A model of human event detection in multiple process monitoring situations

It is proposed that human decision making in many multi-task situations might be modeled in terms of the manner in which the human detects events related to his tasks and the manner in which he allocates his attention among his tasks once he feels events have occurred. A model of human event detection performance in such a situation is presented. An assumption of the model is that, in attempting to detect events, the human generates the probability that events have occurred. Discriminant analysis is used to model the human's generation of these probabilities. An experimental study of human event detection performance in a multiple process monitoring situation is described and the application of the event detection model to this situation is addressed. The experimental study employed a situation in which subjects simulataneously monitored several dynamic processes for the occurrence of events and made yes/no decisions on the presence of events in each process. Input to the event detection model of the information displayed to the experimental subjects allows comparison of the model's performance with the performance of the subjects

A model for dynamic allocation of human attention among multiple tasks

The problem of multi-task attention allocation with special reference to aircraft piloting is discussed with the experimental paradigm used to characterize this situation and the experimental results obtained in the first phase of the research. A qualitative description of an approach to mathematical modeling, and some results obtained with it are also presented to indicate what aspects of the model are most promising. Two appendices are given which (1) discuss the model in relation to graph theory and optimization and (2) specify the optimization algorithm of the model

Dynamic task allocation for a man-machine symbiotic system

This report presents a methodological approach to the dynamic allocation of tasks in a man-machine symbiotic system in the context of dexterous manipulation and teleoperation. This report addresses a symbiotic system containing two symbiotic partners which work toward controlling a single manipulator arm for the execution of a series of sequential manipulation tasks. It is proposed that an automated task allocator use knowledge about the constraints/criteria of the problem, the available resources, the tasks to be performed, and the environment to dynamically allocate task recommendations for the man and the machine. The presentation of the methodology includes discussions concerning the interaction of the knowledge areas, the flow of control, the necessary communication links, and the replanning of the task allocation. Examples of task allocation are presented to illustrate the results of this methodolgy

Functional relationships among monitoring performance: Subjective report of thought process and compromising states of awareness

A biocybernetic system for use in adaptive automation was evaluated using EEG indices based on the beta, alpha, and theta bandwidths. Subjects performed a compensatory tracking task while their EEG was recorded and one of three engagement indices was derived: beta/(alpha + theta), beta/alpha, or 1/alpha. The task was switched between manual and automatic modes as a function of the subjects' level of engagement and whether they were under a positive or negative feedback condition. It was hypothesized that negative feedback would produce more switches between manual and automatic modes, and that the beta/(alpha + theta) index would produce the strongest effect. The results confirmed these hypotheses. There were no systematic changes in these effects over three 16-minute trials. Tracking performance was found to be better under negative feedback. An analysis of the different EEG bands under positive and negative feedback in manual and automatic modes found more beta power in the positive feedback/manual condition and less in the positive feedback/automatic condition. The opposite effect was observed for alpha and theta power. The implications of biocybernetic systems for adaptive automation are discussed

Assessing Artificial Agent Response Time Effects on Human-Agent Teams in Variable Inter-Arrival Time Environments

Autonomous systems have gained an expanded presence within the Department of Defense (DoD). Furthermore, the DoD has clearly stated autonomous systems must extend the capabilities of their human operators. Thus, the exploration of strategies for effective pairing of humans and automation supports this vision. Previous research demonstrated that the time at which an automated agent assumes a task for its human teammate, or agent response time (ART), affects human-agent team performance, human engagement, and human workload. However, in this research environment, the time between subsequent tasks appearing to the human-agent team, or inter-arrival time (IAT), remained constant. Variable IAT environments more accurately reflect real-world operational environments. Previous research also maintained ART at a fixed level. Additionally, the effect of human understanding of automated teammate actions on human-agent team performance remains unknown. This thesis attempts to analyze the effect of an agent with adaptive ART that varies based on current IAT on human-agent team performance, human engagement, and human workload. Additionally, it seeks to determine the implication of agent predictability to the human. This thesis explores these issues in three phases. First, a method and development of a variable ART function for use in future phases is presented. Second, a study of a variable ART teammate against a fixed ART teammate highlights the significance of providing detailed agent instruction to the human. Third, analysis of instruction and type of agent teammate across an entire input IAT function and at different IAT levels is conducted. This work establishes key factors for adaptive ART function implementation. Based on specific IAT changes, the current research demonstrates that adaptive ART can boost human-agent team performance and manipulate human engagement. Furthermore, predictability of agent action in variable IAT environments is a desired system attribute

