{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T14:00:21Z","timestamp":1772200821279,"version":"3.50.1"},"reference-count":47,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2018,2,22]],"date-time":"2018-02-22T00:00:00Z","timestamp":1519257600000},"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":[[2018,6]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p> An experiment used workload capacity analysis to quantify automation usage strategy across different task difficulty and display format types in a speeded task. <\/jats:p><\/jats:sec><jats:sec><jats:title>Background<\/jats:title><jats:p> Workload capacity measures the efficiency of concurrent information processing and can serve as a gauge of automation usage strategy in speeded decision tasks. The present study used workload capacity analysis to investigate automation usage strategy while information display format and task difficulty were manipulated. <\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p> Subjects performed a speeded judgment task assisted by an automated aid that issued decision cues at varying onset times. Response time distributions were converted to measures of workload capacity. <\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p> Two variants of a workload capacity measure, C<jats:sub>zOR<\/jats:sub> and C<jats:sub>zAND<\/jats:sub>, gave evidence that operators moderated their own decision times both in anticipation of and following the arrival of the aid\u2019s diagnosis under difficult task conditions regardless of display format. <\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p> Assistance from an automated decision aid may cause operators to delay their own responses in a speeded decision task, producing joint response time distributions that are slower than optimal. <\/jats:p><\/jats:sec><jats:sec><jats:title>Application<\/jats:title><jats:p> Even when it renders its own judgments quickly and with high accuracy, an automated decision aid may slow responses from a user. Automation designers should consider the relative costs and benefits of response accuracy and time when choosing whether and how to implement an automated decision aid. <\/jats:p><\/jats:sec>","DOI":"10.1177\/0018720818759356","type":"journal-article","created":{"date-parts":[[2018,2,22]],"date-time":"2018-02-22T19:32:08Z","timestamp":1519327928000},"page":"527-537","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":14,"title":["Effects of Task Difficulty and Display Format on Automation Usage Strategy: A Workload Capacity Analysis"],"prefix":"10.1177","volume":"60","author":[{"given":"Yusuke","family":"Yamani","sequence":"first","affiliation":[{"name":"Old Dominion University, Norfolk, Virginia"},{"name":"Oregon State University, Corvallis"}]},{"given":"Jason S.","family":"McCarley","sequence":"additional","affiliation":[{"name":"Oregon State University, Corvallis"}]}],"member":"179","published-online":{"date-parts":[[2018,2,22]]},"reference":[{"key":"bibr1-0018720818759356","doi-asserted-by":"publisher","DOI":"10.1016\/j.apergo.2005.11.001"},{"key":"bibr2-0018720818759356","doi-asserted-by":"publisher","DOI":"10.1177\/0018720817700258"},{"key":"bibr3-0018720818759356","doi-asserted-by":"publisher","DOI":"10.1177\/001872089203400502"},{"key":"bibr4-0018720818759356","volume-title":"Aviation automation: The search for a human-centered approach","author":"Billings C. 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