin Phillips), a 157-axial-gradiometer KIT MEG system located in a magnetically quiet lab at the Maryland Neuroimaging Center on campus (which I co-direct with Jonathan Simon), and a 3T MRI scanner also located at the MNC. If you are interested in getting involved in our research at the undergraduate or graduate level, just email me.
Referential indexing across sentences and scenes. The role of the angular gyrus in language comprehension has long been a bit of a puzzle, although it frequently ‘lights’ up when people are comprehending sentences or phrases (e.g. Matchin et al. 2019). I’ve become convinced that this activity reflects a limited capacity working memory system for referential indexes, just like the famous ‘object file’ system that supports visual working memory for scenes, in neighboring inferior parietal cortex. These inferior parietal indexes point to conceptual properties encoded by the anterior temporal lobes, and play a crucial role in working memory computations over referents as well as ensuring appropriate episodic memory update during sentence comprehension. We’re now beginning to investigate how and when events and entities get indexed across the course of a sentence.
Syntactic memory representation. An unsolved problem for cognitive neuroscience is how the brain encodes hierarchical relationships of the kind observed in even simple sentences of human language. This is not just a representational problem (how are these relationships represented in neural activity) but it’s a short-term memory problem, as language comprehension and production requires tracking an input or output that is physically realized as a linear sequence spanning several seconds. In 2018 I was awarded several years of NSF funding to investigate the contribution of sustained neural activity to syntactic representation with EEG, MEG, and fMRI. A couple exploratory but interesting initial findings: Matchin et al. (2018), Lau & Liao (2018).
The N400 effect and the nature of prediction in language. I worked for many years with a number of methodologies to better understand the generators of an extremely robust and reliable ERP response known as ‘the N400 effect’, so that I could use it as a more precise tool for getting inside the ‘black box’ of real-time language comprehension and interpretation. It turns out that this measure is not a great indicator of the process of evaluating real-world plausibility, but it is a very sensitive indicator of lexical or (more likely) conceptual predictability (Lau et al. (2016), Lau et al. (2013), Lau et al. (2008)). Therefore, we’ve used the N400 since then to ask questions about what kinds of predictions are instantiated during language comprehension, and how.
Linguistic knowledge and processing in late second language acquisition. In collaboration with the larger Language Science community here, I’ve begun to investigate the problem of real-time comprehension in a late-learned second language. Much of this work, led by Eric Pelzl, has used lexical tone as a case study to ask about how and why stored ‘lexical’ mappings for features like tone could be constrained by early language experience, and how online processing may fail to access knowledge that late learners do display on offline tests (Pelzl et al. (2018), Pelzl et al. (2020)).