PROJECT SUMMARY/ABSTRACT The speech motor system shows a remarkable ability to quickly and efficiently learn movements based on auditory feedback. One common manifestation of such auditory feedback-based learning is auditory-motor adaptation, which can be observed in everyday speaking situations that involve changes in speech acoustics, for which speakers must learn to compensate in order to maintain intelligible speech. Despite its fundamental nature in maintaining and fine-tuning speech production, however, there exist crucial knowledge gaps in the understanding of auditory-motor adaptation. In fact, even basic mechanisms in adaptation, such as which factors drive the learning (i.e., adapting to compensate for auditory perturbations like formant shifts) and unlearning (i.e., returning to the baseline movements upon the removal of the previously applied auditory perturbation) processes remain largely unclear. Previous studies have hypothesized that auditory-motor adaptation may result from minimizing auditory prediction errors–the discrepancies between predicted auditory consequences of motor commands and actual auditory feedback. To date, however, this fundamental hypothesis remains surprisingly underinvestigated. Hence, this proposed research combines computational (Aim 1) and neurophysiological (Aim 2) approaches to directly examine whether auditory prediction errors are the primary factor driving speech auditory-motor adaptation. As a part of the first specific aim, several new versions of a computational model of speech motor control, Feedback Awareness Control of Tasks in Speech (FACTS) will be developed to simulate auditory-motor adaptation. The new versions of FACTS will be tested and validated in order to examine the mechanistic role of auditory prediction errors in adaptation. Aim 2 will use magnetoencephalography imaging to examine Speaking-Induced Suppression (SIS)—suppression of auditory responses to self-produced speech compared to the responses to passively heard speech—which is thought to represent auditory prediction errors. Here, whole-brain data driven analyses of SIS will be examined during a series of auditory-motor adaptation tasks. In both aims, auditory-motor adaptation to both spectral perturbations (i.e., adaptation to perturbed formant frequencies) and temporal perturbations (i.e., adaptation to lengthened voice onset time) will be examined. Together, this combined computational modeling and neurophysiological approach will offer key mechanistic insights into the neural basis of auditory-motor adaptation. The immediate outcome may provide the first direct evidence for the critical role of auditory prediction errors in auditory-motor adaptation, with profound implications for theoretical and computational models of speech motor control. The broader impact of the work may establish a solid foundation for novel treatment strategies to improve speech motor treatment efficacy in clinical populations.