# DIVA Meets EEG: Model Validation Using Formant-Shift Reflex
## 1 Introduction  
Effective oral communication is a basic and valued human daily activity [1, 2]. A key aspect of this function is the sensory-motor integration for the control of speech production, which has been shown to be critical for speech acquisition [3] and that is affected in speech and voice disorders including vocal hyperfunction [4, 5], stuttering and other disfluencies [6, 7], as well as in neurodegenerative diseases (Parkinson's disease) [8, 9]. Studies on sensory-motor integration have traditionally used the altered auditory feedback paradigm [3], i.e., vocal compensations elicited by perturbations in the intensity, frequency, and temporality of the auditory feedback of one's own voice. Auditory perturbations have been studied via two approaches: (1) some trials are perturbed randomly, generating a reflexive compensatory response on the part of the participant, and (2) the perturbation is gradual, inducing the adaptation to the perturbation response. Both methods consist of recording the participant's voice through a microphone, artificially altering speech formants or fundamental frequency, and playing back the altered vocalization to the participant in near real time through headphones [10]. Only a few studies (e.g., [11; 12; 13; 14; 15; 16; 17; 18; 19]) have been carried out regarding compensation in response to dormant perturbation. Research on speech production and acquisition has proposed several models of speech motor control [20]. For example, the Directions into Velocities of Articulators (DIVA) model has been developed using control theory concepts and anatomo-physiological information of brain networks. This model represents a unified neurocomputational framework that accounts for different aspects of speech production, including compensatory behaviors due to sensory feedback perturbations [21; 22]. Following predictive coding [3], the DIVA model uses sensory feedback information to track and correct transient deviations from the desired vocalization. This is achieved by generating error signals that modify previously learned speech-motor programs and reconfiguring the set of motor commands associated with the activation of the articulatory and laryngeal musculature. Therefore, the DIVA model has laid the foundation for a great deal of research regarding the role of auditory feedback on speech production and acquisition in both normal-hearing and hearing-impaired populations [23; 24; 25; 26; 27; 28]. Furthermore, it has become a valuable tool for assessing the etiology of stuttering, apraxia, and other speech pathologies [3; 29]. The theoretical bases of the DIVA model are supported by empirical work demonstrating increased activity of the prefrontal, Rolandic and superior temporal cortices in response to auditory feedback perturbations, which has been observed using different functional modalities [30; 31; 32; 33; 34; 35]. Nevertheless, the match between DIVA model predictions and experimentally acquired brain activity has been exclusively tested using functional magnetic resonance imaging (fMRI) [3; 18; 36]. It remains to be seen if a similar match is observed when brain activity is assessed through the electroencephalogram (EEG). It may be advantageous to the field of speech production to verify the DIVA model with EEG, as this neuroimaging modality is a direct measure of the electrical activity of the brain and allows for the representation of whole-brain oscillatory dynamics with high temporal resolution [37; 38]. Furthermore, EEG is a portable, low-cost technology with relatively broad availability. Considering the large number of EEG studies assessing vocal and speech behaviors in disturbed acoustic environments [39; 40; 41], an extension of the DIVA model to EEG may contribute to disentangling key neural mechanisms of sensorimotor integration for speech-motor control. Therefore, this study aims to investigate whether the brain activations intrinsic to DIVA match the brain activity maps estimated from EEG.