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 excellent facilities for conducting cognitive neuroscience research: 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 very magnetically quiet lab at the Maryland Neuroimaging Center on campus (which I co-direct with Jonathan Simons), and a 3T MRI scanner also located at the MNC. We have a shared lab meeting on Fridays at noon (1108 Marie Mount Hall; all welcome) in which all kinds of experimental work from the department is presented.

If you are interested in getting involved in our research at the undergraduate or graduate level, please feel free to contact me.

Some Current Emphases

Syntactic combination and memory representation. A fascinating 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 is inherently a short-term memory problem, as comprehension and production of language requires tracking multiple components of an input or output that is physically realized as a linear sequence spanning several seconds. Despite several decades of work, research up to now has yielded only a few suggestive hints about how this might be accomplished. With the goal of beginning to crack this problem, we are pursuing both older approaches that may have been discarded too soon (EEG indices of syntactic working memory like the left anterior negativity) and newer approaches that show promise but need further validation (syntactic ‘constituent-size’ paradigms from Stan Dehaene’s group, and syntactic ‘constituent-rate’ paradigms from David Poeppel’s group).
Representative work: Matchin et al. (2017), Lau & Liao (2018).

The N400 effect and the nature of prediction in language. I have worked for many years with a number of methodologies to refine my understanding of the cognitive underpinnings of an extremely robust and reliable ERP response known as ‘the N400 effect’, so that I can 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 conceptual predictability. Therefore, our recent work has been able to begin to ask questions about what kinds of predictions are instantiated during language comprehension, and how.
Representative work: Lau et al. (2016), Chow et al. (2016), Lau et al. (2013), Lau et al. (2008)

Online processing mechanisms for interpretation. A number of current projects ask about the mechanisms by which we construct interpretations from sentences during online language comprehension. For example, what kind of antecedent information is reactivated in comprehending anaphora, and how quickly? How is argument role information encoded and incrementally processed during comprehension? Can we identify neural measures that dissociate  mechanisms used to navigate syntactic information vs discourse information in memory? Which aspects of sentence-level interpretation are highly incremental and which are less so? Much of this work is done in collaboration with Alexander Williams.
Representative work: Lago et al. (2017), Chow et al. (2015)

Sentence processing in late second language acquisition.  One of the joys of being part of the larger Language Science community here is that I have been able to join collaborations that bring some of the insights from basic research to bear on the problem of real-time comprehension in a late-learned second language. Some of the representative questions we’ve asked here are, how do online comprehension processes differ for late learners of a second language? Where and why do we observe dissociations between competence in offline tasks and in rapid, real-world conversation? How do native speakers adapt to variation and errors in the production of second language speakers?
Representative work: Pelzl et al. (2018)