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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

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.
ContributorsHsiung, Chi-Ping (M.S.) (Author) / Chiou, Erin (Thesis advisor) / Cooke, Nancy J. (Thesis advisor) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2019
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Description
Highly automated vehicles require drivers to remain aware enough to takeover

during critical events. Driver distraction is a key factor that prevents drivers from reacting

adequately, and thus there is need for an alert to help drivers regain situational awareness

and be able to act quickly and successfully should a

Highly automated vehicles require drivers to remain aware enough to takeover

during critical events. Driver distraction is a key factor that prevents drivers from reacting

adequately, and thus there is need for an alert to help drivers regain situational awareness

and be able to act quickly and successfully should a critical event arise. This study

examines two aspects of alerts that could help facilitate driver takeover: mode (auditory

and tactile) and direction (towards and away). Auditory alerts appear to be somewhat

more effective than tactile alerts, though both modes produce significantly faster reaction

times than no alert. Alerts moving towards the driver also appear to be more effective

than alerts moving away from the driver. Future research should examine how

multimodal alerts differ from single mode, and see if higher fidelity alerts influence

takeover times.
ContributorsBrogdon, Michael A (Author) / Gray, Robert (Thesis advisor) / Branaghan, Russell (Committee member) / Chiou, Erin (Committee member) / Arizona State University (Publisher)
Created2018
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Description
The current study aims to explore factors affecting trust in human-drone collaboration. A current gap exists in research surrounding civilian drone use and the role of trust in human-drone interaction and collaboration. Specifically, existing research lacks an explanation of the relationship between drone pilot experience, trust, and trust-related behaviors as

The current study aims to explore factors affecting trust in human-drone collaboration. A current gap exists in research surrounding civilian drone use and the role of trust in human-drone interaction and collaboration. Specifically, existing research lacks an explanation of the relationship between drone pilot experience, trust, and trust-related behaviors as well as other factors. Using two dimensions of trust in human-automation team—purpose and performance—the effects of experience on drone design and trust is studied to explore factors that may contribute to such a model. An online survey was conducted to examine civilian drone operators’ experience, familiarity, expertise, and trust in commercially available drones. It was predicted that factors of prior experience (familiarity, self-reported expertise) would have a significant effect on trust in drones. The choice to use or exclude the drone propellers in a search-and-identify scenario, paired with the pilots’ experience with drones, would further confirm the relevance of the trust dimensions of purpose versus performance in the human-drone relationship. If the pilot has a positive sense of purpose and benevolence with the drone, the pilot trusts the drone has a positive intent towards them and the task. If the pilot has trust in the performance of the drone, they ascertain that the drone has the skill to do the task. The researcher found no significant differences between mean trust scores across levels of familiarity, but did find some interaction between self-report expertise, familiarity, and trust. Future research should further explore more concrete measures of situational participant factors such as self-confidence and expertise to understand their role in civilian pilots’ trust in their drone.
ContributorsNiichel, Madeline Kathleen (Author) / Chiou, Erin (Thesis advisor) / Cooke, Nancy J. (Committee member) / Craig, Scotty (Committee member) / Arizona State University (Publisher)
Created2019
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Description

This chapter is not a guide to embodied thinking, but rather a critical call to action. It highlights the deep history of embodied practice within the fields of dance and somatics, and outlines the value of embodied thinking within human-computer interaction (HCI) design and, more specifically, wearable technology (WT) design.

This chapter is not a guide to embodied thinking, but rather a critical call to action. It highlights the deep history of embodied practice within the fields of dance and somatics, and outlines the value of embodied thinking within human-computer interaction (HCI) design and, more specifically, wearable technology (WT) design. What this chapter does not do is provide a guide or framework for embodied practice. As a practitioner and scholar grounded in the fields of dance and somatics, I argue that a guide to embodiment cannot be written in a book. To fully understand embodied thinking, one must act, move, and do. Terms such as embodiment and embodied thinking are often discussed and analyzed in writing; but if the purpose is to learn how to engage in embodied thinking, then the answers will not come from a text. The answers come from movement-based exploration, active trial-and-error, and improvisation practices crafted to cultivate physical attunement to one's own body. To this end, my "call to action" is for the reader to move beyond a text-based understanding of embodiment to active engagement in embodied methodologies. Only then, I argue, can one understand how to apply embodied thinking to a design process.

ContributorsRajko, Jessica (Author)
Created2018