Physical modeling applies to physiology, too

Abstract

Abstract. A physical model was utilized to show that the neural system can memorize a target position and is able to cause motor and sensory events that move the arm to a target with more accuracy. However, this cannot indicate in which coordinates the necessary computations are carried out. Turning off the lights causes the error to increase which is accomplished by cutting off one feedback path. The geometrical properties of arm kinematics and the properties of the kinesthetic and visual sensorial systems should be better known before inferences about higher levels of processing can be drawn. An acceptable model for a physical system should be able to account for the observations in a wide range of situations and in a manner that does not depend on its own representation but only on that of the represented system. Coincidentally, this necessity is currently receiving a fair bit of attention in the robotics research, although, at least in the case of mechanical systems, the question was settled satisfactorily by the physicists and the mathematicians of the last century. Bruynincks (1991) has listed four types of invariance which are required to construct a theory with physical relevance. Although discussed in an engineering context, these invariances are also needed for physiological models. These are: invariance (1) to a change in reference frame, (2) to a change in physical units, (3) to a change in mathematical representation, and (4) to a change in arbitrary choices. It is unfortunate that the model proposed by Flanders et al. fails all four requirements. In other terms, one could construct uncountable variations of the proposed model that could equally explain the data. The question of reference frame invariance is of course of direct relevance to the subject matter since the essence of the proposed model is to deal with coordinate changes. In the study and in this commentary, the human arm is