Mechanisms of sensorimotor adaptation in a hierarchical state feedback control model of speech
Fig 4
Design C implemented with adaptive UKF (AUKF).
A: The task estimator (Design C) implemented with AUKF. During perturbation trials, if the squared auditory prediction errors inversely weighted by noise covariance (ϵ) were larger than a given threshold (γ), larger correction signals were generated from the Kalman filter for the current and the following time step. This in turn allowed faster and larger updates in the articulatory-to-task model. B: Simulations are shown with data from the control group in Kim & Max [19] in the top row, and with the native English speakers in Mitsuya et al. [61] in the bottom row. In all three simulations, the AUKF (blue solid line) produced more realistic simulations compared to the non-adaptive UKF (green dashed line). C: The model also generated F2 changes even when only F1 was perturbed. The simulated adaptation was similar to the healthy control group’s data in [62]. Perturbed trials are indicated by yellow shaded areas in both B and C.