A Function-to-Task Process Model for Adaptive Automation System Design

Adaptive automation systems allow the user to complete a task seamlessly with a computer performing tasks at which the human operator struggles. Unlike traditional systems that allocate functions to either the human or the machine, adaptive automation varies the allocation of functions during system operation. Creating these systems requires designers to consider issues not present during static system development. To assist in adaptive automation system design, this paper presents the concept of inherent tasks and takes advantage of this concept to create the function-to-task design process model. This process model helps the designer determine how to allocate functions to the human, machine, or dynamically between the two. An illustration of the process demonstrates the potential complexity within adaptive automation systems and how the process model aids in understanding this complexity during early stage design

Comparing Types Of Adaptive Automation Within A Multi-tasking Environment

Throughout the many years of research examining the various effects of automation on operator performance, stress, workload, etc., the focus has traditionally been on the level of automation, and the invocation methods used to alter it. The goal of the current study is to instead examine the utilization of various types of automation with the goal of better meeting the operator’s cognitive needs, thus improving their performance, workload, and stress. The task, control of a simulated unmanned robotic system, is designed to specifically stress the operator’s visual perception capabilities to a greater degree. Two types of automation are implemented to support the operator’s performance of the task: an auditory beep aid intended to support visual perception resources, and a driving aid automating control of the vehicle’s navigation, offloading physical action execution resources. Therefore, a comparison can be made between types of automation intended to specifically support the mental dimension that is under the greatest demand (the auditory beep) against those that do not (the driving automation). An additional evaluation is made to determine the benefit of adaptively adjusting the level of each type of automation based on the current level of task demand, as well as the influence of individual differences in personality. Results indicate that the use of the auditory beep aid does improve performance, but also increases Temporal Demand and Effort. Use of driving automation appears to disengage the operator from the task, eliciting a vigilance response. Adaptively altering the level of automation to meet task demands has a mixed effect on performance and workload (reducing both) when the auditory beep automation is used. However, adaptive driving automation is clearly detrimental, iv causing an increase in workload while decreasing performance. Higher levels of Neuroticism are related to poorer threat detection performance, but personality differences show no indication of moderating the effects of either of the experimental manipulations. The results of this study show that the type of automation implemented within an environment has a considerable impact on the operator, in terms of performance as well as cognitive/emotional stat

Development of a systems theoretical procedure for evaluation of the work organization of the cockpit crew of a civil transport airplane

To achieve optimum design for the man machine interface with aircraft, a description of the interaction and work organization of the cockpit crew is needed. The development of system procedure to evaluate the work organization of pilots while structuring the work process is examined. Statistical data are needed to simulate sequences of pilot actions on the computer. Investigations of computer simulation and applicability for evaluation of crew concepts are discussed

A Psychophysiological Assessment of the Efficacy of Event-Related Potentials and Electroencephalogram for Adaptive Task Allocation

The present study was designed to test the efficacy of using Electroencephalogram (EEG) and Event-Related Potentials (ERPs) for making task allocations decisions. Thirty-six participants were randomly assigned to an experimental, yoked, or control group condition. Under the experimental condition, a compensatory tracking task was switched between manual and automatic task modes based upon the participant\u27s EEG. ERPs were also gathered to an auditory, oddball task. Participants in the yoked condition performed the same tasks under the exact sequence of task allocations that participants in the experimental group experienced. The control condition consisted of a random sequence of task allocations that was representative of each participant in the experimental group condition. Therefore, the design allowed a test of whether the performance and workload benefits seen in previous studies using this biocybernetic system were due to adaptive aiding or merely to the increase in task mode allocations. The results showed that the use of adaptive aiding improved performance and lowered subjective workload under negative feedback as predicted. Additionally, participants in the adaptive group had significantly lower tracking errors scores and NASA-TLX ratings than participants in either the yoked or control group conditions. Furthermore, the amplitudes of the N1 and P3 ERP components were significantly larger under the experimental group condition than under either the yoked or control group conditions. These results are discussed in terms of their implications for adaptive automation design

Understanding Effects of Autonomous Agent Timing on Human-Agent Teams Using Iterative Modeling, Simulation and Human-in-the-Loop Experimentation

Recent U.S. Air Force Research Laboratory strategy documents have suggested the need for research in human-agent teaming. Teaming supports a dynamic shift in roles between the human and the agent, depending upon human performance and mission needs. Further, because the performance of these agents will be highly dependent upon the state of the human and the mission, this strategy suggests the need for increased use of modeling to provide a broader understanding of the automated agent’s behavior. This thesis applies a combination of static modeling in SysML activity diagrams, dynamic modeling of human and agent behavior in IMPRINT, and human experimentation in a dynamic, event-driven environment. The dynamic models and human experiments are used to understand the effects of agent delay time on human behavior, performance, and workload, as well as team dynamics. The models and experiments illustrate that agent delay time has a significant effect upon team behavior, performance, and the roles assumed by the human and agent. Therefore, it is proposed that the consequences of agent timing are significant in the context of human agent teaming and that models, which incorporate the human and agent within a common modeling environment, can be useful in understanding this effect