Interruptions can cause people to make mistakes or errors during human--computer interaction (HCI). Interruptions occur as an unavoidable side-effect of some important kinds of human computer-based activities, for example, (a) constantly monitor for unscheduled changes in information environments, (b) supervise background autonomous services, and (c) intermittently collaborate and communicate with other people. Fortunately, people have powerful innate cognitive abilities that they can potentially leverage to manage multiple concurrent activities if they have specific kinds of control and interaction support. There is great opportunity, therefore, for user-interface design to increase people's ability to successfully handle interruptions, and prevent expensive errors. The literature contains very little concrete design wisdom about how to solve the interruption problems in user interfaces (UIs). Coordination support, however, is identified as a most important design topic. This article presents the results of an empirical investigation to compare basic design solutions for coordinating human interruption in computer -based multitasks. A theory-based taxonomy of human interruption is used

At first glance it seems absurd that busy people doing important jobs should want their computers to interrupt them. Interruptions are disruptive and people need to concentrate to make good decisions. However, successful job performance also frequently depends on people's abilities to (a) constantly monitor their dynamically changing information environments, (b) collaborate and communicate with other people in the system, and (c) supervise background autonomous services. These critical abilities can require people to simultaneously query a large set of information sources, continuously monitor for important events, and respond to and communicate with other human operators. Automated monitoring

This research adopted a model of goal activation to study the mechanisms underlying interrupted task performance. The effects of interruption timing, type of interruption, and age on task time and primary task resumption time were explored under conditions in which attention was switched back and forth between two tasks, much as when drivers shift attention between attending to the road and to an in-vehicle task. The timing of interruptions had a significant impact on task resumption times, indicating that the most costly time to interrupt task performance is during the middle of a task. However, this effect was overshadowed by age-related performance decrements for older participants. Interruptions that prevented strategic rehearsal of goals resulted in longer resumption times as compared with interruptions that allowed rehearsal. Actual or potential applications of this research include the design of in-vehicle device user interfaces, the timing of invehicle messages, and current metrics for assessing driver distraction.

The extent to which memory for information content is reliable, trustworthy, and accurate is crucial in the information age. Being forced to divert attention to interrupting messages is common, however, and can cause memory loss. The memory e#ects of interrupting messages were investigated in three experiments. In Experiment 1, attending to an interrupting message decreased memory accuracy. Experiment 2, where four interrupting messages were used, replicated this result. In Experiment 3, an interrupting message was shown to be most disturbing when it was semantically very close to the main message. Drawing from a theory of long-term working memory it is argued that interrupting messages can both disrupt the active semantic elaboration of content during encoding and cause semantic interference upon retrieval. Properties of the interrupting message a#ect the extent and type of errors in remembering. Design implications are discussed.

Operators in complex event-driven domains must coordinate competing attentional demands in the form of multiple tasks and interactions. This study examined the extent to which this requirement can be supported more effectively through informative interruption cueing (in this case, partial information about the nature of pending tasks). The 48 participants performed a visually demanding air traffic control (ATC) task. They were randomly assigned to 1 of 3 experimental groups that differed in the availability of information (not available, available upon request, available automatically) about the urgency and modality of pending interruption tasks. Within-subject variables included ATC-related workload and the modality, frequency, and priority of interruption tasks. The results show that advance knowledge about the nature of pending tasks led participants to delay visual interruption tasks the longest, which allowed them to avoid intramodal interference and scanning costs associated with performing these tasks concurrently with ATC tasks. The 3 experimental groups did not differ significantly in terms of their interruption task performance; however, the group that automatically received task-related information showed better ATC performance, thus experiencing a net performance gain. Actual or potential applications of this research include the design of interfaces in support of attention and interruption management in a wide range of event-driven environments.

Evaluation is the key to effective interface design. It becomes even more important when the interfaces are for cutting edge technology, in application areas that are new and with little prior design knowledge. Knowing how to evaluate new interfaces can decrease development effort and increase the returns on resources spent on formative evaluation. The problem is that there are few, if any, readily available evaluation tools for these new interfaces. This work focuses on the creation and testing of a new set of heuristics that are tailored to the large screen information exhibit (LSIE) system class. This new set is created through a structured process that relies upon critical parameters associated with the notification systems design space. By inspecting example systems, performing claims analysis, categorizing claims, extracting design knowledge, and finally synthesizing heuristics; we have created a usable set of heuristics that is better equipped for supporting formative evaluation. Contributions of this work include: a structured heuristic creation process based on critical parameters, a new set of heuristics tailored to the LSIE system class, reusable design knowledge in the form of claims and high level design issues, and a new usability evaluation method comparison

The ongoing evolution of modern technology from reactive tool to powerful and independent agent has created problems that are related to breakdowns in humanautomation coordination. These breakdowns can be explained, in part, by the fact that machines can initiate actions on their own and without explicit operator consent but do not possess the communication skills that are required to know when and how to share information with operators concerning their intentions, actions, and limitations. One problem in particular is the extensive and increasing use of automation feedback that requires focal visual attention. Such feedback does not support task-sharing and effective attention allocation, especially in the context of unexpected changes and events. One possible solution to the problem is suggested by multiple-resource theory: the distribution of tasks and information across various sensory channels. In this manuscript, we will provide an overview of our recent series of simulator studies on the effectiveness of multimodal feedback for supporting early stages of attention management. Also, some of the many remaining challenges associated with supporting more complex coordinative functions in human-machine teams will be discussed. 2

Interruptions are often seen as distracting or sometimes devastating elements that need to be minimized or eliminated. However, interruptions are also used to increase efficiency, productivity, prevent errors, and even influence behavior. Existing theories and taxonomies of interruptions fail to account for the helpful aspects of interruptions. Therefore we propose a theoretical framework to help explain the positive aspects of interruptions. Warnings & alerts, reminders, suggestions and notifications are examples of interruptions that have beneficial outcomes by changing and influencing behavior. We propose a cognitive theory of interruptions based on the properties of the users, their tasks, and best presentations depending on the desired effectiveness of the interruption. Norman’s 7-stage action model serves to explain how and why an interruption is accepted, and potential mismatches between the goal of the interruption and the user. Potential applications of this model include better understanding the effects of interruptions, and guidance to design effective and persuasive warnings and alerts, reminders, suggestions and notifications.

This paper describes several generic models that were used to explore cognitive strategies for handling interruptions. One of these models was then modified to fit the specific real-world task of completing an aircraft checklist. This model produced detailed a priori predictions about where the interrupted checklist will be resumed. The implications of these predictions for task design and for the use of cognitive modeling as an approach to understanding interruptions are discussed

A notification represents the proactive delivery of information to a user and reduces the need to visually scan or repeatedly check an external information source. At the same time, notifications often interrupt user tasks at inopportune moments, decreasing productivity and increasing frustration. Controlled studies have shown that linking notification delivery to the perceptual structure of a user’s tasks can reduce these interruption costs. However, in these studies, the scheduling was always performed manually, and it was not clear whether it would be possible for a system to mimic similar techniques. This article contributes the design and implementation of a novel system called Oasis that aligns notification scheduling with the perceptual structure of user tasks. We describe the architecture of the system, how it detects task structure on the fly without explicit knowledge of the task itself, and how it layers flexible notification scheduling policies on top of this detection mechanism. The system also includes an offline tool for creating customized statistical models for detecting task structure. The value of our system is that it intelligently schedules notifications, enabling the reductions in interruption costs shown within prior controlled studies to now be realized by users in everyday desktop computing tasks. It also provides a test bed for experimenting with how notification management policies and other system functionalities can be linked to task