Fitts' law as the outcome of a dynamic noise filtering model of motor control

An alternative, noise filtering model of motor control is presented proposing that the psychomotor system is an inherently noisy mechanical system for which spatial demands should be formulated in terms of a desired signal-to-noise ratio between goal-related propulsion of the limb (signal) and stochastic error (noise).

Abstract

Studies into the real-time evolution of goal-directed movements have been strongly dominated by the view that movement time and other kinematic features are a direct reflection of on-line computational processes. Well-known examples of this position try to explain the logarithmic relation between the movement time of aiming movements and their demanded endpoint accuracy, as described by Fitts in 1954, by the use of centrally controlled servomechanisms and submovements. In the present article it is doubted whether such a strict cognitive approach can provide real understanding of the kinematics of aiming movements. An alternative, noise filtering model of motor control is presented proposing that the psychomotor system is an inherently noisy mechanical system for which spatial demands should be formulated in terms of a desired signal-to-noise ratio between goal-related propulsion of the limb (signal) and stochastic error (noise). Adequate movements would, from this point of view, result from the optimization between the application of muscle forces to the limb system and the noise reducing effects of biomechanical properties, such as stiffness, viscosity, or friction due to surface contact. At a theoretical level, the present approach is exemplified in a simulation model which takes into account the stochastic nature of the motor unit recruitment process and the noise filtering properties of a biomechanical limb. Empirical data acquired in a simulation study, as well as published data on eye-ball control during looking tasks and data on axial pen pressure control in graphic tasks, lend support to the view that adaptive control of muscular co-contraction is a relevant degree of freedom for the control of spatial accuracy.