Modeling Sensorimotor Adaptation the behavioral responses alone. Overall, SimpleDIVA offers new insights into speech and voice motor control and has the potential to inform future directions of speech rehabilitation research in disordered populations. Simulation software, including an easyto-use graphical user interface, is publicly available to facilitate the use of the model in future studies.

Theoretical models of speech production suggest that the speech motor system (SMS) uses auditory goals to determine errors in its auditory output during vowel production. This type of error calculation indicates that within-speaker production variability of a given vowel is related to the size of the vowel’s auditory goal. However, emerging evidence suggests that the SMS may also take into account perceptual knowledge of vowel categories (in addition to auditory goals) to estimate errors in auditory feedback. In this study, we examined how this mechanism influences within-speaker variability in vowel production. We conducted a study ( n = 40 adults), consisting of a vowel categorization task and a vowel production task. The vowel categorization task was designed—based on participant-specific vowels—to estimate the categorical perceptual boundary (CPB) between two front vowels (/ε/ and /æ/). Using the vowel production data of each participant, we calculated a variability-based boundary (VBB) located at the “center of mass” of the two vowels. The inverse of the standard deviation of a vowel distribution was used as the “mass” of the vowel. We found that: (a) categorical boundary was located farther from more variable vowels; and (b) the calculated VBB (i.e., the center of mass of the vowels) significantly and positively correlated with the estimated categorical boundary ( r = 0.912 for formants calculated in hertz; r = 0.854 for formants calculated in bark). Overall, our findings support a view that vowel production and vowel perception are strongly and bidirectionally linked.

Purpose We continuously monitor our speech output to detect potential errors in our productions. When we encounter errors, we rapidly change our speech output to compensate for the errors. However, it remains unclear whether we adjust the magnitude of our compensatory responses based on the characteristics of errors. Method Participants ( N = 30 adults) produced monosyllabic words containing /ɛ/ (/hɛp/, /hɛd/, /hɛk/) while receiving perturbed or unperturbed auditory feedback. In the perturbed trials, we applied two different types of formant perturbations: (a) the F1 shift, in which the first formant of /ɛ/ was increased, and (b) the F1–F2 shift, in which the first formant was increased and the second formant was decreased to make a participant's /ɛ/ sound like his or her /æ/. In each perturbation condition, we applied three participant-specific perturbation magnitudes (0.5, 1.0, and 1.5 ɛ–æ distance). Results Compensatory responses to perturbations with the magnitude of 1.5 ɛ–æ were proportionally smaller than responses to perturbation magnitudes of 0.5 ɛ–æ. Responses to the F1–F2 shift were larger than responses to the F1 shift regardless of the perturbation magnitude. Additionally, compensatory responses for /hɛd/ were smaller than responses for /hɛp/ and /hɛk/. Conclusions Overall, these results suggest that the brain uses its error evaluation to determine the extent of compensatory responses. The brain may also consider categorical errors and phonemic environments (e.g., articulatory configurations of the following phoneme) to determine the magnitude of its compensatory responses to auditory errors.