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.

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.

Human perceptual and cognitive abilities are limited resources. Attention is the mechanism used to allocate such resources in the most effective way. Current technologies, in addition to allowing fast access to information and people, should be designed to support human attentional processes on which they impose further strain. This paper analyses the issues related to the design of systems capable of such support: Attention Aware Systems. We introduce the research aimed at understanding and modelling human attentional processes, including perceptual and cognitive processes as studied in cognitive psychology, as well as rhetorical, aesthetic, and social aspects related to attentional mechanisms. We analyse current approaches to the design of Attention Aware Systems along three major features: detection of user's current attentional state, detection and evaluation of possible alternative attentional states, strategies for focus switch or maintenance. Finally, we discuss the most promising research direction for the development of systems capable of supporting human attentional mechanisms.

Abstract not found

For both practitioners and researchers, span of control plays an important role in defining and understanding the role of the CEO. In this paper, we combine organizational chart information for a sample of 65 companies with detailed data on how their CEOs allocate their work time, which we define as their span of activity. Span of activity provides a direct measure of the CEO’s management style, including the attention devoted to specific subordinates and functions, the time devoted to individual work and outside constituencies, a preference for multilateral or bilateral interaction, the degree of planning, etc. We find that CEOs with a larger number of reports spend more time with subordinates, more time on large meetings, less time on unplanned activities. The presence of a delegate, such as the COO, allows the CEO to reduce the time spent with insiders and to focus on bilateral and unplanned activities. These results suggest that time-use information is helpful in interpreting how span