- This event has passed.
Ph.D. Proposal: Md Abdullah Al Fahim
February 23, 2022 @ 1:30 pm - 2:30 pm EST
Title: The Effect of Experienced Emotions on User’s Trust in Automation
Ph.D. Candidate: Md Abdullah Al Fahim
Major Advisor: Dr. Mohammad Maifi Hasan Khan
Associate Advisors: Dr. Ross Buck, Dr. Derek Aguiar, Dr. Dong-Guk Shin
Review Committee Members: Dr. Walter Krawec, Dr. Yusuf Albayram
Date/Time: Wednesday, February 23rd, 2022, 1:30P.M. – 2:30P.M.
Location: WebEx Online
Meeting link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m6f6f625465c249b1c68ca39fa5a325b5
Meeting number: 2623 853 2283
Password: Qpv3qQ6rQj2
Join by phone: +1-415-655-0002 US Toll
Access code: 2623 853 2283
Abstract:
Technological advances are increasingly pushing humans to team up with and rely on automation to accomplish various complex tasks more efficiently. In such human-automation interaction scenarios, trust in automation is noted to be one of the most important factors that governs users’ dependence on such systems. Therefore, ensuring an appropriate level of trust and usage by avoiding misuse (i.e., overtrust) and disuse (i.e., undertrust) is of extreme importance. Without an appropriate level of trust, human-automation collaborations are likely to fail and lead to suboptimal performance.
Recent work in the human-automation trust literature acknowledges the influence of emotions on users’ trust in automation among other factors. However, what emotions are related to human-automation interaction, how these emotions are elicited during the interaction, and how these experienced emotions affect users’ trust in the automation and subsequent usage behavior is far from clear. Guided by prior work in human-human trust, human-automation trust, and emotions, this dissertation aims to investigate the relationship between emotion and human-automation trust interaction.
In the first part of the thesis, we investigate how different factors such as reliability, risk, and anthropomorphism influence users’ experienced emotions. We also identify the emotions that are relevant to human-automation interaction and establish the mediating effect of these emotions on system attributes and different trustworthiness perception relationships.
Next, based on the findings, we investigate whether we can influence users’ experienced emotions during interaction with automation by providing affective feedback after critical system events (e.g., errors, warnings). Our results confirmed the success of affective feedback at influencing users’ experienced emotions and subsequently influencing users’ trust in the automation.
Finally, to mitigate the potential negative effect of emotions on users’ usage of automation, we proposed to investigate the effect of raising emotional awareness on users’ emotions and trust. The insights from this dissertation can be utilized to develop user-aware intelligent systems, induce and sustain an appropriate emotional state through user interfaces, and facilitate an appropriate level of trust.
