Robert S. Gutzwiller

Discrete task switching in overload

We describe a computational multi-attribute decision model that predicts the decision aspect of sequential multitasking. We investigate how people choose to switch tasks or continue performing an ongoing task when they are in overload conditions where concurrent performance of tasks is impossible. The model is based on a meta-analytic integration of 31 experiments from the literature on applied task switching. Consistent trends from the meta-analysis, to avoid switching, and to switch to tasks lower difficulty, along with greater salience, priority and interest are used to set polarity parameters in the mathematical model. A decision model of multi-task attention switching is presented.The model posits that task switching is based on attractiveness attributes of alternative tasks, related to salience, difficulty, interest and priority.A fundamental bias against task switching is included.The weights and polarity of these attributes are based on two meta-analyses of applied task switching data, which are presented.

The Human Factors of Cyber Network Defense

Technology’s role in the fight against malicious cyber-attacks is critical to the increasingly networked world of today. Yet, technology does not exist in isolation: the human factor is an aspect of cyber-defense operations with increasingly recognized importance. Thus, the human factors community has a unique responsibility to help create and validate cyber defense systems according to basic principles and design philosophy. Concurrently, the collective science must advance. These goals are not mutually exclusive pursuits: therefore, toward both these ends, this research provides cyber-cognitive links between cyber defense challenges and major human factors and ergonomics (HFE) research areas that offer solutions and instructive paths forward. In each area, there exist cyber research opportunities and realms of core HFE science for exploration. We raise the cyber defense domain up to the HFE community at-large as a sprawling area for scientific discovery and contribution.

Workload overload modeling: An experiment with MATB II to inform a computational model of task management

Task switching choice was examined building from a model of task overload management. An experiment using the Multi-Attribute Task Battery (MATB) was undertaken to explore the influence of two parameters of the model, task priority and task difficulty. Participants were free to switch between the four component tasks, with the number of switches and task choice for conflicting events observed. A unique post-experiment survey measured subjective ratings of task attributes. We found that task difficulty, by reducing switching, and task priority, which determined whether increased task difficulty increased time in task, significantly influenced task switching predominantly in line with our predictions. The specific role of priority in multi-task management, and future directions including time-on-task related effects and the role of operator fatigue, are discussed.

The Role of Working Memory in Levels of Situation Awareness

Situation awareness (SA) brings together theories in cognition to examine what an operator perceives, understands and predicts about their environment. Previous characterization of working memory (WM) influence in levels of awareness however is sparse and has several shortcomings, including how both WM and SA have been measured. In the current experiment, a factor analytic approach to WM was adopted based on performance on three different WM tasks. These factor scores were then related to SA which was measured over two forms of scenarios in a complex dynamic decision-making task. In one scenario, Level 1 SA was assessed, and the other assessed Level 3 processes implicitly. Findings from 99 participants indicate WM was unrelated to Level 1, but was related to Level 3 SA with the relationship strengthening with increasing task experience. These results refine and contribute to ongoing theory in the context of SA and dynamic task performance, and provide future directives to individual differences research in human factors.

Time Sharing Between Robotics and Process Control Validating a Model of Attention Switching

Objective: The aim of this study was to validate the strategic task overload management (STOM) model that predicts task switching when concurrence is impossible. Background: The STOM model predicts that in overload, tasks will be switched to, to the extent that they are attractive on task attributes of high priority, interest, and salience and low difficulty. But more-difficult tasks are less likely to be switched away from once they are being performed. Method: In Experiment 1, participants performed four tasks of the Multi-Attribute Task Battery and provided task-switching data to inform the role of difficulty and priority. In Experiment 2, participants concurrently performed an environmental control task and a robotic arm simulation. Workload was varied by automation of arm movement and both the phases of environmental control and existence of decision support for fault management. Attention to the two tasks was measured using a head tracker. Results: Experiment 1 revealed the lack of influence of task priority and confirmed the differing roles of task difficulty. In Experiment 2, the percentage attention allocation across the eight conditions was predicted by the STOM model when participants rated the four attributes. Model predictions were compared against empirical data and accounted for over 95% of variance in task allocation. More-difficult tasks were performed longer than easier tasks. Task priority does not influence allocation. Conclusions: The multiattribute decision model provided a good fit to the data. Applications: The STOM model is useful for predicting cognitive tunneling given that human-in-the-loop simulation is time-consuming and expensive.

