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Understanding the Impact of User Frustration Intensities on Task Performance Using A Novel Adaptation of the OCC Theory of Emotions

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Have you ever heard the saying "frustration is written all over your face"? Well this saying is true, but that is not the only place. Frustration is written all over your face . . . and your body. The human body has various means to communicate an emotion without utterance of a single word. These biological-based emotional cues are displayed in both human-human interaction (HHI) and human-computer interaction (HCI). The Media Equation says that people interact with computers as if they are human. This includes becoming frustrated with them when they do not produce an expected outcome. In fact, most emotions are exhibited when a user perceives the computer is not performing the action it was told to do. The emotion experienced from this type of event is called user frustration. User frustration occurs when the human perceives the computer is interfering with the completion of a task. Confusing help menus, perceived loss of control and many other factors contribute to user frustration; however, little is known about just how much frustration is caused by these irritating, yet often-experienced events. Research has shown that human body-based measures such as heart-rate, posture, skin temperature, etc. can be used to measure user frustration. An elevated heart-rate and skin temperature often denote frustration, as well as decreasing posture. This research utilizes known methods such as task interference and system delays to elicit frustration from users. Frustration is measured from human bio-signal data such as heart rate, posture, skin temperature, and respiration. Along with this, a novel adaptation of the OCC Theory of emotions is used to determine the amount or intensity of user frustration experienced by a person. Intensities computed from this OCC adaptation are used to examine their effect on task performance metrics. This research seeks to determine if the amount of frustration experienced by a user (intensities) impact user performance uniquely.

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