NCN@Illinois Video Team
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Accurate vocal production relies on several factors including sensory feedback and the ability to predict future challenges to the control processes. Repetitive patterns of perturbations in sensory feedback by themselves elicit implicit expectations in the vocal control system regarding the timing, quality and direction of perturbations. In the present study, the predictability of voice pitch-shifted auditory feedback was experimentally manipulated. A block of trials where all pitch-shift stimuli were upward, and therefore predictable was contrasted against an unpredictable block of trials in which the stimulus direction was randomized between upward and downward pitch-shifts. It was found that predictable perturbations in voice auditory feedback led to a reduction in the proportion of compensatory vocal responses, which might be indicative of a reduction in vocal control. The predictable perturbations also led to a reduction in the magnitude of the N1 component of cortical Event Related Potentials (ERP) that was associated with the reflexive compensations to the perturbations. We hypothesize that formation of expectancy in our study is accompanied by involuntary allocation of attentional resources occurring as a result of habituation or learning, that in turn trigger limited and controlled exploration-related motor variability in the vocal control system.
Chuck Larson and his lab study the neural mechanisms controlling the voice and the larynx. They have found that when people hear their own voice through earphones, and when the voice pitch through the earphones is unexpectedly changed upwards or downwards, people automatically adjust the pitch of their voice. This phenomenon indicates that there is a close coupling between the auditory system's monitoring of voice and the motor system for adjusting the laryngeal system for voice output. Our recent research has focused on the brain mechanisms underlying the interaction between auditory feedback and voice control. We use EEG and fMRI techniques along with connectivity modeling to identify the timing and interactions between the various brain locations involved in these processes.
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