A task analysis toward characterizing cyber-cognitive situation awareness (CCSA) in cyber defense analysts

Cyberspace is an increasingly crucial part of everyday living. We have long recognized that defending this space is complex, requiring information integration, and decisions of man and machine to coalesce in a dynamic environment full of shifting priorities. These properties suggest that, as in other domains with similar characteristics, situation awareness (SA) of a human cyber defender is paramount to the quality of decision outcomes in cyber defense. The majority of existing research in cyber situation awareness, centers on information systems and computers, which piece together disparate data. Fused data from multiple sources, for example, is necessary for cyberspace visualization efforts. The judgment for successful cyber SA from this perspective is different from one that is human-centered. In comparison, we rarely assess human cognitive awareness in cyberspace. In part, this reflects a need, based on prior theory, to first define critical elements of information that the human must perceive, work to elucidate how humans combine these elements to comprehend the state of the network, and how together, this awareness helps analysts predict the future state of the network. In other words, although data fusion can provide value by reducing the cognitive load created to piece together disparate sources of information, human awareness of the network (cyber-cognitive situation awareness - CCSA) is perhaps the ultimate intermediary for defense performance. Toward such an understanding, we discuss the results of a cognitive task analysis (CTA) which sought to determine the goals and abstracted elements of awareness that cyber analysts seek in network defense. We present the foundation for a series of planned experiments that establishes CCSA measurement, and baselines the efforts of cyber defenders. Once assessed, we can then begin to consider the help offered by fusion systems, automation of defensive capabilities, and cyber visualizations in a methodologically rigorous manner that has been lacking.

Human-Computer Collaboration in Adaptive Supervisory Control and Function Allocation of Autonomous System Teams

The foundation for a collaborative, man-machine system for adaptive performance of tasks in a multiple, heterogeneous unmanned system teaming environment is discussed. An autonomics system is proposed to monitor missions and overall system attributes, including those of the operator, autonomy, states of the world, and the mission. These variables are compared within a model of the global system, and strategies that re-allocate tasks can be executed based on a mission-health perspective (such as relieving an overloaded user by taking over incoming tasks). Operators still have control over the allocation via a task manager, which also provides a function allocation interface, and accomplishes an initial attempt at transparency. We plan to learn about configurations of function allocation from human-in-the-loop experiments, using machine learning and operator feedback. Integrating autonomics, machine learning, and operator feedback is expected to improve collaboration, transparency, and human-machine performance.

Addressing Human Factors Gaps in Cyber Defense

Cyber security is a high-ranking national priority that is only likely to grow as we become more dependent on cyber systems. From a research perspective, currently available work often focuses solely on technological aspects of cyber, acknowledging the human in passing, if at all. In recent years, the Human Factors community has begun to address human-centered issues in cyber operations, but in comparison to technological communities, we have only begun to scratch the surface. Even with publications on cyber human factors gaining momentum, there still exists a major gap in the field between understanding of the domain and currently available research meant to address relevant issues. The purpose for this panel is to continue to expand the role of human factors in cyber research by introducing the community to current work being done, and to facilitate collaborations to drive future research. We have assembled a panel of scientists across multiple specializations in the human factors community to have an open discussion regarding how to leverage previous human factors research and current work in cyber operations to continue to push the bounds of the field.

Command and control of teams of autonomous systems

The command and control of teams of autonomous vehicles provides a strong model of the control of cyber-physical systems in general. Using the definition of command and control for military systems, we can recognize the requirements for the operational control of many systems and see some of the problems that must be resolved. Among these problems are the need to distinguish between aberrant behaviors and optimal but quirky behaviors so that the human commander can determine if the behaviors conform to standards and align with mission goals. Similarly the commander must able to recognize when goals will not be met in order to reapportion assets available to the system. Robustness in the face of a highly variable environment can be met through machine learning, but must be done in a way that the tactics employed are recognizable as correct. Finally, because cyber-physical systems will involve decisions that must be made at great speed, we consider the use of the Rainbow framework for autonomics to provide rapid but robust command and control at pace.

The Status of the Strategic Task Overload Model (STOM) for Predicting Multi-Task Management

A model for task switching which focuses on the decision making of operators in overloaded multitask conditions is reviewed and new research presented. The STOM model is an ongoing effort and as such, work is now accumulating, which serves to validate the model as a useful predictive method, but also is uncovering uncertainties that require further investigation. Here we summarize the origins of the model, which was informed by past modeling efforts, a literature review and a meta-analysis. We then describe in detail the basic parameters of STOM and the current status of each, before discussing future directions and six uncertainties uncovered when building our understanding of task switching choice.