Extending Three Existing Models to Analysis of Trust in Automation: Signal Detection, Statistical Parameter Estimation, and Model-Based Control.

Abstract

The objective is to propose three quantitative models of trust in automation. Current trust-in-automation literature includes various definitions and frameworks, which are reviewed. This research shows how three existing models, namely those for signal detection, statistical parameter estimation calibration, and internal model-based control, can be revised and reinterpreted to apply to trust in automation useful for human-system interaction design. The resulting reinterpretation is presented quantitatively and graphically, and the measures for trust and trust calibration are discussed, along with examples of application. The resulting models can be applied to provide quantitative trust measures in future experiments or system designs. Simple examples are provided to explain how model application works for the three trust contexts that correspond to signal detection, parameter estimation calibration, and model-based open-loop control.