Sensing and indicating interruptibility in office workplaces

In office workplaces, interruptions by co-workers, emails or instant messages are common. Many of these interruptions are useful as they might help resolve questions quickly and increase the productivity of the team. However, knowledge workers interrupted at inopportune moments experience longer task resumption times, lower overall performance, more negative emotions, and make more errors than if they were to be interrupted at more appropriate moments. To reduce the cost of interruptions, several approaches have been suggested, ranging from simply closing office doors to automatically measuring and indicating a knowledge worker’s interruptibility - the availability for interruptions - to co-workers. When it comes to computer-based interruptions, such as emails and instant messages, several studies have shown that they can be deferred to automatically detected breakpoints during task execution, which reduces their interruption cost. For in-person interruptions, one of the most disruptive and time-consuming types of interruptions in office workplaces, the predominant approaches are still manual strategies to physically indicate interruptibility, such as wearing headphones or using manual busy lights. However, manual approaches are cumbersome to maintain and thus are not updated regularly, which reduces their usefulness. To automate the measurement and indication of interruptibility, researchers have looked at a variety of data that can be leveraged, ranging from contextual data, such as audio and video streams, keyboard and mouse interaction data, or task characteristics all the way to biometric data, such as heart rate data or eye traces. While studies have shown promise for the use of such sensors, they were predominantly conducted on small and controlled tasks over short periods of time and mostly limited to either contextual or biometric sensors. Little is known about their accuracy and applicability for long-term usage in the field, in particular in office workplaces. In this work, we developed an approach to automatically measure interruptibility in office workplaces, using computer interaction sensors, which is one type of contextual sensors, and biometric sensors. In particular, we conducted one lab and two field studies with a total of 33 software developers. Using the collected computer interaction and biometric data, we used machine learning to train interruptibility models. Overall, the results of our studies show that we can automatically predict interruptibility with high accuracy of 75.3%, improving on a baseline majority classifier by 26.6%. An automatic measure of interruptibility can consequently be used to indicate the status to others, allowing them to make a well-informed decision on when to interrupt. While there are some automatic approaches to indicate interruptibility on a computer in the form of contact list applications, they do not help to reduce in-person interruptions. Only very few researchers combined the benefits of an automatic measurement with a physical indicator, but their effect in office workplaces over longer periods of time is unknown. In our research, we developed the FlowLight, an automatic interruptibility indicator in the form of a traffic-light like LED placed on a knowledge worker's desk. We evaluated the FlowLight in a large-scale field study with 449 participants from 12 countries. The evaluation revealed that after the introduction of the FlowLight, the number of in-person interruptions decreased by 46% (based on 36 interruption logs), the awareness on the potential harm of interruptions was elevated and participants felt more productive (based on 183 survey responses and 23 interview transcripts), and 86% remained active users even after the two-month study period ended (based on 449 online usage logs). Overall, our research shows that we can successfully reduce in-person interruption cost in office workplaces by sensing and indicating interruptibility. In addition, our research can be extended and opens up new opportunities to further support interruption management, for example, by the integration of other more accurate biometric sensors to improve the interruptibility model, or the use of the model to reduce self-interruptions

Can I Have Your Attention? Implications of the Research on Distractions and Multitasking for Reference Librarians

The media have identified the last decade as “the age of distraction.” People today find it harder to work on long, sustained tasks because distractions are eroding their attention span, fostering a culture of discontinuity. Fields as diverse as psychology, business, education, human-computer interaction, and communication studies have produced a wealth of studies on interruptions, distractions, and multitasking–research that has important implications for reference librarians. The nature of our jobs invites interruptions by the public, requires familiarity with the latest technology, stimulates curiosity about a broad range of subjects, and demands adeptness at multitasking–all factors which can atomize attention

Facilitating Reliable Autonomy with Human-Robot Interaction

Autonomous robots are increasingly deployed to complex environments in which we cannot predict all possible failure cases a priori. Robustness to failures can be provided by humans enacting the roles of: (1) developers who can iteratively incorporate robustness into the robot system, (2) collocated bystanders who can be approached for aid, and (3) remote teleoperators who can be contacted for guidance. However, assisting the robot in any of these roles can place demands on the time or effort of the human. This dissertation develops modules to reduce the frequency and duration of failure interventions in order to increase the reliability of autonomous robots, while also reducing the demand on humans. In pursuit of that goal, the dissertation makes the following contributions: (1) A development paradigm for autonomous robots that separates task specification from error recovery. The paradigm reduces burden on developers while making the robot robust to failures. (2) A model for gauging the interruptibility of collocated humans. A human-subjects study shows that using the model can reduce the time expended by the robot during failure recovery. (3) A human-subjects experiment on the effects of decision support provided to remote operators during failures. The results show that humans need both diagnosis and action recommendations as decision support during an intervention. (4) An evaluation of model features and unstructured Machine Learning (ML) techniques in pursuit of learning robust suggestions models from intervention data, in order to reduce developer effort. The results indicate that careful crafting of features can lead to improved performance, but that without such feature selection, current ML algorithms lack robustness in addressing a domain where the robot's observations are heavily influenced by the user's actions.Ph.D

Representation and analysis of real-time control structures

Thesis. 1978. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.Bibliography: p. 110-111.by Rowland Frank Archer, Jr.M.S

Resumption lag at interruptible timing might not be short in actual environment

Experiments performed in controlled environments have revealed that interruptions occurring at application switching (AS), which correspond to task breakpoints in the computer field, are more subjectively acceptable and require shorter resumption lags (RL), which indicate the cognitive cost. Therefore, in this study, we investigate RLs in uncontrolled office environment. The interruptions at more acceptable ASs were expected to show shorter RLs, because the cognitive costs of the interruption are less

Description of the Integrated Driver Model

DTFH61-89-C-00044A simulation model for predicting driver behavior and system performance when the automobile driver performs concurrent steering and auxiliary in-vehicle tasks is described. This model is an integration of two previously existing computerized models referred to as the "procedural model" and the driver/vehicle model". The procedural component deals primarily with in-vehicle tasks and with the task-selection and attention-allocation procedures, whereas the driver/vehicle component predicts closed-loop continuous control (steering) behavior. Given descriptions of the driving environment and of driver information-processing limitations, the resulting integrated model allows one to predict a variety of performance measures for typical scenarios. These measures include time histories for vehicle state variables such as lane position and steering wheel deflection as well as allocation of visual and cognitive attention. Model calibration and validation are discussed, and use of the model in analyzing complex task situations and in generating human factors guidelines is demonstrated