{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/82291"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/82291","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Supporting Coordination in Widely Distributed Cognitive Systems: The Role of Conflict Type, Time Pressure, Display Design, and Trust","abstract":"Overall, this study found that pilots were often unable to detect conflicts and thus failed to make informed and accurate decisions about proposed machine actions. Detection performance was particularly poor for conflicts related to clearance implementation, which were most likely to be missed if the automation did more than expected by the pilot. In addition to conflict type, the factors time pressure, high trust in air traffic control, and low trust in automated systems also contributed to poor detection performance. Based on a model of the cognitive processes involved in conflict detection, these findings are explained by the inability of pilots to generate expectations of system behavior to guide their monitoring, as well as the failure of the automation to provide effective feedback on its goals and actions in support of data-driven conflict detection. Possible approaches for improving human-machine coordination through more effective information representation and sharing are discussed.","abstract_html":"Overall, this study found that pilots were often unable to detect conflicts and thus failed to make informed and accurate decisions about proposed machine actions. Detection performance was particularly poor for conflicts related to clearance implementation, which were most likely to be missed if the automation did more than expected by the pilot. In addition to conflict type, the factors time pressure, high trust in air traffic control, and low trust in automated systems also contributed to poor detection performance. Based on a model of the cognitive processes involved in conflict detection, these findings are explained by the inability of pilots to generate expectations of system behavior to guide their monitoring, as well as the failure of the automation to provide effective feedback on its goals and actions in support of data-driven conflict detection. Possible approaches for improving human-machine coordination through more effective information representation and sharing are discussed.","abstract_has_math":false,"creators":["Olson, Wesley Allan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Nadine B. Sarter"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:40:10Z","date_published":"2015-09-25T20:40:10Z","updated_at":"2026-07-22T22:26:17Z","subjects":["Psychology, Cognitive"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI9953102"],"render_values":[{"text":"(MiAaPQ)AAI9953102","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/82291","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Nadine B. 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Detection performance was particularly poor for conflicts related to clearance implementation, which were most likely to be missed if the automation did more than expected by the pilot. In addition to conflict type, the factors time pressure, high trust in air traffic control, and low trust in automated systems also contributed to poor detection performance. Based on a model of the cognitive processes involved in conflict detection, these findings are explained by the inability of pilots to generate expectations of system behavior to guide their monitoring, as well as the failure of the automation to provide effective feedback on its goals and actions in support of data-driven conflict detection. Possible approaches for improving human-machine coordination through more effective information representation and sharing are discussed.","Made available in DSpace on 2015-09-25T20:40:10Z (GMT). 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Sarter"],"dc:creator":["Olson, Wesley Allan"],"dc:date":["2015-09-25T20:40:10Z","10000-01-01","1999"],"dc:description":["Overall, this study found that pilots were often unable to detect conflicts and thus failed to make informed and accurate decisions about proposed machine actions. Detection performance was particularly poor for conflicts related to clearance implementation, which were most likely to be missed if the automation did more than expected by the pilot. In addition to conflict type, the factors time pressure, high trust in air traffic control, and low trust in automated systems also contributed to poor detection performance. Based on a model of the cognitive processes involved in conflict detection, these findings are explained by the inability of pilots to generate expectations of system behavior to guide their monitoring, as well as the failure of the automation to provide effective feedback on its goals and actions in support of data-driven conflict detection. 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