Bharath Chandrasekaran, PhD | Neural systems in auditory and speech categorization

Speech signals are multidimensional, acoustically variable, and temporally ephemeral. A significant computational challenge in speech perception (and more broadly, audition) is categorization, that is, mapping continuous, multidimensional, and variable acoustic signals into discrete, behavioral equivalence classes. Despite the enormity of this computational challenge, native speech perception is rapid and automatic. In contrast, learning novel speech categories is effortful, and considered one of the most challenging categorization tasks for the mature brain. I will discuss three lines of ongoing research using multimodal neuroimaging, computational modeling, and behavioral training approaches: a) assessing the role the multiple cortical-subcortical systems underlying successful speech and nonspeech category learning in adulthood, b) assessing sources of individual differences in speech and nonspeech category learning, and c) designing optimal, neurobiologically-constrained training paradigms that reduce inter-individual differences in auditory learning success.