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

Objective: The objective is to propose three quantitative models of trust in automation. Background: Current trust-in-automation literature includes various definitions and frameworks, which are reviewed. Method: 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. Results: The resulting reinterpretation is presented quantitatively and graphically, and the measures for trust and trust calibration are discussed, along with examples of application. Conclusion: The resulting models can be applied to provide quantitative trust measures in future experiments or system designs. Applications: 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.

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