posted on 2021-05-27, 20:06authored byAyoub Daliri
Purpose: The speech motor system uses feedforward and feedback control mechanisms that are both reliant on prediction errors. Here, we developed a state-space model to estimate the error sensitivity of the control systems. We examined (a) whether the model accounts for the error sensitivity of the control systems and (b) whether the two systems have similar error sensitivity.
Method: Participants (N = 50) completed an adaptation paradigm, in which their first and second formants were perturbed such that a participant’s /ε/ would sound like her /ӕ/. We measured adaptive responses to the perturbations at early (0–80 ms) and late (220–300 ms) time points relative to the onset of the perturbations. As data-driven correlates of the error sensitivity of the feedforward and feedback systems, we used the average early responses and difference responses (i.e., late minus early responses), respectively. We fitted the state-space model to participants’ adaptive responses and used the model’s parameters as model-based estimates of error sensitivity.
Results: We found that the late responses were larger than the early responses. Additionally, the model-based estimates of error sensitivity strongly correlated with the data-driven estimates. However, the data-driven and model-based estimates of error sensitivity of the feedforward system did not correlate with those of the feedback system.
Conclusions: Overall, our results suggested that the dynamics of adaptive responses as well as error sensitivity of the control systems can be accurately predicted by the model. Furthermore, our results suggested that the feedforward and feedback control systems function independently.
Supplemental Material S1. Adaptation responses in early, mid-, and late time windows.
Supplemental Material S2. Deviation response.
Supplemental Material S3. Qualitative comparison between the state-space model and simpleDIVA model.
Daliri, A. (2021). A computational model for estimating the speech motor system's sensitivity to auditory prediction errors. Journal of Speech, Language, and Hearing Research. Advance online publication. https://doi.org/10.1044/2021_JSLHR-20-00484
This work was supported by National Institutes of Health Grant R21 DC017563 (awarded to A. Daliri).