Understanding the variability of pointing tasks with event-driven intermittent control

Abstract

Event-driven Intermittent control (IC) has been used as a framework to explain relevant aspects of human movement. The events, which are generated by states crossing predefined thresholds, give rise to state trajectories that mimic those of a continuous controller, by only using feedback information at event times. Here we present the results of using an optimisation approach to identify the parameters of an intermittent controller from experimental data, where users performed one dimensional mouse movements in a reciprocal pointing task. The results show that IC is able to reproduce both, the dynamical features and the variability of the pointing task across participants. We then introduce probabilistic elements in the IC framework in the form of Gaussian processes as an additional method to represent human movement variability.