{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T22:30:20Z","timestamp":1782426620273,"version":"3.54.5"},"reference-count":26,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2019,2,27]],"date-time":"2019-02-27T00:00:00Z","timestamp":1551225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Hum Factors"],"published-print":{"date-parts":[[2019,11]]},"abstract":"<jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>The objective is to propose three quantitative models of trust in automation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Current trust-in-automation literature includes various definitions and frameworks, which are reviewed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method:<\/jats:title>\n                    <jats:p>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\u2013system interaction design.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The resulting reinterpretation is presented quantitatively and graphically, and the measures for trust and trust calibration are discussed, along with examples of application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The resulting models can be applied to provide quantitative trust measures in future experiments or system designs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Applications:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/0018720819829951","type":"journal-article","created":{"date-parts":[[2019,2,27]],"date-time":"2019-02-27T19:11:28Z","timestamp":1551294688000},"page":"1162-1170","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":35,"title":["Extending Three Existing Models to Analysis of Trust in Automation: Signal Detection, Statistical Parameter Estimation, and Model-Based Control"],"prefix":"10.1177","volume":"61","author":[{"given":"Thomas B.","family":"Sheridan","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology, Lexington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,2,27]]},"reference":[{"key":"bibr1-0018720819829951","doi-asserted-by":"publisher","DOI":"10.1002\/9780470519851"},{"key":"bibr2-0018720819829951","doi-asserted-by":"publisher","DOI":"10.1177\/0018720815582261"},{"key":"bibr3-0018720819829951","doi-asserted-by":"publisher","DOI":"10.1109\/TMMS.1969.299895"},{"issue":"1","key":"bibr4-0018720819829951","first-page":"1","volume":"4","author":"Ferrell W. 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