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Attribution Biases and Trust Development in Physical Human-Machine Coordination: Blaming Yourself, Your Partner or an Unexpected EventJanuary 2019 (has links)
abstract: Reading partners’ actions correctly is essential for successful coordination, but interpretation does not always reflect reality. Attribution biases, such as self-serving and correspondence biases, lead people to misinterpret their partners’ actions and falsely assign blame after an unexpected event. These biases thus further influence people’s trust in their partners, including machine partners. The increasing capabilities and complexity of machines allow them to work physically with humans. However, their improvements may interfere with the accuracy for people to calibrate trust in machines and their capabilities, which requires an understanding of attribution biases’ effect on human-machine coordination. Specifically, the current thesis explores how the development of trust in a partner is influenced by attribution biases and people’s assignment of blame for a negative outcome. This study can also suggest how a machine partner should be designed to react to environmental disturbances and report the appropriate level of information about external conditions. / Dissertation/Thesis / Masters Thesis Human Systems Engineering 2019
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The human-machine teams create, explain, and recover from coordination breakdowns: a simulator study of disturbance management on modern flight decksNikolic, Mark I. 29 September 2004 (has links)
No description available.
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A Cognitive Systems Engineering Approach to Developing Human Machine Interface Requirements for New TechnologiesFern, Lisa C. 11 August 2016 (has links)
No description available.
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Directive Displays: Supporting Human-machine Coordination by Dynamically Varying Representation, Information, and Interjection StrengthRayo, Michael F., Jr 03 September 2013 (has links)
No description available.
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