Most motor acts involve coordinating the actions of more than one articulator to achieve a task goal. Nowhere is this more true than with speech production, in which the right speech sounds come out of our mouths only when we achieve a precise coordination of the simultaneous actions of many independent articulators. For example, if a prior soundís production leaves the tongue in a certain position, and the next sound to produce is /u/, speakers automatically round their lips by just the right amount to compensate for that tongue position, and /u/ is correctly produced. Thus, across different productions of /u/, tongue position and lip rounding vary widely, but they maintain a fixed relationship with each other. What principles govern the learning of such coordinations? This Wednesday, at Sensorimotor Journal club, I will present a recent review article that explores the suitability of optimal state feedback control models for explaining multi-articulator coordination:
Diedrichsen, J., Shadmehr, R., & Ivry, R. B. (2010). The coordination of movement: optimal feedback control and beyond. [doi: DOI: 10.1016/j.tics.2009.11.004]. Trends in Cognitive Sciences, In Press, Corrected Proof. (link to pdf of article)
Optimal control theory and its more recent extension, optimal feedback control theory, provide valuable insights into the flexible and task-dependent control of movements. Here, we focus on the problem of coordination, defined as movements that involve multiple effectors (muscles, joints or limbs). Optimal control theory makes quantitative predictions concerning the distribution of work across multiple effectors. Optimal feedback control theory further predicts variation in feedback control with changes in task demands and the correlation structure between different effectors. We highlight two crucial areas of research, hierarchical control and the problem of movement initiation, that need to be developed for an optimal feedback control theory framework to characterise movement coordination more fully and to serve as a basis for studying the neural mechanisms involved in voluntary motor control.