Research

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, 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.

Current Work

Getting rid of ‘words’ and traditional ‘morphemes’ in psychological models. Everybody who works on natural language knows at some level that linguistic knowledge isn’t a neat set of ‘words’ or ‘items’. So it’s a real problem that the dominant terminology in psychology of language is still framed this way–our scientific terms don’t match our current scientific beliefs. We are working through the consequences for existing models of production and comprehension and developing new experimental and theoretical approaches that benefit from getting rid of ‘words’ and traditional ‘morphemes’. 

  • Lau, E. (submitted) Lexical paths. pdf
  • Cuonzo, C., MacDonald, A., & Lau, E. (2026). Blueberries and fingerprints: ERP insights into compound structure in production. Cognitive Neuropsychology. pdf
  • Krauska, A. & Lau E. (2023). Moving away from lexicalism in psycho- and neuro- linguistics. Special issue on ‘Syntax, the brain, and linguistic theory’ in Frontiers in Language Sciences. pdf
  • Gaston, P., Brodbeck, C., Phillips, C., Lau, E. (2023). Auditory word comprehension is less incremental in isolated words. Neurobiology of Language. pdf
  • Yu, X., Mancha, S., A. Tian, X., & Lau, E. Shared neural computations for syntactic and morphological structures: evidence from Mandarin Chinese. bioarxiv

Short-term referential indexes across scenes and sentences. The cognitive system for indexing and tracking objects in the current context has been studied a lot in vision, and we know a fair amount about how it works. We’re interested in how this ability relates to the ability to track and update things that are being discussed in the current conversation, what are sometimes called ‘discourse referents’ (we know this includes a temporary representation of the current context, because hippocampal patients who can’t encode new long-term memories can hold relatively normal conversations). We believe understanding the relationship between indexical tracking across vision and language is one way of getting clearer on the distinction between language and thought.

  • Yu, X. & Lau, E. (2023). The binding problem 2.0: beyond perceptual features. Cognitive Science. pdf
  • Yu, X. & Lau, E. (2025). Same set of visual pointers for biological and non-biological objects in working memory. Visual Cognition. pdf
  • Yu, X. & Lau, E. (2025). A finite set of content-free pointers in visual working memory: MEG evidence. NeuroReport. pdf
  • Yu, X., Li, J., Zhu, H., Tian, X., & Lau, E. (2024). Electrophysiological hallmarks for event relations and event roles in working memory. Frontiers in Neuroscience. pdf

Noun meanings, and conceptual structure. After a long time trying to understand reference in language, I finally learned about what are sometimes called ‘sortalist’ approaches, e.g. in the work of Peter Geach, John Macnamara, Mark Baker, and psychologist Sandeep Prasada. This work recognizes that nouns seem to be special linguistic devices for dealing with reference to individuals, contrasting with dominant approaches in linguistic semantics and psychology which group together nouns and adjectives (and/or their corresponding concepts) as ‘predicates’, ‘properties’, or ‘features’. We think this is the key for resolving longstanding challenges in integrating reference and semantic interpretation in psychological and neural models of language. We are working on restating the classic arguments for this approach and developing new ones from linguistic argument and cognitive experimentation.

Language and longer-term knowledge acquisition. In the last few years I have been developing the hypothesis that the subject-predicate organization of natural language sentences was shaped by the need to map to the data structure of the hippocampus, a neural circuit which originally evolved to code knowledge about locations in space for goal-directed navigation. This idea relates to beautiful early papers on subject and topic by Yuki Kuroda and Tanya Reinhart, as well as relatively unknown work by Leslie McPherson. I have speculated (not yet in print!) that this may contribute to the explanation of the subject/object relative clause asymmetry and center-embedding difficulty.

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. In 2018 I was awarded NSF funding to investigate the contribution of sustained neural activity to syntactic representation with EEG, MEG, and fMRI. In visual working memory, tracking small numbers of individuated objects over a delay period is associated with a sustained neural response. We investigated whether an analogous sustained response is associated with representing some kind of linguistic information, as has sometimes been reported. Our results, though not conclusive, were largely negative. We either observed short-lived effects of linguistic structure, or we observed sustained effects that were modulated by meaning parameters, as would be expected if they reflected non-linguistic conceptual or referential representations. This project also led us to realize that investigating the neural implementation of structured language representation–rather than the neural basis of structured language behavior–logically requires rejecting the neural-net architecture that has been the foundation for neuroscience across the past 80 years. As necessary as that may be, it’s a pretty tall order for a little band of neurolinguists.

