A Survey of Attention Management Systems in Ubiquitous Computing Environments

Author:

Anderson Christoph1,Hübener Isabel1,Seipp Ann-Kathrin1,Ohly Sandra2,David Klaus1,Pejovic Veljko3

Affiliation:

1. Chair for Communication Technology, University of Kassel, Wilhelmshöher Allee, Kassel, Germany

2. Chair of Business Psychology, University of Kassel, Kassel, Germany

3. University of Ljubljana, Ljubljana, Slovenia

Abstract

Today's information and communication devices provide always-on connectivity, instant access to an endless repository of information, and represent the most direct point of contact to almost any person in the world. Despite these advantages, devices such as smartphones or personal computers lead to the phenomenon of attention fragmentation, continuously interrupting individuals' activities and tasks with notifications. Attention management systems aim to provide active support in such scenarios, managing interruptions, for example, by postponing notifications to opportune moments for information delivery. In this article, we review attention management system research with a particular focus on ubiquitous computing environments. We first examine cognitive theories of attention and extract guidelines for practical attention management systems. Mathematical models of human attention are at the core of these systems, and in this article, we review sensing and machine learning techniques that make such models possible. We then discuss design challenges towards the implementation of such systems, and finally, we investigate future directions in this area, paving the way for new approaches and systems supporting users in their attention management.

Funder

Ambient Notification Environments

LOEWE Program of Excellence in Research, Hessen, Germany

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Reference122 articles.

1. If not now, when?

2. The many faces of information technology interruptions: a taxonomy and preliminary investigation of their performance effects

3. Memory for goals: An activation-based model;Altmann Erik M.;Cognitive Science,2002

4. Erik M. Altmann and J. Gregory Trafton. 2007. Timecourse of recovery from task interruption: Data and a model. Psychonomic Bulletin 8 Review 14 6 (2007) 1079--1084. Erik M. Altmann and J. Gregory Trafton. 2007. Timecourse of recovery from task interruption: Data and a model. Psychonomic Bulletin 8 Review 14 6 (2007) 1079--1084.

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