In the Brain and Machine Laboratory, they use human electrophysiology and computational modeling to study how reading is learned by children and how it works in adults. They interweave their physiological and computational work in a symbiotic fashion, such that they interpret their electrophysiological data in the context of their computational models, while their models are constrained by the reality of neurophysiology. For example, their computational modeling of the processes that link print with meaning takes into account several important computational properties of neurons, such as the separation of excitation and inhibition, and the differing time constants of different populations of inhibitory neurons, in service of producing simulations that continuously mirror the dynamics of the neural systems implementing reading. This work contrasts with more traditional reading models in that it strives to simulate reading processes as the occur, not after they have concluded, as is the case with models that simulate only behavioral reaction time or accuracy. Meanwhile, they use the Event-Related Potential (ERP) technique to collect and analyze massive data sets appropriate for computational modeling, such as the single-item ERP corpus, which includes stable ERPs representing the neural response to single words.
- PhD, MA, University of Illinois at Urbana-Champaign
- BS, Massachusetts Institute of Technology
- Computational modeling and human electrophysiology
- language comprehension
- reading development