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.
Cross-linguistically realistic psychological models of ‘lexical’ knowledge. Many psycholinguistic models take it as a default assumption that ‘lexical’ knowledge of your language takes the form of a set of triadic ‘words’ or ‘lexical items’ that map a conceptual unit to a syntactic unit to a form unit (or, for models that deny the existence of syntax, a set of binary mappings between a conceptual unit and a form unit). Comprehension or production processes are therefore often conceived as two-stage: one stage of identifying the lexical items, and another stage of representing relations between them. But these assumptions about the architecture of linguistic knowledge were developed by researchers who mainly worked on English and a small set of other Indo-European languages, and who were further biased by the white-space segmentation used in those writing systems. When one looks across a broader range of languages, the notion of a single domain for stored meaning, syntax, and form appears wholly unsustainable (Haspelmath 2017). If we instead take language production or comprehension to be a process of translating between three structured representations (meaning, syntax, phonology) with their own native primitives and relational structure, we can expect that stored linguistic knowledge will consist of separate sets of mappings from meaning to syntax and from syntax to phonological form, and that these stored mappings can hold between complex relational structures and not just atoms. We are working through the consequences that this ‘non-lexicalist’ architecture has for models of production (Krauska & Lau, 2023) and comprehension (Cuonzo & Lau, in progress), and some of our recent MEG work provides suggestive evidence about the ways in which comprehension of isolated ‘words’ is qualitatively different from comprehension of words in connected speech (Gaston et al. 2023).
Short-term referential indexes across sentences and scenes. The role of the angular gyrus in language comprehension has long been 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 temporal lobes, and play a crucial role in working memory computations over referents both in vision and in language, as well as ensuring appropriate episodic memory update. We also suspect that some of the sustained negativities observed in EEG language comprehension studies (such as the SAN and the NRef) might be related to sustained negativities associated with object indexes in visual working memory studies (see Cruz Heredia et al. (2021) for some speculation in this direction). We’re beginning to investigate how and when events and entities get indexed across the course of a sentence, and the extent to which these circuits are fully shared across language and vision (Yu & Lau, 2023).
Language and longer-term knowledge acquisition. The 20th century approach to interpretation as evaluating the truth conditions of sentence meanings with respect to a model has taught us a lot, but I think we’ve got to think more about how the language system hooks into the acquisition of knowledge about the world and the transformation of existing knowledge into different formats and data structures. Those things matter because multiple non-linguistic systems for representing the world were in place before language evolved in humans, so language was likely shaped by the need to interface with their formats. In the last few years I have been developing the hypothesis that the underlying subject-predicate organization of many natural language assertive sentences is due to the data structure of the hippocampus, a key structure for knowledge acquisition of a certain kind which originally evolved to code knowledge about locations in space for goal-directed navigation. I have an early, quixotic manuscript outlining this hypothesis, and I am working on a new one that’s more accessible to linguists and neuroscientists. The idea is very much related to beautiful early papers on subject and topic by Yuki Kuroda and Tanya Reinhart, as well as relatively unknown work by Leslie McPherson. I also think every linguist should be reading Sandeep Prasada, who has the best theory in psychology today about the non-linguistic cognitive data structures for representing kinds and their instances, and which I believe should be guiding our theories about what nouns mean and why they behave the way they do in language.
Neural basis of syntactic 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 (see review in Lau (2018)). 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. We now have a set of exploratory but interesting initial findings: Matchin et al. (2018), Lau & Liao (2018), Cruz Heredia et al. (2021). In our comprehension studies so far, many of the neural effects of structure we are observing appear to reflect semantic or conceptual structure. We are beginning to explore now whether ERP measures of simple phrase production may provide a better means of isolating syntactic representations.
Predictive mechanisms in language comprehension. 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, since then we’ve used the N400 to ask questions about what kinds of predictions are instantiated during language comprehension, and how (e.g. Liao & Lau (2020)). On a side note, I no longer believe that the N400 primarily reflects ‘activation’ of concepts or lexical items; I suspect it rather reflects processes involved in binding those concepts and the relations between them to a non-linguistic working memory representation. But a paper about that will probably have to wait another year or two. As I come to better appreciate the extremely different parsing problems posed by text and speech, I am now trying to shift more of my comprehension work over to connected speech. In a new collaboration with Jonathan Simon’s lab we are using MEG temporal-response function analysis methods to show how both global and local context predictive models for speech can co-exist in different brain regions and contribute to speech perception in parallel (Brodbeck et al. (2022)), and to examine whether these models are differentially affected in non-native speakers.
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), Pelzl et al. (2021)).