Faculty highlight: Naomi Feldman
Naomi Feldman recently joined the Department of Linguistics as an Assistant Professor in computational psycholinguistics. Her research uses tools from statistics and machine learning to formalize questions about at how people learn and represent the structure of their language. For example, behavioral evidence indicates that perception of sounds is biased toward the centers of phonetic categories. Can we predict this bias by assuming that listeners are using knowledge about which sounds tend to occur most often? Infants learn to segment words around the same time that they learn phonetic categories. How would learners benefit by using information about which sounds occur together in words to constrain phonetic category acquisition? Naomi’s courses include two introductory graduate level courses, one in computational psycholinguistics and one in phonology.