Information-theoretic characterization of uncertainty in manual control

Abstract

We present a novel approach for quantifying the impact of uncertainty in manual control, based on information and control theories and utilizing the information-theoretic capacity of empowerment, a task-independent universal utility measure. Empowerment measures, for agent-environment systems with stochastic transitions, how much influence, which can be sensed by the agent sensors, an agent has on its environment. It enables combining different types of disturbances, arising in human-machine systems (i.e. noise, delays, errors, etc.), into one single measure. We expand empowerment to manual control, demonstrate its application in the field of HCI and evaluate it in a user study. Results showed that empowerment is strictly monotonic in relation to the means of standard performance metrics total time off-target, perceived uncertainty, perceived performance and frustration, which suggests its potential in making theoretical predictions of other measures. Loss of empowerment implicated interesting trends in activity levels, which open a new area for future work. Results suggest the potential empowerment has in providing better theoretical foundations for the science of HCI.