Understanding the stereotypical characteristics of human movement can better inform rehabilitation practices by providing a template of healthy and expected human motor control. Multiplicative noise is inherent in goal-directed movement, and plays an important role in computational motor control models to help support phenomena such as stereotypical kinematic profiles in time-constrained and unconstrained tasks. Most tasks are not carried out along an isolated degree-of-freedom (DOF), and modelling the contribution of noise can be difficult. In this work, we add a noise term proportional to the degree of simultaneity for multi-DOF tasks to approximate the contribution of system noise, and compare the simulation results against data from a 2-DOF experiment. With this approach, our model is able to explain previously observed motor phenomena including the presence of submovements in multi-DOF tasks, and the transition from simultaneousto sequential control of joints without the presence of visual feedback.
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