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

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.

journal_name

Hum Factors

journal_title

Human factors

authors

Sheridan TB

doi

10.1177/0018720819829951

subject

Has Abstract

pub_date

2019-11-01 00:00:00

pages

1162-1170

issue

7

eissn

0018-7208

issn

1547-8181

journal_volume

61

pub_type

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