Active inference is a domain-general theory of brain functioning which reconceptualizes the perception-action interface in terms of a common process of minimization of sensory prediction errors (Adams et al., 2013; Friston et al., 2010). Such accounts have been extensively applied to the control of manual action guided by visual sensory feedback; however, they have received relatively little explicit attention in speech motor control. We present here the first detailed description of an active inference framework to auditorily-guided speech production. We compare the architecture of active inference models to existing computational models of speech motor control, and describe an active inference account of how compensation and adaptation result from perturbations of auditory feedback. We highlight several unique aspects of active inference, as well as emerging hypotheses for future empirical work. In particular, active inference accounts emphasise a role for proprioception in speech motor learning, and offer the potential to model the effects of other voices on speech production in phenomena such as phonetic convergence.
Bradshaw et al. (Tue,) studied this question.