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

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 state trajectories that mimic those continuous controller, only using feedback information at event times. Here we present the results an optimisation approach identify parameters intermittent controller from experimental data, where users performed one dimensional mouse movements in reciprocal pointing task. show IC is able reproduce both, dynamical features and variability task across participants. We then introduce probabilistic elements form Gaussian processes additional method represent movement variability.