Computational motor control as a window to understanding schizophrenia.

In addition to mental disorders such as attention, emotion, delusions, hallucinations, and difficulties in social skills, the patients with schizophrenia exhibits significant abnormality in sensorimotor perception and control. To seek a neurobiological cause of the heterogeneous symptoms in schizophrenia, we focused on the impaired inference mechanism of the self-agency of the schizophrenia's brain where the sensory outcome generated by the self-initiated action was misattributed to the other agent's action. By developing a novel computational model of agency experience using a Bayesian decision making framework, we united the computational mechanisms of agency and motor control via internal model: a model for one to predict the sensory consequence of action. Our theory based on optimal feedback control with Kalman filtering successfully predicted a variety of schizophrenia's motor abnormalities assuming a deformed internal model. To discuss the plausibility of these model predictions, we reviewed literature that might support these predictions. We further proposed some experiments that potentially examine the proposed model of schizophrenia. Our approach in investigating a problem of mind by projecting it on the coordinates system of the embodiment effectively shed light on a central neuropathology of this disease that had been latent behind the observed behaviors.