Evidence for adaptive shoulder-elbow control in cyclical movements with different amplitudes, frequencies, and orientations

The evolution of joint dynamics and muscle patterning in the shoulder and elbow was studied for cyclical line drawing tasks at different frequencies, amplitudes, and orientations in the horizontal plane. Three main modes of control were identified: elbow-centered, shoulder-centered, and elbow-shoulder, each referring to the principal joints or joint combinations that were used to achieve the behavioral goals. The contribution of the shoulder joint was most prominent across the majority of movement orientations and largely paralleled changes in the dynamic (inertial) forces in the end effector (shoulder-centered control). The two joints either exchanged roles during the performance of the right diagonal movement (elbow-centered control) or shifted from a single-joint strategy to a dual-joint strategy during the performance of large amplitudes with low or medium cycling frequencies (shoulder-elbow control). These behavioral results support the existence of a modular control mode that allows the central nervous system to effectively tune motor commands to meet a broad variety of orientations, amplitudes, and frequencies. This refers to the emergence of a context-dependent control mode for the shoulder and elbow that optimizes the implementation of the underlying motor goals under a rich combination of spatial and temporal manipulations.     

Why we view the brain as a computer

The view that the brain is a sort of computer has functioned as a theoretical guideline both in cognitive science and, more recently, in neuroscience. But since we can view every physical system as a computer, it has been less than clear what this view amounts to. By considering in some detail a seminal study in computational neuroscience, I first suggest that neuroscientists invoke the computational outlook to explain regularities that are formulated in terms of the information content of electrical signals. I then indicate why computational theories have explanatory force with respect to these regularities:in a nutshell, they underscore correspondence relations between formal/mathematical properties of the electrical signals and formal/mathematical properties of the represented objects. I finally link my proposal to the philosophical thesis that content plays an essential role in computational taxonomy.