  • Cruz Heredia, A., Dickerson, B., Lau, E. (2021). Towards understanding sustained neural activity across syntactic dependencies. Neurobiology of Language. pdf
  • Matchin, W., Brodbeck, C., Hammerly, C., & Lau, E. (2018). The temporal dynamics of structure and content in sentence comprehension: Evidence from fMRI-constrained MEG. Human Brain Mapping. pdf
  • Lau, E. & Liao, C-H. (2017). Linguistic structure across time: ERP responses to coordinated and uncoordinated noun phrases. Language, Cognition, and Neuroscience. pdf
  • Lau, E. (2018). Neural indices of structured sentence representation: State of the art. In Psychology of Learning and Motivation, Vol. 68. pdf

Predictive mechanisms in language comprehension. I worked for many years 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. 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. Nowadays I no longer believe that the N400 primarily reflects ‘activation’ of concepts or lexical items; I suspect it rather reflects information transmission between units needed to update estimates of the cause of the sensory data and make those estimates available for further computations. 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 speech. In collaboration with Jonathan Simon’s lab we have used 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.

  • Brodbeck, C., Bhattasali, S., Cruz Heredia, A., Resnik, P., Simon, J.Z., & Lau, E. (2022). Parallel processing in speech perception with local and global representations of linguistic context. eLife. full article
  • Liao, C.-H. & Lau, E. (2020). Enough time to get results? An ERP investigation of prediction with complex events. Language, Cognition and Neuroscience, 1-21. pdf
  • Lau, E. & Namyst, A. (2019). fMRI evidence that left posterior temporal cortex contributes to N400 effects of predictability independent of congruity. Brain and Language. pdf
  • Lau, E., Namyst, A., Fogel, A., Delgado, T. (2016). A direct comparison of N400 effects of predictability and incongruity in adjective-noun combination. Collabra.
  • Lau, E.F. & Nguyen, E.T. (2015). The role of temporal predictability in semantic expectation: An MEG investigation. Cortex.
  • Lau, E.F., Weber, K., Gramfort, A., Hamalainen, M., & Kuperberg, G. (2014). Spatiotemporal signatures of lexical-semantic prediction. Cerebral Cortex.
  • Lau, E.F., Gramfort, A., Hamalainen, M., & Kuperberg, G. (2013). Automatic semantic facilitation in anterior temporal cortex revealed through multimodal neuroimaging. Journal of Neuroscience.
  • Lau, E.F., Holcomb, P.J., & Kuperberg, G.R. (2013). Dissociating N400 effects of prediction from association in single word contexts. Journal of Cognitive Neuroscience.
  • Lau, E.F., Almeida, D., Hines, P., Poeppel, D. (2009). A lexical basis for context effects: evidence from the N400. Brain and Language.
  • Lau, E.F., Phillips, C., & Poeppel, D. (2008). A cortical network for semantics: (de)constructing the N400. Nature Reviews Neuroscience.

Linguistic knowledge and processing in late second language acquisition.  In collaboration with the larger Language Science community here, some of my work has investigated the problem of real-time comprehension in a late-learned second language. Much of this work, led by Eric Pelzl, 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. Nowadays I can see new connections between this earlier work and our current interest in getting away from simplistic notions of ‘words’; lexical tone is one of the many phenomena that belie an atomistic approach to stored phonological wordforms as a collection of phonemes, and force us to consider seriously what type of structured relations they represent (as in Bill Idsardi’s ‘phonological graph’ PFE approach).

  • Thorburn, C., Karunathilake, I.M.D., Dixon, L., Lau, E., Simon, J. (submitted) Neural recordings of continuous speech reveal robust signatures of prediction in second language learners of English. bioarxiv
  • Liao, C.-H., & Lau, E. (2023). ERP sensitivity to subcategorization violations in L2 learners. Second Language Research. pdf
  • Pelzl, E., Lau, E., Guo, T., DeKeyser, R. (2021). Advanced second language learners of Mandarin show persistent deficits for lexical tone encoding in picture-to-word form matching. Frontiers in Communication. full article
  • Pelzl, E., Lau, E. F., Jackson, S. R., Guo, T., & Gor, K. (2021). Behavioral and neural responses to tone errors in foreign-accented Mandarin. Language Learning, 71(2), 414-452. pdf
  • Pelzl, E., Lau, E., Guo, T., DeKeyser, R. (2020). Even in the best-case scenario L2 learners have persistent difficulty perceiving and utilizing tones in Mandarin: Findings from behavioral and ERP experiments. Studies in Second Language Acquisition. pdf
  • Pelzl, E., Lau, E., Guo, T., & DeKeyser, R. (2018). Advanced Second Language Learners’ Perception of Lexical Tone Contrasts. Studies in Second Language Acquisition. pdf

Finally, I believe that Randy Gallistel, Hessam Akhlaghpour, and Sam Gershman are correct that much of the information storage in the brain is being done discretely inside single cells, and that neural spiking is a code for transmitting this information between units, not the representation itself. Harkening back to Ramon y Cajal’s single neuron doctrine, we need to stop thinking about the brain as a unified ‘net’ of dumb automaton units, and instead think about each neuron as an independent organism (as it originally was, evolutionarily), running its own computations and storing and exchanging its results with others. The faster we all work to advance that conceptual revolution in neuroscience, the better our chances of getting to correctly interpret cogneuro data in our lifetime.