The Temporal Dynamics of Spoken Word Recognition

Spoken language comprehension is a fast, dynamic process. Listeners must recognize speech sounds that arrive rapidly, and at the same time, they must deal with enormous acoustic variability caused by a variety of factors, such as differences between talkers' voices and dialects. Despite this, listeners are remarkably good at speech recognition. How do human listeners do this so well? Toscano will describe work using neuroimaging, electrophysiology, and eye-tracking to examine spoken language comprehension as it happens in real time. These techniques provide a millisecond-scale window into the processes that allow us to understand speech, revealing that listeners track continuous acoustic changes in the sound signal and recognize speech continuously in time rather than in discrete stages. Together, these findings suggest that we must reconsider many of the assumptions in current models of spoken word recognition, and they offer insights into how we can improve computer-based speech recognition systems.

Joe Toscano received his Ph.D. in cognitive psychology in 2011 from the University of Iowa. His research focuses on speech perception and spoken word recognition and examines how listeners map sounds onto words, how they adapt to different accents and listening conditions, and how they learn to do this. His work uses several techniques that allow us to study the time-course of speech processing (eye-tracking, optical neuroimaging, and electrophysiology), as well as computational modeling approaches that simulate language learning and game-based tasks that allow us to investigate natural language use in the lab. His research has been published in top-tier journals, including Psychological Science, Psychonomic Bulletin & Review, and Cognitive Science. Joe is also interested in science outreach efforts, and he helped create the Chambana Science Café to allow researchers from Illinois to discuss their work with the public.

Principle and Applications of Magnetomotive Optical Coherence Tomography (MM-OCT)

MM-OCT is a functional extension of OCT which utilizes magnetic nanoparticles (MNPs) as contrast agent. MM-OCT imaging in conjunction with targeted MNPs/magnetic microspheres is useful for detecting disease in human (e.g., atherosclerotic lesion, breast cancer, etc.) and for assessing the viscoelastic properties of the surrounding tissues. Kim will talk about the principle and applications of magnetomotive optical coherence tomography.

Jongsik Kim completed his Ph.D in bioengineering at the University of Pittsburgh. He was formerly a postdoctoral research associate in the Center for Ultrasound Molecular Imaging and Therapeutics at the University of Pittsburgh Medical Center. Jongsik is currently working with Stephen A. Boppart on improving magnetomotive optical coherence tomography (MM-OCT) system. MM-OCT has been developed by Boppart and his colleagues.