A person seeking someone else's attention is normally able to quickly assess how interruptible they are. This assessment allows for behavior we perceive as natural, socially appropriate, or simply polite. On the other hand, today's computer systems are almost entirely oblivious to the human world they operate in, and typically have no way to take into account the interruptibility of the user. This paper presents a Wizard of Oz study exploring whether, and how, robust sensor-based predictions of interruptibility might be constructed, which sensors might be most useful to such predictions, and how simple such sensors might be. The study simulates a range of possible sensors through human coding of audio and video recordings. Experience sampling is used to simultaneously collect randomly distributed self-reports of interruptibility. Based on these simulated sensors, we construct statistical models predicting human interruptibility and compare their predictions with the collected self-report data. The results of these models, although covering a demographically limited sample, are very promising, with the overall accuracy of several models reaching about 78%. Additionally, a model tuned to avoiding unwanted interruptions does so for 90% of its predictions, while retaining 75% overall accuracy.

Introduction One of the main results of Twentieth-century Cognitive Psychology is that, despite the overall impressive abilities of people to sense, remember, and reason about the world, our cognitive abilities are extremely limited in well-characterized ways. In particular, psychologists have found that people grapple with scarce attentional resources and limited working memory. Such limitations become salient when people are challenged with remembering more than a handful of new ideas or items in the short term [20,28], recognizing important targets against a background pattern of items [5,26], or interleaving multiple tasks [6,26]. These results indicate that we cannot help but to inspect the world via a limited spotlight of attention. As such, we often generate clues implicitly and explicitly about what we are selectively attending to and how deeply we are focusing. Given constraints on attentional resources, it is no surprise that communication among people relies deeply on atte

Goal-directed cognition is often discussed in terms of specialized memory structures like the "goal stack." The goal-activation model presented here analyzes goal-directed cognition in terms of the general memory constructs of activation and associative priming. The model embodies three predictive constraints: (1) the interference level, which arises from residual memory for old goals; (1) the strengthening constraint, which makes predictions about time to encode a new goal; and (3) the priming constraint, which makes predictions about the role of cues in retrieving pending goals. These constraints are formulated algebraically and tested through simulation of latency and error data from the Tower of Hanoi, a means-ends puzzle that depends heavily on suspension and resumption of goals. Implications of the model for understanding intention superiority, postcompletion error, and effects of task interruption are discussed.

We present methods for inferring the cost of interrupting users based on multiple streams of events including information generated by interactions with computing devices, visual and acoustical analyses, and data drawn from online calendars. Following a review of prior work on techniques for deliberating about the cost of interruption associated with notifications, we introduce methods for learning models from data that can be used to compute the expected cost of interruption for a user. We describe the Interruption Workbench, a set of event-capture and modeling tools. Finally, we review experiments that characterize the accuracy of the models for predicting interruption cost and discuss research directions.

: We describe a study on the influence of instant messaging (IM) on ongoing computing tasks. The study both replicates and extends earlier work on the cost of sending notifications at different times and the sensitivity of different tasks to interruption. We investigate alternative hypotheses about the nature of disruption for a list evaluation task, an activity identified as being particularly costly to interrupt. Our findings once again show the generally disruptive effects of IM, especially during fast, stimulus-driven search tasks. In addition, we show that interruptions coming early during a search task are more likely to result in the user forgetting the primary task goal than interruptions that arrive later on. These findings have implications for the design of user interfaces and notification policies that minimize the disruptiveness of notifications. Keywords: Notifications, user study, interruptions, information overload, divided attention 1 Introduction With the adv...

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

We describe research on the effects of instant messaging (IM) on ongoing computing tasks. We present a study that builds on earlier work exploring the influence of sending notifications at different times and the kinds of tasks that are particularly susceptible to interruption. This work investigates alternative hypotheses about the nature of disruption for a list evaluation task, an activity we had identified as being particularly costly to interrupt. Our findings replicate earlier work, showing the generally harmful effects of IM, and further show that notifications are more disruptive for fast, stimulus-driven search tasks than for slower, more effortful semantic-based search tasks. 1. Introduction Instant messaging systems, including America Online's Instant Messenger, Microsoft Network's Messenger, and Yahoo!'s Messenger service claim over 70 million users and research shows that the number of users of these services is growing rapidly (MediaMetrix, December, 1999). The benefits...

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

Interest has been growing in opportunities to build and deploy statistical models that can infer a computer user’s current interruptability from computer activity and relevant contextual information. We describe a system that intermittently asks users to assess their perceived interruptability during a training phase and that builds decision-theoretic models with the ability to predict the cost of interrupting the user. The models are used at run-time to compute the expected cost of interruptions, providing a mediator for incoming notifications, based on a consideration of a user’s current and recent history of computer activity, meeting status, location, time of day, and whether a conversation is detected.

Designing and evaluating notification systems represents an emerging challenge in the study of human--computer interaction. Users rely on notification systems to present potentially interruptive information in an efficient and effective manner to enable appropriate reaction and comprehension. Little is known about the effects of these systems on ongoing computer tasks. As the research community strives to understand information design suitable for opposing usage goals, few existing efforts lend themselves to extensibility.