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The purpose of this research is to investigate how to mitigate user frustration and improve task performance in the context of human-computer interaction (HCI). Even though user frustration plays a role in many aspects of HCI and studies have looked into the consequences of frustration in various fields, the ways to mitigate frustration are less deeply examined. Once the system has the ability to understand and include user emotions as factors in HCI, the interaction between the user and the computer system could be adapted if the computers are able to modify its behavior with users in appropriate ways to further joint performance. Specifically, a preliminary study was conducted to explore the task performance, motivation, and confidence implications of changing the interaction between the human and the computer via different etiquette strategies. Participants solved a total of twenty mathematics problems under different frustration condition with feedback given in different styles of etiquette. Changing etiquette strategies in tutoring led to changes in performance, motivation, and confidence. The most effective etiquette strategies changed when users were frustrated. This work provides the foundation for the design of adaptive intelligent tutoring system based on etiquette strategies.

This study investigates the influence of time delay on user emotion, arousal, and satisfaction in humanrobot interaction (HRI). Time delay is a gap between an input from a user and the corresponding feedback response from the system, and its negative consequence on performance has been documented in various areas including psychology and HRI. On the contrary, the effects of time delay on user emotion and satisfaction have been difficult to establish due to the fluctuations in the emotional aspect of the physiological state of the user. In this study, the hypothesis of whether time delay during robot vehicle operation increases user arousal and negative emotions while lowering user satisfaction was tested. Participants were asked to remote-control a robot vehicle to navigate different mazes in a remote location. Time delay was manipulated by introducing lags into system feedback. Subjective and objective measures included emotion tracking through face recognition, and electrodermal activity (EDA). User frustration, anger, and arousal increased while user satisfaction decreased. A better understanding of how time delay influences user's emotion and how change in emotion is expressed in physiological signals would be of crucial importance to designing an affect-aware robotic systems that have the ability to appropriately respond to user emotional state.

The purpose of this research is to investigate long term effects of cognitive skill degradation through the use of automation. Even though advanced studies have looked into information automation (IA) in aviation, the amount of empirical data on the effects of these systems on the retention of cognitive skills is less deeply examined. Measurement and analysis of the effects of IA on cognitive performance is an important first step in understanding cognitive skill degradation, which should be considered during the design of these systems. The use of an automation aid is expected to result in a high level of performance degradation over time. Participants were randomly placed into three experimental groups (manual, alternating, or automation) and asked to perform flight planning calculations as an experiment task. Participants performed the task five times, once every two weeks. The manual group used the manual method throughout the experiment, the alternating group switched between the manual and automated method every trial. The automation group used the manual method for the first trial, the automated method for the three consecutive trials and then went back to using the manual method during the last trial. The automation group showed the most performance degradation and highest workload, while the alternating group presented reduced performance degradation and workload, and the manual group showed the least performance degradation and workload. This work provides the foundation for the design of guidelines and recommendations for IA systems in order to prevent cognitive skill degradation.

If computer tutors can vary the interaction style to effectively mitigate negative emotions, then ITS designers would have one more mechanism in which to design affect-aware adaptations that provide the proper responses in situations where human emotions affect the ability to learn.