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

User attention is a scarce resource, and users are susceptible to interruption overload. Systems do not reason about the effects of interrupting a user during a task sequence. In this study, we measure effects of interrupting a user at different moments within task execution in terms of task performance, emotional state, and social attribution. Task models were developed using event perception techniques, and the resulting models were used to identify interruption timings based on a user’s predicted cognitive load. Our results show that different interruption moments have different impacts on user emotional state and positive social attribution, and suggest that a system could enable a user to maintain a high level of awareness while mitigating the disruptive effects of interruption. We discuss implications of these results for the design of an attention manager. Categories & Subject Descriptors H.5.2 [Information Interfaces and Presentation]: User Interfaces — evaluation/methodology, user-centered

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.

The computer and communication systems that office workers currently use tend to interrupt at inappropriate times or unduly demand attention because they have no way to determine when an interruption is appropriate. Sensor-based statistical models of human interruptibility offer a potential solution to this problem. Prior work to examine such models has primarily reported results related to social engagement, but it seems that task engagement is also important. Using an approach developed in our prior work on sensor-based statistical models of human interruptibility, we examine task engagement by studying programmers working on a realistic programming task. After examining many potential sensors, we implement a system to log low-level input events in a development environment. We then automatically extract features from these low-level event logs and build a statistical model of interruptibility. By correctly identifying situations in which programmers are non-interruptible and minimizing cases where the model incorrectly estimates that a programmer is non-interruptible, we can support a reduction in costly interruptions while still allowing systems to convey notifications in a timely manner. Author Keywords Situationally appropriate interaction, managing human attention, sensor-based interfaces, context-aware computing, machine learning, interruptibility.

Our studies have shown that as displays become larger, users leave more windows open for easy multitasking. A larger number of windows, however, may increase the time that users spend arranging and switching between tasks. We present Scalable Fabric, a task management system designed to address problems with the proliferation of open windows on the PC desktop. Scalable Fabric couples window management with a flexible visual representation to provide a focus-plus-context solution to desktop complexity. Users interact with windows in a central focus region of the display in a normal manner, but when a user moves a window into the periphery, it shrinks in size, getting smaller as it nears the edge of the display. The window "minimize" action is redefined to return the window to its preferred location in the periphery, allowing windows to remain visible when not in use. Windows in the periphery may be grouped together into named tasks, and task switching is accomplished with a single mouse click. The spatial arrangement of tasks leverages human spatial memory to make task switching easier. We review the evolution of Scalable Fabric over three design iterations, including discussion of results from two user studies that were performed to compare the experience with Scalable Fabric to that of the Microsoft Windows XP TaskBar.

Our studies have shown that as displays become larger, users leave more windows open for easy multitasking. A larger number of windows, however, may increase the time that users spend arranging and switching between tasks. We introduce GroupBar, a task management system for dealing with the profusion of windows on the PC desktop. Designed to offer the same basic form and function as the existing Microsoft Windows^TM TaskBar, GroupBar additionally allows users to group windows into higher-level tasks and enables task switching with a single mouse click. In order to gain experience with GroupBar usage and to develop reasonable task definitions we conducted a longitudinal field study. Based on the results of that field study, we conducted a comparative user study wherein we found that participants were able to multitask faster when using GroupBar than when using the existing Windows TaskBar.

Many context-aware services make the assumption that the context they use is completely accurate. However, in reality, both sensed and interpreted context is often ambiguous. A challenge facing the development of realistic and deployable context-aware services, therefore, is the ability to handle ambiguous context. In this paper, we describe an architecture that supports the building of context-aware services that assume context is ambiguous and allows for mediation of ambiguity by mobile users in aware environments. We illustrate the use of our architecture and evaluate it through three example context - aware services, a word predictor system, an In/Out Board, and a reminder tool.

Interrupting users engaged in tasks typically has negative effects on their task completion time, error rate, and affective state. Empirical research has shown that these negative effects can be mitigated by deferring interruptions until more opportune moments in a user’s task sequence. However, existing systems that reason about when to interrupt do not have access to task models that would allow for such finer-grained temporal reasoning. To enable this reasoning, we have developed an integrated framework for specifying and monitoring user tasks. For task specification, our framework provides a language that supports expressive specification of tasks using a concise notation. For task monitoring, our framework provides an event database and handler that manages events from any instrumented application and a task monitor that observes a user’s progress through specified tasks. We describe the design and implementation of our framework, showing how it can be used to specify and monitor practical, representative user tasks. We also report results from two user studies measuring the effectiveness of our existing implementation. The use of our framework will enable attention aware systems to consider a user’s position in a task when reasoning about when to interrupt.

This article provides a first look at an extensible philosophy for studying other instances of multitasking or collaborative performance. We argue that the models and framework presented here will improve the HCI community's ability to classify and evaluate existing and emerging notification systems, as well as to catalog information and interaction design guidelines and lessons learned in a cohesive, collective manner. In the next section, we present a more thorough overview of notification systems appearing in recent literature and itemize general user goals, providing motivation and background material for the model we present in Section 3