Extending Three Existing Models to Analysis of Trust in Automation: Signal Detection, Statistical Parameter Estimation, and Model-Based Control. (November 2019)

• Record Type: Journal Article
• Authors: Sheridan, Thomas B.
• Is Part Of: Human factors. Volume 61:Number 7(2019)
• Page Start: 1162
• Page End: 1170
• Publication Date: 2019-11
• Subjects: trust in automation -- automation -- expert systems -- cognitive modeling -- cognitive architectures -- cognition -- individual differences -- mathematical modeling -- methods and skills -- human–automation interaction; Human engineering -- Periodicals; 620.82
• DOI: 10.1177/0018720819829951
• Languages: English
• ISSNs: 0018-7208

Abstract

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.