The goal of speaking is to produce the right sounds that convey an intended message. Accordingly, speakers monitor their sound output and use this auditory feedback to further adjust their speech production. Drs. Nagarajan and Houde hypothesized that the brain not only generates the motor signals that control the speech production but also generates a prediction of what this speech should sound like and performs an on-going comparison during speaking in order to dynamically adjust speech production. Whole-brain magnetoencephalographic imaging (MEG-I) experiments will be performed to monitor subjects' auditory neural activity as they hear themselves speak. The first tests task and feature specificity of the feedback prediction. If the prediction encodes only task-relevant acoustic features (i.e., pitch for a singing task), then auditory cortical activity will depend only on the acoustic goal for that task. The second tests the importance of categorical identity in the process of comparing feedback with the prediction. If feedback is altered enough to change the meaning of a word (e.g., when /bad/ is altered to /dad/), this is expected to have a much larger impact on auditory cortical activity than non-categorical alterations. These experiments are expected to improve our understanding of how the brain uses auditory feedback to maintain accuracy in speech production.
The proposed research activity also has broader impact. First, these expected findings may further contribute to a better understanding of and effective treatments for speech dysfunctions. For example, accurate models of brain networks used to control speaking form the basis for testable hypotheses about neural origins of speech disorders such as stuttering or spasmodic dysphonia. Second, the research project will provide a special opportunity to train and educate graduate students and postdoctoral fellows in the use of real-time speech alteration and MEG-I techniques which are only available at very few US institutions. Outreach with collaborators at San Francisco State University, and to the San Francisco Unified School district through the NSF funded Science Education Partnership (SEP) program will provide research experience to their students, specifically students from socioeconomically disadvantaged minorities who are under-represented in the sciences. The research team will also participate in the big data sharing effort by making the data and analysis tools available to support efforts to make use of real data in the teaching of STEM-related courses and to enable participation in discovery science by those who would otherwise have no access to such data.
