A neural-network model enabling sensorimotor learning: application to the control of arm movements and some implications for speech-motor control and stuttering.

Low-level motor control is defined as adapting an organism to the unique physical properties of its own limbs. The two-jointed arm serves to exemplify that effective low-level motor control demands a neurally medicated inversion of the dynamics, as well as of the kinematics, of a limb system. Reflex-like processing--that is, feedforword of either actual or predicted proprioceptive signals--is thereby assumed to be the principle of the dynamics control. As regards speech-motor control, the overall tool transformation is assumed to transform the force pattern of the articulatory muscles into speech sounds. Like the arm model, the vocal-tract transformation thus defined is also divided into two parts, namely the transformation relating the muscle forces to the mechanospatial states of the vocal tract (which is analogous to the forward dynamics including natural interarticulatory couplings), and the transformation relating the mechanospatial states to the speech sounds. Low-level speech-motor control, then, needs to invert both transformations, each of which can be learned by means of the self-imitation algorithm. Erroneous learning can fail to decouple interarticulatory coupling and therefore lead to abnormal feedback loops through the reflex-like operating neural network, which in turn can cause stuttering if audiophonatoric coupling is involved in learning.